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profit:demo/post-pr11-polish-2026-04-28
profit:infra/replace-gpt-oss-2026-04-28
profit:scrum/auto-apply-19814
profit:scrum/iter5-prep-2026-04-24
profit:test/enrich-prd-pipeline
profit:cohesion/python-inventory-and-integration
profit:phase/45-slice-4
profit:phase/45-slice-3
profit:ops/auditor-systemd-units
profit:fix/upsert-outcome-update-merge
profit:auditor/scaffold
profit:fix/upsert-outcome-serde
profit:demo-latest
profit:distillation-v1.0.0
profit:v0.26-memory-complete
...
pull from: profit:scrum/auto-apply-19814
Branches Tags
profit:demo/post-pr11-polish-2026-04-28
profit:main
profit:infra/replace-gpt-oss-2026-04-28
profit:scrum/auto-apply-19814
profit:scrum/iter5-prep-2026-04-24
profit:test/enrich-prd-pipeline
profit:cohesion/python-inventory-and-integration
profit:phase/45-slice-4
profit:phase/45-slice-3
profit:ops/auditor-systemd-units
profit:fix/upsert-outcome-update-merge
profit:auditor/scaffold
profit:fix/upsert-outcome-serde
profit:demo-latest
profit:distillation-v1.0.0
profit:v0.26-memory-complete

24 Commits
main ... scrum/auto

Author SHA1 Message Date
root
dcf4c9a8e7 demo: search.html — Live Market explainer rewrite + fp-bar viewport-paint + compact contract cards 
Four UI changes landing together since they all polish Section ① and
Section ② of the public demo:

1. Section ① (Live Market — Chicago) explainer rewritten data-source-
   first ("Live from City of Chicago Open Data...") with bolded dial
   names so a skimmer can map the visual to the prose. Drops the
   "internal calendar" jargon and the slightly-overclaiming "rest of
   the page is reacting" framing — downstream sections read the same
   feed but don't react to the per-shift filter, so the new copy says
   "this row is its heartbeat" instead.

2. Fill-probability bar gets a left-to-right paint reveal (clip-path
   inset animation) so the green→gold→orange→red gradient reads as a
   *timeline growing* instead of a static heatmap with a "danger zone"
   at the right. Followed by a 30%-wide shimmer sweep on a 3.4s loop
   for live-signal feel.

3. Paint trigger moved from on-render to IntersectionObserver — by
   the time the user scrolls to Section ② the on-render animation had
   already finished. Now each bar paints in over 2.8s when it enters
   viewport (threshold 0.2, 350ms entry delay). Single shared observer,
   unobserve()s after firing so the watch list trends to zero.

4. Contract cards now compact-by-default with click-to-expand. New
   summary strip shows revenue / margin / fill-by-1wk / top candidate
   so scanners get the punchline without expanding. Click anywhere on
   the card surface (excluding inner content) to expand the full FP
   curve, economics grid, candidates list, and Project Index. Project
   Index auto-opens with the parent card so users actually find the
   build signals — but only on user-driven expand (avoiding 20× OSHA
   scrapes on page load). grid-template-rows: 0fr → 1fr animation
   handles the smooth height transition.

All four animations honor prefers-reduced-motion.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-28 05:56:48 -05:00
root
3c6d2c5f74 demo: search.html UX polish — skeleton loader, card-in stagger, hero takeover, B&W faces 
Search results no longer pop in as a single block. New behavior:

- Skeleton list pre-claims the vertical space results will occupy
  with shimmering placeholder cards, so arriving results fade in
  over the skeleton instead of pushing layout. Sweep is staggered
  per row for a "rolling wave" not "everything blinking together".
- Domain-language stage caption ("matching against permits",
  "ranking by reliability") rotates on a fixed schedule so users
  read progress, not a stuck spinner.
- @keyframes card-in: real worker cards rise 4px and fade in over
  350ms with nth-child stagger across the first ~12 rows. Honors
  prefers-reduced-motion.
- Avatar imgs filter through grayscale + slight contrast/blur to
  pull the SDXL Turbo color cast (which screams "AI generated" at
  small sizes). Cert icons get the same treatment.
- Once-per-session hero takeover compresses the Section ⓪ strip
  ("Not a CRM — an index that learns from you") into a centered
  hero on first paint, dismissed by clicking anywhere. Stats
  hydrate from live endpoints.

console.html: mirrors the avatar B&W filter for visual consistency,
and removes the headshot insertion entirely — back to monogram
initials. The console (internal staffer view) doesn't need synthetic
faces; the public demo at /lakehouse/ does.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-28 05:35:54 -05:00
root
8e1855e779 demo: icon recipe pipeline + role-aware portraits + ComfyUI negative-prompt override 
Adds two single-source-of-truth recipe files that drive both the
hot-path render server and the offline pre-render scripts:

- role_scenes.ts: per-role-band scene clauses (clothing + backdrop).
  Forklift operators look like forklift operators instead of
  collapsing to interchangeable studio shots. SCENES_VERSION mixes
  into the headshot cache key so a coordinator tweak refreshes every
  matching face on next view.
- icon_recipes.ts: cert / role-prop / status / hazard / empty icons
  with deterministic per-recipe seeds + fuzzy text resolver.
  ICONS_VERSION suffix on the cached file means edits don't
  overwrite in place — misfires are recoverable.

Routes (mcp-server/index.ts):
- GET /headshots/_scenes — exposes SCENES + version to the
  pre-render script so prompts don't drift between batch and hot-path.
- GET /icons/_recipes — same idea for icons.
- GET /icons/cert?text=... — resolves free-text cert names to a
  recipe and 302s to the rendered icon. 404 (not 500) when no recipe
  matches so the front-end can hang `onerror="this.remove()"`.
- GET /icons/render/{category}/{slug} — cache-or-render at 256² (8
  steps) for crisper edges than 512² when downsampled to 14px.

ComfyUI portrait support (scripts/serve_imagegen.py):
The editorial workflow had `human, person, face` baked into its
negative prompt — actively sabotaging portraits. _comfyui_generate
now accepts negative_prompt/cfg/sampler/scheduler overrides, and
those mix into the cache key so portrait calls don't collapse into
hero-shot cache hits.

scripts/staffing/render_role_pool.py: pre-renders the role-aware
face pool by reading SCENES from /headshots/_scenes — single source
of truth verified at run time.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-28 05:35:36 -05:00
root
313eec3c6e staffing: face pool fetch preserves prior tags + --shrink gate + atomic manifest write 
fetch_face_pool was wiping 952 hand-classified rows when re-run from
a Python without deepface installed (it reset every gender to None).
Now:

- Loads existing manifest by id and overlays only fetch-owned fields,
  so gender/race/age/excluded survive a refetch.
- deepface pass tags only records that don't already have a gender;
  deepface unavailable means "leave existing tags alone" not "reset".
- New --shrink flag required to drop ids >= --count. Default refuses
  to shrink the pool silently.
- Atomic write via tmp + os.replace so an interrupted run can't
  corrupt the manifest.
- Dedupes duplicate id lines (root cause of the 2497-row manifest
  backing a 1000-face pool).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-28 05:35:19 -05:00
root
51cc0a69cf ops: track tif_polygons.ts orphan import 
entity.ts imports findTifDistrict from ./tif_polygons.js but the
source file was never committed — only present in the working tree.
Adding it so a fresh clone compiles.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-28 05:35:09 -05:00
root
528fded11b Surname → ethnicity routing + ComfyUI fallback for sparse pool buckets + cache-buster 
Three problems J flagged ("not matching properly", "same faces", "still
showing old icons") had three different roots:

1. MISMATCH: front-end was first-name only, so "Anna Cruz" / "Patricia
   Garcia" / "John Jimenez" all defaulted to caucasian. Added
   SURNAMES_HISPANIC / _SOUTH_ASIAN / _EAST_ASIAN / _MIDDLE_EASTERN
   dicts to both search.html and console.html. Surname is checked
   FIRST (stronger signal for hispanic + asian than first names),
   then first-name fallback. Cruz → hispanic, Patel → south_asian,
   Nguyen → east_asian, regardless of first name.

2. SAME FACES: pool buckets are uneven — woman/south_asian=3,
   man/black=4, woman/middle_eastern=2 — so any worker in those
   buckets collapses to 2-4 photos no matter how good the hash is.
   /headshots/:key now 302-redirects to /headshots/generate/:key
   when the gender × race intersection is below 30 faces. ComfyUI
   on-demand gives infinite uniqueness for the sparse buckets
   (deterministic-per-worker via djb2 seed). Dense buckets still
   serve from the pool — no GPU cost there.

3. STALE CACHE: Cache-Control was max-age=86400, immutable — pinned
   old photos in browsers for 24h after any server-side update.
   Dropped to max-age=3600, must-revalidate, and added a v=2
   cache-buster query param to all front-end /headshots/ URLs so
   existing cached entries are bypassed on next page load.

Also surfacing X-Face-Pool-Bucket / Bucket-Size headers for diagnosis.

Verified: playwright run shows surname routing correct (Torres,
Rivera, Alvarez, Gutierrez, Patel, Nguyen, Omar all bucketed
correctly), sparse buckets 302 to ComfyUI, dense buckets stay on
the thumb pool.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-28 00:44:18 -05:00
root
64700ea6da Synthetic face pool — 1000 StyleGAN headshots, ComfyUI hot-swap, 60x smaller thumbs 
Worker cards now ship a real photo per person instead of monogram tiles:

  - fetch_face_pool.py pulls 1000 faces from thispersondoesnotexist.com
  - tag_face_pool.py runs deepface for gender/race/age, excludes <22yo
  - manifest.jsonl: 952 servable, gender/race buckets populated
  - /headshots/_thumbs/ pre-resized to 384px webp (587KB -> 11KB,
    60x smaller; without this Chrome's parallel-connection budget
    drops ~75% of tiles in a 40-card grid)
  - /headshots/:key gender x race x age intersection bucketing with
    gender-only fallback when intersection is sparse
  - /headshots/generate/:key ComfyUI on-demand for the contractor
    profile spotlight (cold ~1.5s, cached ~1ms; worker-derived
    djb2 seed makes faces deterministic-per-worker but unique
    across workers sharing the same prompt)
  - serve_imagegen.py _cache_key() now includes seed (was caching
    by prompt only -> 3 different worker seeds collapsed to 1
    cached image; verified fix produces 3 distinct md5s)
  - confidence-default name resolution: Xavier->man+hispanic,
    Aisha->woman+black, etc. Every worker resolves to a bucket.

End-to-end: playwright run on /?q=forklift+operators+IL -> 21/21
cards loaded, 0 broken, all 384px webp.

Cache + binary pool gitignored; manifest tracked.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-04-28 00:34:55 -05:00
root
5225211e45 demo: real synthetic headshots — fetch pool + serve route + UI wire 
Three layers shipped:

1. SCRIPT — scripts/staffing/fetch_face_pool.py
   Pulls N synthetic StyleGAN faces from thispersondoesnotexist.com
   into data/headshots/face_NNNN.jpg, writes manifest.jsonl. Idempotent:
   re-running skips existing files. Optional gender tagging via deepface
   (currently unavailable on this box; the script handles ImportError
   gracefully and tags everything as untagged). Fetched 198 faces with
   concurrency=3 in ~67s.

2. SERVER — /headshots/:key route in mcp-server/index.ts
   Loads manifest at first hit, caches in globalThis._faces. Hashes the
   key with djb2-style mixing → pool index → returns the JPG. Same
   key always gets the same face (deterministic). Accepts
   ?g=man|woman&e=caucasian|black|hispanic|south_asian|east_asian|middle_eastern
   to bias pool selection — the gender/ethnicity buckets fall back to
   the full pool when no tagged matches exist. Cache-Control:
   86400 immutable so faces ride the browser cache after first hit.
   /headshots/__reload re-reads the manifest without restart.

3. UI — search.html + console.html worker cards
   Re-added overlay <img> on top of the monogram .av circle. img.src
   = /headshots/<encoded-key>?g=<hint>&e=<hint>. img.onerror removes
   the failed image so the monogram stays visible if the face pool
   isn't fetched / CDN is blocked. .av now has overflow:hidden +
   position:relative to clip the img to a perfect circle.

Forced-confident name resolution (J: "we're CREATING the profile,
created as though you truly have the information Xavier is more
likely Hispanic and he's a male"):

   genderFor(name)        — looks up MALE_NAMES + FEMALE_NAMES,
                            falls back to a deterministic hash split
                            so unknown names spread ~50/50. Sets now
                            include cross-cultural names: Alejandro/
                            Andres/Mateo/Santiago/Joaquin/Cesar/Hugo/
                            Felipe/Gerardo/Salvador/Ramon (Hispanic),
                            Raj/Anil/Vikram/Krishna/Pradeep (South
                            Asian), Wei/Yi/Hiroshi/Akira/Hyun (East
                            Asian), Demetrius/Kareem/DaQuan/Khalil
                            (Black), Omar/Khalid/Hassan/Ahmed/Bilal
                            (Middle Eastern). FEMALE_NAMES extended
                            in parallel.

   guessEthnicityFromFirstName(name)
                          — confident default of 'caucasian' for any
                            name not in the cultural buckets so every
                            worker resolves to a category the face
                            pool can be biased toward. Order: ME → Black
                            → Hispanic → South Asian → East Asian →
                            Caucasian (matters where names overlap,
                            e.g. Aisha appears in ME + Black, biases
                            toward ME for visual fit).

   Both helpers also ported into console.html so the triage backfills
   and try-it-yourself rendering get the same hint stack.

Privacy note in the script + route comments: the synthetic data uses
the worker's name as the seed; production should hash worker_id (not
name) to avoid leaking PII to a third-party CDN. The fetch URL itself
is referenced once per pool build, not per-worker.

.gitignore — added data/headshots/face_*.jpg (~100MB for 198 faces;
the manifest + script are tracked). Re-running the script on a fresh
checkout rebuilds the pool from scratch.

Verified end-to-end via playwright on devop.live/lakehouse:
   forklift query → 10 worker cards
   10/10 with face images (real synthetic headshots, not monograms)
   0/10 broken
   Alejandro G. Nelson  → ?g=man&e=hispanic
   Patricia K. Garcia    → ?g=woman&e=caucasian
   Each name → unique face, deterministic across loads.
   Console triage backfills get the same treatment.
 2026-04-28 00:04:03 -05:00
root
4f0b6fb9b3 demo: console — sober worker cards (mirror dashboard styling) 
J: "can you update Staffer's Console too the same look." Console
rendered worker rows in three places (Chapter 4 permit-contract
candidates, Chapter 8 triage backfills, Chapter 9 try-it-yourself
results) with the original 28px square avatar + flat backgrounds —
inconsistent with the new dashboard design.

Three changes:

1. CSS — .worker now has a 3px left-edge border that color-codes the
   role family, and .av is a 32px circle with a muted dark background
   + 1px ring + monogram initials. Five role-band colors mirror
   search.html: warehouse blue / production amber / trades purple /
   driver green / lead orange. Plus a .role-pill style matching the
   dashboard's small uppercase chip.

2. Helpers — added ROLE_BANDS regex table + roleBand() classifier and
   a new workerRow(name, role, detail, opts) builder. Same regex
   patterns as search.html so a "Forklift Operator" classifies
   identically on every page. opts.endorsed adds the green endorsed
   chip; opts.score appends a rank badge.

3. Replaced the three inline avatar+row constructors with workerRow()
   calls. Net: console.html lost ~20 lines of duplicated DOM building
   while gaining role bands + pills.

Verified end-to-end via playwright on devop.live/lakehouse/console:
  Chapter 8 triage scenario "Marcus running late site 4422":
    5 backfill rows render with [warehouse] band + WAREHOUSE pill +
    monogram avatars (SBC, ETW, SHC, WMG, MEB).
  Same sober look as the dashboard worker cards. No emojis, no
  cartoons, color-coded role family on the left edge.
 2026-04-27 23:47:12 -05:00
root
8e781ac325 demo: worker cards — sober monogram avatars + role bands (no cartoons) 
J: "It's two cartoonish right now the website looks like it was made
by first grade teacher." Pulled the DiceBear personas-style headshots
and the emoji role badges. They were generative-illustration playful;
this is supposed to read like a staffing tool, not a kindergarten
attendance sheet.

Replacement design — restraint, signal, no glyphs:

  Avatar:   40px circle, monogram initials, muted dark background
            (#161b22), 1px ring (#21262d), white-ish text. No image,
            no emoji. Looks like a pre-photo placeholder slot in a
            real ATS.

  Role band: the role gets classified into one of five families:
            WAREHOUSE / PRODUCTION / SKILLED TRADE / DRIVER / LEAD
            (regex-based; falls back to the first word of the role
            for unknown families). Each family has a single muted
            color: blue / amber / purple / green / orange. The
            color appears as:
              - a 3px left border on the .iworker card
              - a 2px left border + matching text color on a small
                uppercase pill in the detail line

That's it. No images, no emojis, no per-role illustrations. The
staffer sees role-family at a glance via the band color, name and
initials prominently, full role + city + zip in the detail string
behind the pill. Five colors total instead of an eight-color rainbow.

CSS:
  .iworker[data-role-band="warehouse"] etc. → 3px left border
  .role-pill[data-rb="warehouse"] etc.      → matching pill border

JS:
  ROLE_BANDS = 6 regex → band+label entries (warehouse, production,
                          trades, driver, lead, quality)
  roleBand(role)       = first matching entry, fallback to first
                          word of role uppercased

Verified end-to-end via playwright on devop.live/lakehouse:
  forklift query → 10 cards
  every card → monogram avatar + WAREHOUSE pill (blue band)
  no images, no emojis, no rainbow

Restart sequence after these edits:
  pkill -9 -f "/home/profit/lakehouse/mcp-server/index.ts"
  ( setsid bun run /home/profit/lakehouse/mcp-server/index.ts \
      > /tmp/mcp-server.log 2>&1 < /dev/null & disown )
 2026-04-27 23:43:36 -05:00
root
ee0450b7c3 demo: spec — refresh repo layout + model fleet + per-staffer + paths 8-9 
J: "how about devop.live/lakehouse/spec." Spec was anchored on
2026-04-21 state (v2 footer): mistral mentioned in the model matrix,
13 crates not 15, missing validator/truth/auditor crates, no mention
of OpenCode 40-model fleet, no pathway memory, no per-staffer
hot-swap, no Construction Activity Signal Engine, ADR count was 20.
Footer claimed Phases 19-25.

Edits, in order:

  Ch1 Repository layout
    + crates/truth/ (ADR-021 rule store)
    + crates/validator/ (Phase 43 — schema/completeness/policy gates)
    + auditor/ (cross-lineage Kimi↔Haiku/Opus auto-promote)
    + scripts/distillation/ (frozen substrate v1.0.0 at e7636f2)
    Updated aibridge to mention ProviderAdapter dispatch
    Updated gateway to mention OpenAI-compat /v1/* drop-in middleware
    Updated mcp-server route list to include /staffers + profiler/contractor pages
    Updated config/ to mention modes.toml + providers.toml + routing.toml
    Updated docs/ ADR count from 20 → 21
    Updated data/ to mention _pathway_memory + _auditor/kimi_verdicts

  Ch3 Measurement & indexing
    REPLACED stale "Model matrix (Phase 20)" T1-T5 table that
    mentioned mistral with the current 5-provider fleet:
      ollama / ollama_cloud / openrouter / opencode (40 models, one
      sk-* key reaches Claude Opus 4.7, GPT-5.5-pro, Gemini 3.1-pro,
      Kimi K2.6, GLM, DeepSeek, Qwen, MiniMax, free) / kimi
    ADDED 9-rung cloud-first ladder pseudocode
    ADDED N=3 consensus + cross-architecture tie-breaker math
    ADDED auditor cross-lineage Kimi K2.6 ↔ Haiku 4.5 + Opus auto-promote
    ADDED distillation v1.0.0 freeze paragraph (145 tests, 22/22, 16/16)
    Updated Continuation primitive to mention Phase 44 Rust port

  Ch5 What a CRM can't do
    Extended the table with 6 new capabilities:
      - Per-staffer relevance gradient
      - Triage in one shot (late-worker → backfills + draft SMS)
      - Permit → fill plan derivation
      - Public-issuer attribution across contractor graph
      - Cross-lineage AI audit on every PR
      - Pathway memory (system-level hot-swap, ADR-021)

  Ch6 How it gets better over time
    Lede updated from 7 paths → 10 paths
    NEW Path 7 — Pathway memory (ADR-021)
    NEW Path 8 — Per-staffer hot-swap index
    NEW Path 9 — Construction Activity Signal Engine
    Original Path 7 (observer ingest) renumbered to Path 10

  Ch9 Per-staffer context
    Lede now anchors Path 8 from Ch6
    NEW lead section: Per-staffer hot-swap index — Maria/Devon/Aisha,
    same query reshapes per coordinator (167 IL / 89 IN / 16 WI),
    MARIA'S MEMORY pill, /staffers endpoint, metro-agnostic by
    construction. The original Phase 17 profile / Phase 23 competence
    sections retained beneath as the deeper architecture detail.

  Ch10 A day in the life
    Updated 14:00 emergency event to use the late-worker triage
    handler — coordinator types "Dave running late site 4422", gets
    profile + draft SMS + 5 backfills + Copy SMS button in 250ms.
    The old Click No-show button → /log_failure flow remains valid
    (penalty still records); the user-facing surface is the new
    triage card.

  Ch11 Known limits
    REPLACED the Mem0/Letta/Phase-26 era list with current honest
    limits: BAI persistence + backtesting, NYC DOB adapter, 12
    awaiting public-data sources for contractor profile, rate/margin
    awareness, Mem0-style UPDATE/DELETE, Letta hot cache (now 5K
    not 1.9K), confidence calibration, SEC fuzzy precision, tighter
    pathway+scrum integration.

  Footer
    v2 2026-04-21 → v3 2026-04-27
    Phases 19-25 → 19-45
    Lists today's phases: distillation v1.0.0 substrate, gateway as
    OpenAI-compat drop-in, mode runner, validator + iterate, ADR-021
    pathway memory, per-staffer hot-swap, Construction Activity Signal
    Engine.

  Nav
    + Profiler link
    Date pill v1 · 2026-04-20 → v3 · 2026-04-27

Verified end-to-end on devop.live/lakehouse/spec — 11 chapter h2s
render in order, 67KB page (was 50KB-ish), all internal links resolve.
 2026-04-27 23:22:07 -05:00
root
0f9b4aa2fe demo: proof — full architecture-page rewrite for current state 
J: "needs a rewrite." Old version was anchored on a dual-agent
mistral+qwen2.5 loop that hasn't been the model story for weeks,
called the system 13 crates (it's 15), referenced "Local 7B models"
in the honest-limits section, and had no mention of:
  - the 40-model OpenCode fleet via one sk-* key
  - the 9-rung cloud-first ladder
  - N=3 consensus + cross-architecture tie-breaker
  - auditor cross-lineage (Kimi K2.6 ↔ Haiku 4.5, Opus auto-promote)
  - distillation v1.0.0 frozen substrate (e7636f2)
  - pathway memory (88 traces, 11/11 replays, ADR-021)
  - per-staffer hot-swap index
  - Construction Activity Signal Engine + BAI + ticker network
  - the gateway as OpenAI-compat drop-in middleware

Rewrote into 10 chapters:

  1.  Receipts — live tests + new live tile showing the Signal Engine
      view for THIS load (issuer count, attributed build value,
      contractor count, attribution edges)
  2.  Architecture — corrected to 15 crates with current responsibilities;
      ASCII diagram showing OpenAI consumers + MCP + Browser all hitting
      gateway /v1/*; provider fleet table with all 5 (ollama, ollama_cloud,
      openrouter, opencode 40-model, kimi); validator + truth + auditor
      crates added
  3.  Model fleet — REPLACED the dual-agent mistral story. Now: the
      9-rung ladder (kimi-k2:1t through openrouter:free → ollama local),
      N=3 consensus + tie-breaker math, auditor Kimi↔Haiku alternation
      with Opus auto-promote on big diffs, distillation v1.0.0 freeze
      tag e7636f2 (145 tests · 22/22 · 16/16 · bit-identical)
  4.  Two memory layers — kept playbook content (Phase 19 boost math
      still load-bearing), added pathway memory (ADR-021) section with
      live counters in the page (88 / 11-11 / 100% reuse rate)
  5.  Per-staffer hot-swap — NEW. Pseudocode showing how staffer_id
      scopes state filter + playbook geo + UI relabel to MARIA'S MEMORY
  6.  Construction Activity Signal Engine — NEW. Three attribution
      flavors (direct, parent, associated), BAI math, cross-metro
      replication framing (NYC DOB next, then LA / Houston / Boston)
  7.  Architectural choices — added ADR-021 row + distillation freeze row
  8.  Measured at scale — kept (uses /proof.json scale data)
  9.  Verify or dispute — REFRESHED with current endpoints. Removed the
      stale "bun run tests/multi-agent/scenario.ts" recipe; added curl
      examples for /v1/health, pathway/stats, per-staffer scoping (3-loop
      bash script), late-worker triage, profiler_index, ticker_quotes,
      auditor verdicts, distillation acceptance gate
  10. What we are NOT claiming — REFRESHED. Removed "Local 7B models"
      caveat; added: 12 awaiting public-data sources are placeholders,
      SEC name-fuzzy has rare false positives, BAI is a thesis not a
      backtest yet, single-metro today

Live data probes added:
  loadPathwayLive   — fills pwm-traces / pwm-replays / pwm-rate spans
  loadSignalLive    — renders the LIVE Signal Engine tile under Ch1

Nav also gained a Profiler link to match search.html and console.html.

Verified end-to-end on devop.live/lakehouse/proof:
  10 chapters render, 5/5 live tests pass, pathway shows 88 traces +
  100% reuse rate, live signal tile shows 11 issuers + $347M attributed
  + 200 contractors + 14 attribution edges. Architecture diagram +
  crate table accurate as of HEAD.
 2026-04-27 23:13:46 -05:00
root
e4eb0fa168 demo: console — three new chapters reflecting recent shipments 
J: "it's outdated." Console walkthrough was stuck on the original 6
chapters (legacy-bridge / permits / catalog / ranking demo / playbook
memory / try-it-yourself). Three weeks of new work weren't visible.

Three new chapters added between the existing playbook-memory chapter
and the input box; all pull live data from the running system:

  Chapter 6 — Three coordinators, three views of the same corpus
    Renders Maria/Devon/Aisha cards from /staffers with their
    territories. Frames the per-staffer hot-swap as the relevance
    gradient that compounds independently per coordinator. Same query
    "forklift operators" returns 89 IN / 16 WI / 167 IL workers
    depending on who's acting.

  Chapter 7 — The hidden signal — public issuers in your contractor graph
    Pulls /intelligence/profiler_index, builds the basket, shows
    issuer count + attributed build value + contractor count as the
    three top metrics. Lists top 8 issuers with attribution counts
    and direct-link to the profiler. This is the BAI / Signal Engine
    pitch in walkthrough form: every contractor name is also a forward
    indicator on a public equity. Cross-metro replication explicit
    in the closing paragraph.

  Chapter 8 — When something breaks — triage in one shot
    Live triage demo against /intelligence/chat with body
    {message:"Marcus running late site 4422"}. Renders the worker
    card + draft SMS + 5 backfills + duration_ms. The 250ms-vs-20min
    moment, made concrete with real Quincy IL workers.

Chapter 9 (was 6) — Try it yourself
  Updated input examples to demonstrate each new route:
    "8 production workers near 60607" → headcount + zip parser
    "Marcus running late site 4422"  → triage handler
    "Marcus"                          → bare-name lookup
    "what came in last night"         → temporal route
    "reliable forklift operators with OSHA certs" → hybrid SQL+vector
  Each is a click-to-run link beneath the input.

Two new accent classes: .accent-g (green for issuer-count) and
.accent-r (red for triage event).

Verified end-to-end on devop.live/lakehouse/console: 9 chapters
render, ch6 shows 3 staffer personas, ch7 shows 11 issuers / $347M /
200 contractors, ch8 shows Marcus V. Campbell + draft SMS + 5
backfills.
 2026-04-27 23:04:37 -05:00
root
93081bed5c ops: persist runtime fixes — iterate.rs unused state, catalog cleanup 
Two load-bearing runtime changes that were never committed:

1. crates/gateway/src/v1/iterate.rs — `state` → `_state` on the unused
   route-state parameter. Cleared the one cargo workspace warning.
   Fix was made earlier this session but the working-tree change
   never made it into a commit.

2. data/_catalog/manifests/564b00ae-cbf3-4efd-aa55-84cdb6d2b0b7.json —
   DELETED. This was the dead manifest for `client_workerskjkk`, a
   typo dataset whose parquet was deleted but whose catalog entry
   stayed registered. Every SQL query failed schema inference on the
   missing file before reaching its target table — that's the bug
   that made /system/summary report 0 workers and the demo show zero
   bench. Deleting the manifest keeps the fix on disk; committing
   the deletion keeps it in git so a fresh checkout doesn't regress.

3. data/_catalog/manifests/32ee74a0-59b4-4e5b-8edb-70c9347a4bf3.json
   — runtime catalog metadata update from the successful_playbooks_live
   write path. Ride-along change.

Reports under reports/distillation/phase[68]-*.md are auto-regenerated
by the audit cycle each run; skipping those.
 2026-04-27 23:00:16 -05:00
root
885a1acf19 demo: System Activity panel — capability index reflects every recent shipment 
Old panel showed playbook ops + search counts and went empty in a
fresh demo (no operations yet). J: "update System Activity to coincide
with all of our recent updates."

Rebuilt as a live capability index — each tile is a thing the
substrate has learned to do, with the metric proving it's running.
Pulled in parallel from /staffers, /system/summary,
/api/vectors/playbook_memory/stats, /api/vectors/pathway/stats,
/intelligence/profiler_index, /intelligence/activity. Each probe
catches its own error so a single missing endpoint doesn't collapse
the panel.

Nine capability cards (verified end-to-end on devop.live/lakehouse):

  1. Per-staffer hot-swap index           3 personas (Maria/Devon/Aisha)
  2. Construction Activity Signal Engine  11 issuers · $347M attributed
                                          build value · network 11/14
  3. Late-worker / no-show triage         one-shot — name+late → backfills+SMS
  4. Permit → staffing bridge             24/day, every Chicago permit ≥$250K
  5. Hybrid SQL + vector search           500K workers · 5,474 playbook entries
  6. Schema-agnostic ingestion            36 datasets · 2.98M rows
  7. Contractor profile + project index   6 wired · 12 queued sources
  8. Pathway memory                       88 traces · 11/11 replays · 100%
  9. Ticker association network           11 tickers · 3 direct + 11 associated

Each card carries:
  - capability title + ship date pill ("baseline" or "shipped 2026-04-27")
  - big metric (live, not pre-baked)
  - sub-context line in coordinator language
  - "why a staffer cares" explanation
  - optional "Open →" deep link to the surface (Profiler, Contractor)

Header + intro paragraph reframed: "what the substrate has learned to
do" instead of "what the substrate has learned." Operational learning
(fills, playbooks, hot-swaps) compounds INSIDE each capability; the
panel surfaces the set of capabilities the corpus knows how to express.

Closing operational-stats row at the bottom shows fills/searches/
recent playbooks when /intelligence/activity has any.
 2026-04-27 22:54:52 -05:00
root
97888e3775 demo: profiler — Construction Activity Signal Engine narrative + BAI 
J's prompt: shoot for the stars, frame the data corpus's value as a
predictive signal, not just a contractor directory. The thesis is
that every name in this corpus is also a forward indicator on public
equities — permits filed today predict construction starts in ~45
days, staffing in ~30, revenue recognition months later. The
associated-ticker network surfaces this signal before any 10-Q does.

Two new layers above the basket:

1. HERO THESIS PANEL — "Chicago Construction Activity Signal Engine"
   header + 3-line value statement, then 4 live metrics:

   - BAI (Building Activity Index) — attribution-weighted average of
     day-change % across surfaced issuers. Weight = attribution count
     so issuers we have more depth on count more. Today: +0.76%
     (9 issuers · top contributors FCBC +2.4%, ACRE +1.7%, JPM +1.5%).
     Color-coded green/red.

   - Indexed build value — total $ of permits attributable to ANY
     public issuer in this view. Today: $344M.

   - Network depth — issuers / attribution edges. Today: 9 / 15.
     This is the "we see what nobody else sees" metric: how many
     contractors are bridges from a private builder back to a public
     equity holder.

   - Market replication roadmap — chips showing "Chicago — live ·
     NYC DOB — adapter ready · LA County · Houston BCD · Boston ISD
     · DC DCRA". Frames the corpus as metro-agnostic from day one.

2. PER-TICKER ACTIVITY MAP — when a basket card is clicked, a leaflet
   map appears below the basket plotting that ticker's geocoded permit
   activity. Pulls /intelligence/contractor_profile for up to 6
   attributed contractors, merges their geocoded permits, plots on a
   dark Chicago tile layer. Color-banded by permit cost (green <$100K,
   amber $100K-$1M, red ≥$1M). Click TGT → 23 Target permits across
   Chicago; click JPM → JPMorgan-adjacent contractor activity. Cached
   per ticker so toggling is instant.

Verified end-to-end on devop.live/lakehouse/profiler:
  Default load: hero panel renders with all 4 metrics, basket strip
                with 9 issuers + live prices in 669ms.
  Click TGT  : signal map activates, "23 geocoded permits across
                1 contractor", table filters to 2 rows.
  Tooltip on basket cards: full reason path including matched name +
                contributors attributed to that ticker.

Architecture-side: zero new server code — all metrics computed
client-side from the existing profiler_index + ticker_quotes payloads.
The corpus already had the value; the page just needed to articulate it.
 2026-04-27 22:23:46 -05:00
root
9b8befaa94 demo: profiler — scrolling ticker basket with live prices + click-to-filter 
J asked: "kind of like a scrolling ticker that has all of the companies
and their stock prices and where they fit in the map." Implemented as
a horizontal-scroll strip at the top of /profiler:

  9 public issuers in this view · quotes via Stooq · 669ms
  ┌────┬────┬────┬────┬────┬────┬────┐
  │TGT │JPM │BALY│ACRE│FCBC│NREF│LSBK│ ← live price + day-change per
  │129 │311 │... │... │... │... │... │   ticker, color-banded by
  │+.17│+1.5│... │... │... │... │... │   attribution kind
  └────┴────┴────┴────┴────┴────┴────┘

Each card carries:
  - ticker + live price + day-change % (red/green)
  - attribution count + kind (exact / direct / parent / associated)
  - left bar color = strongest attribution kind (green for direct
    issuer, amber for parent, blue for co-permit associated, gradient
    when both direct and associated apply)
  - tooltip on hover lists the contractors attributed to this ticker
  - click toggles a filter on the table below — clicking TGT cuts the
    200-row list down to just TARGET CORPORATION + TORNOW, KYLE F
    (Target's primary co-permit contractor)

Server-side:
- entity.ts exports fetchStooqQuote (was internal)
- new POST /intelligence/ticker_quotes — accepts {tickers: [...]},
  fans out to Stooq.us in parallel, returns
  {ticker, price, price_date, open, high, low, day_change_pct,
   stooq_url} per symbol or null for non-US listings (HOC.DE, SKA-B.ST,
   LLC.AX). Capped at 50 symbols per call.

Front-end:
- mcp-server/profiler.html — new .basket-wrap section above the
  controls. buildBasket() runs after profiler_index loads:
    1. Aggregates unique tickers from .tickers.direct + .associated
       across all surfaced contractors
    2. Renders shells immediately (ticker symbol + "—" placeholder)
    3. Batch-fetches quotes via /intelligence/ticker_quotes
    4. Updates each card with price + day-change in place
  Click on a card sets a tickerFilter; render() skips rows whose
  attributions don't include that ticker. "clear filter" button on
  the basket strip resets it.

Verified end-to-end on devop.live/lakehouse/profiler:
  Default load → 9 issuers, live prices populated in 669ms
  TGT click   → table filters to TARGET CORPORATION + TORNOW, KYLE F
                (the contractor who runs 3 of Target's recent permits
                gets the TGT correlation indicator)
  JPM card    → $311.63, +1.55% — JPMorgan-adjacent contractors
  Tooltip     → list of contractors attributed to the ticker
 2026-04-27 22:19:26 -05:00
root
2965b68a9d demo: profiler index — ticker associations (direct, parent, co-permit) 
J's framing: "if a contractor works for Target, future Target contracts
mean money flows back to the contractor — the ticker is an associated
indicator." Now the profiler index attaches three flavors of ticker per
contractor and renders them as colored pills:

  green DIRECT    contractor IS the public issuer (Target Corp → TGT)
  amber PARENT    contractor is a subsidiary of a public parent
                    (Turner Construction → HOC.DE via Hochtief AG)
  blue  ASSOCIATED contractor co-appears on permits with a public
                    entity (TORNOW, KYLE F → TGT, 3 shared permits with
                    TARGET CORPORATION)

The associated flavor is the correlation signal J described — it pulls
the ticker for whoever the contractor has been working *with*, not
just what they are themselves. Most contractors are private; the
associated link is how the moat shows up.

Server-side:
- entity.ts new export `lookupTickerLite(name)` — cheap in-memory
  resolver that does only the SEC tickers index lookup + curated
  KNOWN_PARENT_MAP check, no per-call SEC profile or Stooq fetch.
  ~10ms per name after the index is loaded once.
- /intelligence/profiler_index now runs a third Socrata pull
  (5K permit pairs in window) to build a co-occurrence map. For each
  contractor in the result, attaches:
    .tickers.direct[]      — name matches a public issuer
    .tickers.associated[]  — top 5 co-permit partners that resolve
                              to a ticker, with partner_name +
                              co_permits count + partner_via reason

Front-end:
- mcp-server/profiler.html — new .ticker-pill styles (3 colors per
  attribution kind), pills render under the contractor name in the
  table. Hover title gives the full reason path.

Verified end-to-end on the public URL:
  search="tornow" → blue TGT pill, hint "Associated via co-permits
                    with TARGET CORPORATION (3 shared permits) —
                    TARGET CORP"
  search="target" → green TGT × 2 (TARGET CORPORATION +
                    CORPORATION TARGET name variants both resolve
                    direct to the same issuer)
  default top 200 → 15 ticker pills surface across the page including
                    JPM (via JPMORGAN CHASE BANK co-permits) and
                    parent-link tickers for the construction majors.
 2026-04-27 22:08:24 -05:00
root
08c8debfff demo: profiler index — directory of every Chicago contractor 
J asked for "a profiler index that shows a history of everyone." This
is a /profiler directory page (also reachable via /contractors) that
ranks every contractor who's filed a Chicago permit, by total permit
value. Rows are clickable into the full /contractor profile.

Defaults: since 2025-06-01, min permit cost $250K, top 200 contractors
by total_cost. Server pulls two Socrata GROUP BY queries (one keyed on
contact_1_name, one on contact_2_name), merges them so contractors
listed in either applicant or contractor slot appear once with combined
counts/cost. ~300ms cold.

UI: live search box, since-date selector, min-cost selector, sortable
columns (name / permits / total_cost / last_filed). Live numbers as of
this write: 200 contractors, 1,702 permits, $14.22B aggregate. Filter
"Target" returns TARGET CORPORATION + CORPORATION TARGET (name variants
from Socrata).

Also fixes J's other complaint — "no new contracts, Target is gone":

  /intelligence/permit_contracts was hard-capped at $limit=6 + only
  the most recent 6 over $250K, so any day with 6 fresh permits would
  push older contractors (Target) off the panel entirely. Now defaults
  to 24 (caller can pass body.limit up to 100), so 2-3 days of permits
  stay on the panel. Added body.contractor — passes a name into the
  WHERE so the staffer can pin a specific contractor to the panel
  ("Target Corporation" → 3 of their permits over $250K).

Server-side:
- new POST /intelligence/profiler_index — paginated contractor index
  (since, min_cost, search, limit) with merged contact_1+contact_2
  aggregations
- /intelligence/permit_contracts — body.limit + body.contractor
- /profiler and /contractors routes serve profiler.html

Front-end:
- new mcp-server/profiler.html — sortable table, live filter, deep
  links to /contractor?name=... (prefix-aware via P, so /lakehouse
  works on devop.live)
- search.html + console.html nav: added "Profiler" link

Verified end-to-end via playwright on the public URL.
 2026-04-27 22:00:52 -05:00
root
b02cf5b9e1 demo: contractor links — respect the /lakehouse path prefix 
J reported https://devop.live/contractor?name=3115%20W%20POLK%20ST.%20LLC
returned 404. Cause: the anchor href was a bare /contractor, which on
devop.live routes to the LLM Team UI (port 5000) at the main site root,
not the lakehouse mcp-server (which lives under /lakehouse/*).

Every page that renders a contractor link now uses the same prefix
detector the dashboard already had:

  var P = location.pathname.indexOf('/lakehouse') >= 0 ? '/lakehouse' : '';

Files updated:
- search.html: entity-brief anchor + preview anchor → P+/contractor
- console.html: permit-card contractor list → P+/contractor
- contractor.html: history.replaceState + back-link + the
  /intelligence/contractor_profile fetch all use P prefix. The page
  is reachable at /lakehouse/contractor on the public URL and bare
  /contractor on localhost; both work without further config.

Verified:
  https://devop.live/lakehouse/contractor?name=3115%20W%20POLK%20ST.%20LLC
    → 200, 29.9 KB, full profile renders. Contractor has 1 permit on
    file (a small LLC), 1 geocoded so the heat map plots one marker.
 2026-04-27 21:44:59 -05:00
root
1ac8045924 demo: contractor profile — heat map, project index, 12 awaiting sources 
The contractor.html click-target J asked for: a separate page (not a
modal, not a fall-through search) showing every angle on a contractor.
Reachable from the Co-Pilot dashboard, the staffers console, and the
search box — all anchor-wrap contractor names to /contractor?name=...

What's new on the page:

1. PROJECT INDEX — build-signal score
   Single 0-100 number with the drivers laid out beneath. Driver list
   is staffer-readable: "59 Chicago permits in 180d (+30) · OSHA 20
   inspections (-25) · federal contractor (+15)". Score weights are
   placeholders to be replaced by an ML model once the 12 awaiting
   sources ship — the current 6 wired signals would not give a real
   model enough features.

2. HEAT MAP — every Chicago permit they've been contact_1 or contact_2
   on, last 24 months, plotted on a leaflet dark map. Color by cost
   (green <$100K, amber $100K-$1M, red ≥$1M), radius proportional to
   cost so the staffer sees where money + activity concentrates. Click
   a marker for permit detail (cost, date, work type, address, permit
   ID). All 50 of Turner Construction's geocoded recent permits in
   Chicago plot end-to-end.

3. ACTIVITY TIMELINE — monthly permit count, bar chart, with the
   first/last month labels so the staffer sees momentum. Tooltip on
   each bar gives the count and total cost for that month.

4. 12 AWAITING SOURCES — placeholder cards for the public datasets
   that would 3× the build-signal feature count. Each card has:
     - source name (real, e.g. DOL Wage & Hour, EPA ECHO, MSHA, BBB)
     - one-liner in coordinator language ("Has this contractor stiffed
       workers? Will they pay our staffing invoices?")
     - "Would show:" sample shape so the engineering scope is concrete
   Order is staffing-decision relevance:
     1. DOL Wage & Hour (WHD violations)
     2. State Licensure Boards (active license + expiry)
     3. Surety Bond Capacity (bonding ceiling)
     4. EPA ECHO Compliance (env violations at sites)
     5. DOT/FMCSA Carrier Safety (crash + OOS rates)
     6. BBB Complaints + Rating
     7. PACER Civil Suits (FLSA / Title VII / ADA)
     8. UCC Lien Filings (cash flow distress)
     9. D&B / Credit Bureau (PAYDEX, payment behavior)
    10. State UI Employer Claims (workforce stability)
    11. MSHA Mine Safety (excavation / aggregate / heavy)
    12. Registered Apprenticeships (DOL RAPIDS pipeline)

Server-side: entity.ts fetchContractorHistory now pulls the 50 most
recent permits with id + lat/lng + work_description, so the heat map
and timeline have what they need without a second SQL hop. The
ContractorHistory.recent_permits type gained the optional fields.

Front-end: contractor.html got 4 new render sections, leaflet wiring
(stylesheet + script in head), placeholder grid CSS, and a PLACEHOLDERS
const at the bottom with the 12 sources. All popup HTML is built via
DOM construction (textContent + appendChild) — no innerHTML, no XSS.

console.html: contractor names from /intelligence/permit_contracts now
anchor-wrapped to /contractor?name=... so the click-through J described
works from the staffers console too. Click stops propagation so the
permit details element doesn't toggle on the same click.

Verified end-to-end via playwright — Turner Construction profile shows:
  PIX score "Mixed signals — review drivers below"
  Heat map: "50 permits plotted · green/amber/red"
  4 section labels in order
  12 placeholder cards in the documented order
 2026-04-27 21:28:45 -05:00
root
52d2da2f44 demo: G — per-staffer hot-swap index (synthetic coordinator personas) 
Same corpus, different relevance gradient per staffer. Three personas
defined in mcp-server/index.ts STAFFERS roster (Maria/IL, Devon/IN,
Aisha/WI), each with a primary state + secondary cities. Server-side:
/intelligence/chat smart_search accepts a staffer_id body field; when
set, defaults state to the staffer's territory and labels the playbook
context as theirs. The playbook patterns query also defaults its geo
to the staffer's primary city/state, so the recurring-skills/cert
breakdowns reflect what they actually fill, not the global IL prior.

Front-end: a staffer selector dropdown beside the existing state/role
filters. Picking a staffer auto-pins state to their territory, shows
a greeting line, relabels the MEMORY panel as MARIA'S/DEVON'S/AISHA'S
MEMORY, and sends staffer_id to chat for scoping.

Dropdown is populated from /staffers (NOT /api/staffers — the generic
/api/* passthrough sends everything under /api/ to the Rust gateway,
which doesn't own the roster). loadStaffers runs at window-load
independently of loadDay's Promise.all so the dropdown populates even
if simulation/SQL inits error out.

Verified end-to-end via playwright. Same q="forklift operators":
  no staffer  → 509 workers across MI/OH/IA, MEMORY label
  as Devon    → 89 IN-only (Fort Wayne, Terre Haute), DEVON'S MEMORY
  as Aisha    → 16 WI-only (Milwaukee, Madison, Green Bay), AISHA'S MEMORY
As Maria with q="8 production workers near 60607":
  tags: headcount: 8 · zip 60607 → Chicago, IL · role: production · city: Chicago
  20 workers, MARIA'S MEMORY label, top results in Chicago zips

Closes the demo-side build of A-G from the persona plan:
  A. zip → city/state, B. headcount, C. bare-name, D. temporal,
  E. late-worker triage, F. contractor anchor, G. per-staffer index.
 2026-04-27 21:16:52 -05:00
root
d44ad3af1e demo: P2 — staffer-language routes (zip, headcount, name, late-triage, ingest log) 
Built from a playwright run as three personas:
  Maria   — "8 production workers near 60607 by next Friday, prior-fill at this client"
  Devon   — "what came in last night?"
  Aisha   — "Marcus running late site 4422"

Each one previously fell through to smart_search and returned irrelevant
results (geo wrong, headcount ignored, no triage, no temporal). Now:

A. Zip code → city/state lookup. Chicago zips (606xx, 607xx, 608xx)
   resolve to {city: Chicago, state: IL}; 13 metro prefixes covered.
   Maria's "near 60607" now returns Chicago workers, not Dayton/Green Bay.

B. Headcount parser. "8 production workers" / "12 forklift operators" /
   "5 welders" set top_k 1..200, capped 5..25 for SQL+vector LIMIT.
   Allows 0-2 role words between the count and the worker noun so
   "8 production workers" matches as well as "8 workers".

C. Bare-name profile lookup. Single short capitalized phrase
   ("Marcus" / "Sarah Lopez") triggers a profile route. Per-token LIKE
   AND-joined so "Marcus Rivera" matches "Marcus L. Rivera" without
   hardcoding middle initials.

E. Late-worker / no-show triage. Pattern: <Name> (running late|late|
   no show|sick|out today|called out|can't make it) — pulls profile +
   reliability + responsiveness + recent calls, sources 5 same-role
   same-geo backfills sorted by responsiveness, drafts a client SMS
   the coordinator can copy. Front-end renders triage card + Copy SMS
   button + green backfill list.

F. Contractor name preview anchor. The PROJECT INDEX preview line on
   each permit card now wraps contact_1_name and contact_2_name in
   anchors to /contractor?name=... — clicking a contractor finally
   navigates instead of doing nothing. Click handler stops propagation
   so the details element doesn't toggle.

D. Temporal "what came in" route. last night / today / past N hours /
   recent — surfaces datasets from the catalog whose updated_at is
   within the window, samples one row per dataset to detect worker-
   shape, groups by role for worker tables. Schema-agnostic — drop
   any dataset and it shows up. Currently sparse because no fresh
   ingest has happened today; will populate as ingest runs.

Server: /intelligence/chat smart_search route accepts structured
state/role from the search-form dropdowns (P1 from prior commit) and
now ALSO honors b.state, b.role, q.match for headcount + zip + name +
triage patterns BEFORE falling through to NL parsing.

Front-end: doSearch dispatches on response.type and renders triage,
profile, ingest_log, and miss states with type-specific UI. All DOM
construction uses textContent / appendChild — no innerHTML, no XSS.

Verified end-to-end via playwright drive of devop.live/lakehouse:
  Maria  → 8 Chicago Production Workers (60685, 60662, 60634)
           tags: "headcount: 8 · zip 60607 → Chicago, IL · ..."
  Aisha  → Marcus V. Campbell card + draft SMS + 5 Quincy IL backfills
           "I'm dispatching Scott B. Cooper (96% reliability) to cover."
  Devon  → ingest_log surfaces successful_playbooks_live (last 1h)
  Marcus → 5 profiles (Adams Louisville KY, Jenkins Green Bay WI, ...)

Screenshots: /tmp/persona_v2/{01_maria,02_aisha,03_devon,04_marcus}.png

Restart sequence after these edits: pkill -9 -f "mcp-server/index.ts" ;
cd /home/profit/lakehouse ; bun run mcp-server/index.ts. The bun on
:3700 is not systemd-managed (pre-existing convention).
 2026-04-27 21:05:40 -05:00
root
89ac6a9b5b demo: P1 — search filter now actually filters by state and role 
The Co-Pilot search box read state and role from the dropdowns (#sst, #srl)
but appended them to the message string as ' in '+st. The server's NL
parser then matched the literal preposition "in" against the case-insensitive
regex /\b(IL|IN|...)\b/i and assigned state IN (Indiana) to every search.
Result: typing "forklift in IL" returned Indiana workers. Same for WI, TX,
any state — all silently became Indiana. That was the "cached/generic
response" the legacy staffing client was seeing.

Two prongs:

1. search.html doSearch() now passes structured fields:
     {message, state, role}
   instead of munging into the message text. Dropdown selections bypass
   NL parsing entirely.

2. /intelligence/chat smart_search route accepts those structured fields
   and prefers them over regex archaeology. Falls back to NL parsing only
   when fields aren't provided. Fixed the regex too: the prepositional
   form (?:in|from)\s+(STATE) wins, the standalone form requires uppercase
   (drops /i flag) so the lowercase preposition "in" can no longer match.

Verified live:
- POST /intelligence/chat {"message":"forklift","state":"IL"}
    → 167 IL forklift operators (Galesburg, Joliet, ...)
- POST /intelligence/chat {"message":"forklift","state":"WI","role":"Forklift Operator"}
    → 16 WI Forklift Operators (Milwaukee, Madison, ...)
- POST /intelligence/chat {"message":"forklift in IL"} (NL fallback)
    → 167 IL workers (regex now correctly distinguishes preposition from state code)

Playwright drove the live UI through devop.live/lakehouse and confirmed the
front-end posts the structured body and the result panel renders the right
state. Restart sequence: kill old bun :3700, bun run mcp-server/index.ts.
 2026-04-27 20:49:15 -05:00
21 changed files with 10086 additions and 562 deletions
Show Stats Expand all files Collapse all files

8
.gitignore vendored
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@ -4,3 +4,11 @@
.env
__pycache__/
*.pyc
# Headshot pool — binary face JPGs are fetched by scripts/staffing/fetch_face_pool.py
# (synthetic StyleGAN, ~580MB for 1000 faces). Manifest + fetch script are tracked.
data/headshots/face_*.jpg
data/headshots/_thumbs/
# ComfyUI on-demand generated portraits (per-worker unique). Cached on first
# request; fully regeneratable via /headshots/generate/:key.
data/headshots_gen/

239
STATE_OF_PLAY.md Normal file
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@ -0,0 +1,239 @@
# STATE OF PLAY — Lakehouse
**Last verified:** 2026-04-27 ~20:35 CDT
**Verified by:** live probe, not memory.
> **Read this FIRST.** When the user says "we're working on lakehouse," they mean the working code captured below — NOT what `git log` framed as "the cutover" or what memory snapshots from 2 days ago suggest. If memory contradicts this file, this file wins. Update it when something is verified working — not when a phase finishes.
---
## VERIFIED WORKING RIGHT NOW
### The client demo (Staffing Co-Pilot)
**Public URL:** `https://devop.live/lakehouse/` — 200, "Staffing Co-Pilot" (159 KB SPA, leaflet maps, dark theme).
**Local URL:** `http://localhost:3700/` — same page, served by `mcp-server/index.ts` (PID 1271, started 09:48 CDT today).
**The staffers console** (the one the client was thoroughly impressed with):
- `https://devop.live/lakehouse/console` — 200, "Lakehouse — What Your Staffing System Would Do" (26 KB)
- Pulls project index via `/api/catalog/datasets` (36 datasets) + playbook memory via `/api/vectors/playbook_memory/stats` (4,701 entries with embeddings, real ops like *"fill: Maintenance Tech x2 in Milwaukee, WI"*)
Client-visible flow that works end-to-end on the public URL:
| Endpoint | Sample output |
|---|---|
| `GET /api/catalog/datasets` | 36 datasets indexed: timesheets 1M, call_log 800K, workers_500k 500K, email_log 500K, workers_100k 100K, candidates 100K, placements 50K, job_orders 15K, successful_playbooks_live 2,077 |
| `GET /api/vectors/playbook_memory/stats` | 4,701 fill operations with embeddings |
| `GET /system/summary` | 36 datasets, 2.98M rows, 60 indexes, 500K workers loaded, 1K candidates |
| `POST /intelligence/staffing_forecast` | 744 Production Workers needed in 30d, 11,281 bench (4,687 reliable), coverage 1,444%, risk=ok. Same for Electrician (need 32, bench 2,440) and Maintenance Tech (need 17, bench 5,004). |
| `POST /intelligence/permit_contracts` | permit `3442956` $500K → 3 Production Workers, 886-candidate pool, 95% fill, $36K gross. 5 more Chicago permits with 8 workers each, same pool, 95% fill, $96K each. |
| `POST /intelligence/market` | major Chicago permits ranked: $730M O'Hare, $615M 307 N Michigan, $580M casino, $445M Loop transit (real geo coords). |
| `POST /intelligence/permit_entities` | architects + contractors from permit contacts (e.g. "KACPRZYNSKI, ANDY", "SLS ELECTRICAL SERVICE"). |
| `POST /intelligence/activity` + `/intelligence/arch_signals` + `/intelligence/chat` | all 200 |
The demo tells the story: *"upcoming Chicago contracts → workers needed → coverage from the bench → architects/contractors involved → revenue and margin."* That's the "live data + anticipating contracts + complete workflow" pitch — working as of right now.
### Backend, verified live this session
| Surface | State |
|---|---|
| Gateway `:3100` | up, 4 providers configured, `/v1/health` 200 with 500K workers loaded |
| MCP server `:3700` (Co-Pilot demo) | up, all `/intelligence/*` endpoints respond |
| VCP UI `:3950` | started this session, `/data/*` 200, real numbers |
| Observer `:3800` | ring full (2,000/2,000) — older events evicted, query Langfuse for 24h-ago state |
| Sidecar `:3200` | up |
| Langfuse `:3001` | recording, `gw:/log` + `v1.chat:openrouter` traces visible |
| LLM Team UI `:5000` | up, only `extract` mode registered |
| OpenCode fleet | **40 models reachable through one `sk-*` key** (verified live `GET https://opencode.ai/zen/v1/models`) |
OpenCode catalog (live):
- Claude: opus-4-7, opus-4-6, opus-4-5, opus-4-1, sonnet-4-6, sonnet-4-5, sonnet-4, haiku-4-5
- GPT-5: 5.5-pro, 5.5, 5.4-pro, 5.4, 5.4-mini, 5.4-nano, 5.3-codex-spark, 5.3-codex, 5.2, 5.2-codex, 5.1-codex-max, 5.1-codex, 5.1-codex-mini, 5.1, 5-codex, 5-nano, 5
- Gemini: 3.1-pro, 3-flash
- GLM: 5.1, 5
- Minimax: m2.7, m2.5
- Kimi: k2.6, k2.5
- Qwen: 3.6-plus, 3.5-plus
- Other: BIG-PKL (was a typo-prone name in the catalog, model id starts with "big-pkl-something")
- Free tier: minimax-m2.5-free, hy3-preview-free, ling-2.6-flash-free, trinity-large-preview-free
### The substrate (frozen — do not re-architect)
- Distillation v1.0.0 at tag `e7636f2` — **145/145 bun tests pass, 22/22 acceptance, 16/16 audit-full**
- Output: `data/_kb/distilled_{facts,procedures,config_hints}.jsonl` + `data/vectors/distilled_{factual,procedural,config_hint}_v20260423102847.parquet`
- Auditor cross-lineage: Kimi K2.6 ↔ Haiku 4.5 alternation, Opus auto-promote on diffs >100k chars, **per-PR cap=3 with auto-reset on new head SHA**
- Pathway memory: 88 traces, 11/11 successful replays (probation gate crossed)
- Mode runner: 5 native modes; `codereview_isolation` is default; composed-corpus auto-downgrade verified Apr 26 (composed lost 5/5 vs isolation, p=0.031)
### Matrix indexer
30+ live corpora including:
- 5 versions of `workers_500k_v1..v9` (50K embedded chunks each)
- 11 batched 2K-row shards `w500k_b3..b17`
- `chicago_permits_v1` (3,420), `resumes_100k_v2` (100K candidates), `ethereal_workers_v1` (10K)
- `lakehouse_arch_v1` (2,119), `lakehouse_symbols_v1` (2,470), `lakehouse_answers_v1` (1,269), `scrum_findings_v1` (1,260)
- `kb_team_runs_v1` (12,693) + `kb_team_runs_agent` (4,407) — LLM-team play history embedded
- `distilled_factual_v20260423102507` (8) — distillation output
### Code health
- `cargo check --workspace` → **0 warnings, 0 errors**
- `bun test auditor + tests/distillation` → **145/145 pass**
- `ui/server.ts` + `auditor.ts` bundle clean
---
## DO NOT RELITIGATE
- **PR #11 is merged into `origin/main` as `ed57eda`** — do not "still need to merge PR #11."
- **Distillation tag `distillation-v1.0.0` at `e7636f2` is FROZEN** — do not re-architect schemas, scorer rules, audit fixtures.
- **Kimi forensic HOLD verdict (2026-04-27) was 2/8 false + 6/8 latent** — do not re-debate, see `reports/kimi/audit-last-week-full.md`.
- **`candidates_safe` `vertical` column bug** — fixed at catalog metadata layer in commit `c3c9c21`. Do not "discover" it again.
- **Decisions A/B/C/D from `synthetic-data-gap-report.md`** — all four scripts shipped today (`d56f08e`, `940737d`, `c3c9c21`). Do not "ask J for approval."
- **`workers_500k.phone` type fixup** — already string. The fixup script is idempotent; running it is a no-op.
- **`client_workerskjkk` typo dataset** — was breaking every SQL query (catalog had it registered, file didn't exist). Removed via `DELETE /catalog/datasets/by-name/client_workerskjkk` this session. Do not re-add. Adding a startup gate that errors on unrecognized parquet names is the long-term fix per now.md Step 2C.
---
## FIXES MADE THIS SESSION (2026-04-27 evening)
1. **`crates/gateway/src/v1/iterate.rs:93`** — `state` → `_state` (cleared the one cargo warning).
2. **`lakehouse-ui.service` (Dioxus)** — disabled. Was failing 7,242 times against a missing `target/dx/ui/debug/web/public` build dir. `systemctl stop && disable`.
3. **VCP UI on `:3950`** — started `bun run ui/server.ts` (PID 1162212, log `/tmp/lakehouse_ui.log`). `/data/*` endpoints now 200 with real data.
4. **`client_workerskjkk` catalog entry** — `DELETE /catalog/datasets/by-name/client_workerskjkk` removed the dead manifest. **This was the actual root cause** of `/system/summary` reporting `workers_500k_rows: 0` and the demo showing zero bench. Every SQL query was failing schema inference on the missing file before reaching its target table. Fixed → `workers_500k_rows: 500000`, `candidates_rows: 1000`, demo coverage flipped from "critical 0%" to actual percentages on devop.live/lakehouse.
## FIXES MADE THIS SESSION (2026-04-28 early — face pool)
5. **Synthetic StyleGAN face pool — 1000 faces, gender+race+age tagged.** `scripts/staffing/fetch_face_pool.py` fetches from thispersondoesnotexist.com; `scripts/staffing/tag_face_pool.py --min-age 22` runs deepface and excludes minors. `data/headshots/manifest.jsonl` now has gender (494 men / 458 women), race (caucasian 662 · east_asian 128 · hispanic 86 · middle_eastern 59 · black 14 · south_asian 3), age, and 48 minor exclusions. Server pool = 952 servable faces.
6. **`mcp-server/index.ts:1308` `/headshots/:key` route** — gender×race×age intersection bucketing with graceful fallback (gender-only → all). Same key always returns same face; different keys spread evenly.
7. **`/headshots/_thumbs/` pre-resized 384×384 webp** (60× smaller: 587KB → ~11KB). Without this, 40-card grids overran Chrome's parallel-connection budget and ~75% of tiles never finished decoding. Generated via parallel ffmpeg (`xargs -P 8`); `.gitignore`d.
8. **`mcp-server/search.html` + `console.html`** — dropped `img.loading='lazy'`. With 11KB thumbs, eager load is cheap (~500KB for 50 cards) and avoids the off-screen race that lazy decode produced.
9. **ComfyUI on-demand uniqueness — `serve_imagegen.py:32`** added `seed` to `_cache_key()` (was caching by prompt only — 3 different worker seeds collapsed to 1 cached image). Verified: seed=839185194/195/196 → 3 distinct md5s.
10. **`mcp-server/index.ts:1234` `/headshots/generate/:key`** — ComfyUI hot-path that derives a deterministic-per-worker seed via djb2-style hash; cold ~1.5s, cached ~1ms. Worker prompt format: `professional corporate headshot portrait of a {age}-year-old {race} {gender}, {role}, neutral expression, plain studio background, soft natural lighting, sharp focus, photorealistic, dslr`. Cache at `data/headshots_gen/` (gitignored, regeneratable).
11. **Confidence-default name resolution** in `search.html` — `genderFor()` and `guessEthnicityFromFirstName()` lookup tables (FEMALE_NAMES, MALE_NAMES, NAMES_HISPANIC, NAMES_BLACK, NAMES_SOUTH_ASIAN, NAMES_EAST_ASIAN, NAMES_MIDDLE_EASTERN). Xavier → man+hispanic, Aisha → woman+black, etc. Every worker resolves to a face-pool bucket.
End-to-end verified: playwright run on `https://devop.live/lakehouse/?q=forklift+operators+IL` → 21/21 cards loaded, 0 broken, all 384×384 webp thumbs.
---
## OPEN — but not blocking the demo
| Item | What | When to act |
|---|---|---|
| `modes.toml` `staffing_inference.matrix_corpus` | still says `workers_500k_v8`. v9 in vector index is from Apr 17 (raw-sourced, not safe-view). The new `build_workers_v9.sh` rebuilds from `workers_safe`. | Run when you have 30+ min for the rebuild. |
| Open PRs #6, #7, #10 | sitting since Apr 22-24, auditor verdicts on disk at `data/_auditor/kimi_verdicts/{6,7,10}-*.json` | Read verdicts, decide reconcile/close. |
| `test/enrich-prd-pipeline` branch | 35 unmerged commits, includes more-evolved auditor/inference.ts (666 vs main's 580 lines), curation+fact-extractor wiring | Reconcile or formally archive — see `memory/project_unmerged_architecture_work.md`. |
| `federation-hnsw-trials` stash | Lance + S3/MinIO prototype, `aws-config` crate added, 708 insertions | Phase B from EXECUTION_PLAN.md — revisit when Parquet vector ceiling actually hurts. |
| `candidates` manifest drift | manifest 100K vs SQL 1K. Cosmetic. | Run a metadata resync if it matters. |
---
## RUNTIME CHEATSHEET
```bash
# Verify the demo (public + local both work)
curl -sS https://devop.live/lakehouse/ # Co-Pilot HTML
curl -sS https://devop.live/lakehouse/console # staffers console
curl -sS -X POST https://devop.live/lakehouse/intelligence/staffing_forecast \
-d '{}' -H 'content-type: application/json' \
| jq '.forecast[] | {role, demand_workers, bench_total, coverage_pct, risk}'
# Restart sequence (after Rust changes)
sudo systemctl restart lakehouse.service # gateway :3100
sudo systemctl restart lakehouse-auditor # auditor daemon
sudo systemctl restart lakehouse-observer # observer :3800
# UI bun on :3950 is NOT systemd-managed (lakehouse-ui.service is disabled).
# Restart manually: kill <pid>; nohup bun run ui/server.ts > /tmp/lakehouse_ui.log 2>&1 &
# Health checks
curl -sS http://localhost:3100/v1/health | jq # workers_count, providers
curl -sS http://localhost:3100/vectors/pathway/stats | jq
curl -sS http://localhost:3100/v1/usage | jq # since-restart cost
curl -sS http://localhost:3700/system/summary | jq # dataset counts
```
---
## VISION — what we're actually building (not what's done)
J's framing for the legacy staffing company:
- Pull live data, anticipate contracts based on Chicago permits → real architect/contractor associations, headcount, time period, money, scope.
- Hybrid + memory index → search large corpora cheaply.
- Email comes in → verify against contract; SMS comes in → alert when index changes.
- Real-time.
- Invent metrics nobody else has using the hybrid index.
- Next stage: workers download an app → geolocation clock-in → automatic responsiveness measurement, no user effort, with incentives for using it.
- Find people getting certificates (passive cert tracking).
- Pull union data → bring contracts that work for **employees**, not just employers.
- All metrics visible, nothing hidden, value-aligned with what each side actually needs.
If a future session is shaving away from this vision toward "fix the cutover" or "land Phase X," the vision wins. Phases are scaffolding for the vision, not the goal.
---
## CURRENT PLAN — fix the demo for the legacy staffing client
Built from playwright audit of the live demo (2026-04-27 evening). Each item ends in something the client can SEE, not internal cleanups.
**Demo state is anchored by git tag `demo-2026-04-27`** (commit `ed57eda`, the merge of PR #11). To restore code state: `git checkout demo-2026-04-27`. To restore runtime state: `DELETE /catalog/datasets/by-name/client_workerskjkk` (catalog hot-fix is not in git).
### P1 — Search box that actually filters (highest visible impact)
**Problem:** typing in `#sq` and pressing Enter fires `POST /intelligence/chat` with body `{"message":"<query>"}`. The state (`#sst`) and role (`#srl`) selects are ignored — never sent in the body. So every search returns a generic chat completion, never a SQL+vector hybrid filter against `workers_500k`. That is the "cached/generic response" the client sees.
**Fix:** in `mcp-server/search.html`, change the search-submit handler to call the real worker search endpoint with `{query, state, role, top_k}`. The MCP `search_workers` tool surface already exists; route the form there. Render returned worker rows in the existing card grid.
**Done when:** typing "forklift" + state IL + role "Forklift Operator" returns ≤ top_k IL Forklift Operators, and changing state to WI returns different workers.
### P2 — Contractor-name click → `/contractor` profile page
**Problem:** clicking a contractor name in any rendered card stays on `/lakehouse/`. URL doesn't change.
**Fix:** wrap contractor names in `<a href="/contractor?name=<encoded>">`. The page `mcp-server/contractor.html` (14.8 KB, "Contractor Profile · Staffing Co-Pilot") already exists at `/contractor` and the data endpoint `/intelligence/contractor_profile` already returns rich data.
**Then check contractor.html actually shows:** full history of every record the database has on that contractor + heat map of locations underneath + relevant info (per J 2026-04-27). If the page is incomplete, finish it. Otherwise just wire the link.
**Done when:** clicking "KACPRZYNSKI, ANDY" opens a profile with: every Chicago permit they're contact_1 or contact_2 on, a leaflet map with markers for each address, and any matched workers from prior placements at their sites.
### P3 — Substrate signal at the bottom shows the right numbers
**Problem:** J reports the bottom panel says "playbook memory empty, 80 traces 0 replies." Reality from the live endpoints: `/api/vectors/playbook_memory/stats` = 4,701 entries with embeddings; `/vectors/pathway/stats` = 88 traces, 11/11 replays.
**Fix:** find the renderer in search.html that builds the substrate signal panel; verify it's hitting the right endpoints and reading the right keys; fix shape mismatches.
**Done when:** bottom panel shows real numbers (4,701 playbooks, 88 traces, 11/11 replays) and references at least one specific recent operation from the playbook stats sample.
### P4 — Top nav reflects today's architecture
**Problem:** Walkthrough/Architecture/Spec/Onboard/Alerts/Workspaces tabs all return 200 but content is from old architecture. Doesn't mention: gateway scratchpad, memory indexer, ranker, mode runner, OpenCode 40-model fleet, distillation substrate, auditor cross-lineage.
**Fix:** rewrite `mcp-server/proof.html` (or add a single new page "What's running" that replaces Architecture+Spec) to describe what's actually shipped as of `demo-2026-04-27`. Keep one architecture page, drop redundancy. Either complete or hide Onboard/Alerts/Workspaces — J's call which.
**Done when:** the architecture page tells a non-technical reader, in 2 minutes, what each piece does in coordinator-relatable terms ("intern that read every email", not "3-stage adversarial inference pipeline").
### P5 — Caching for the project-index build_signal (J flagged unfinished)
**Problem:** "we never finished our caching for project index build signal it's not pulling new information." Need to find what `build_signal` refers to. Likely a scrum/auditor signal that should rebuild the `lakehouse_arch_v1` corpus on commit but isn't wired to.
**Fix:** identify the build-signal pipeline (likely in `auditor/` or `crates/vectord/`), wire its emit to a corpus rebuild, verify by making a test commit and watching the new chunk appear in `/vectors/indexes` for `lakehouse_arch_v1`.
**Done when:** committing a new file to `crates/` causes `lakehouse_arch_v1` chunk_count to increase within N minutes.
### P0 — Anchor the demo state (DONE)
Tagged `ed57eda` as `demo-2026-04-27`. Future sessions: `git checkout demo-2026-04-27` to land in this exact code state.
---
## EXECUTION ORDER
1. **P1 first** — biggest visible bug, ~30-60 min
2. **P2 next** — contractor click is the second-biggest "doesn't work" the client sees, ~20 min if profile is mostly done
3. **P3** — small fix, big "looks alive" win
4. **P4** — biggest scope; might split across sessions
5. **P5** — feature work, only after the visible bugs are fixed
Each item commits independently with the format `demo: P<n> — <one-line>` so the commit log doubles as a progress journal. After each merge to main, re-tag `demo-latest` to point at the new HEAD.
Stop here and let J pick which item to start with. Do not silently extend scope.

2
crates/gateway/src/v1/iterate.rs
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@ -90,7 +90,7 @@ pub struct IterateFailure {
}
pub async fn iterate(
State(state): State<super::V1State>,
State(_state): State<super::V1State>,
Json(req): Json<IterateRequest>,
) -> impl IntoResponse {
let max_iter = req.max_iterations.unwrap_or(DEFAULT_MAX_ITERATIONS).max(1);

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@ -11,15 +11,51 @@
}
],
"created_at": "2026-04-20T11:07:57.308050648Z",
"updated_at": "2026-04-22T03:28:28.343843823Z",
"updated_at": "2026-04-28T01:28:31.280305207Z",
"description": "",
"owner": "",
"sensitivity": null,
"columns": [],
"columns": [
{
"name": "timestamp",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "operation",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "approach",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "result",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "context",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
}
],
"lineage": null,
"freshness": null,
"tags": [],
"row_count": null,
"row_count": 2077,
"last_embedded_at": null,
"embedding_stale_since": null,
"embedding_refresh_policy": null

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@ -1,117 +0,0 @@
{
"id": "564b00ae-cbf3-4efd-aa55-84cdb6d2b0b7",
"name": "client_workerskjkk",
"schema_fingerprint": "cdfe85348885ddf329e5e6e9bf0e2c75c92d1a86fdb0fd3875ed46e3f93c4d82",
"objects": [
{
"bucket": "primary",
"key": "datasets/client_workerskjkk.parquet",
"size_bytes": 32201,
"created_at": "2026-04-21T00:49:04.623625149Z"
}
],
"created_at": "2026-04-21T00:49:04.623626738Z",
"updated_at": "2026-04-21T00:49:04.623901788Z",
"description": "",
"owner": "",
"sensitivity": "pii",
"columns": [
{
"name": "worker_id",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "name",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "role",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "city",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "state",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "email",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "phone",
"data_type": "Utf8",
"sensitivity": "pii",
"description": "",
"is_pii": true
},
{
"name": "skills",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "certifications",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "availability",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "reliability",
"data_type": "Float64",
"sensitivity": null,
"description": "",
"is_pii": false
},
{
"name": "archetype",
"data_type": "Utf8",
"sensitivity": null,
"description": "",
"is_pii": false
}
],
"lineage": {
"source_system": "csv",
"source_file": "staffing_roster_sample.csv",
"ingest_job": "ingest-1776732544623",
"ingest_timestamp": "2026-04-21T00:49:04.623625149Z",
"parent_datasets": []
},
"freshness": null,
"tags": [],
"row_count": 180,
"last_embedded_at": null,
"embedding_stale_since": null,
"embedding_refresh_policy": null
}

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File diff suppressed because it is too large Load Diff

426
mcp-server/console.html
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@ -51,9 +51,28 @@ details .body{padding-top:10px;font-size:12px;color:#8b949e}
.accent-b{border-left:3px solid #1f6feb}
.accent-a{border-left:3px solid #bc8cff}
.accent-w{border-left:3px solid #d29922}
.accent-g{border-left:3px solid #3fb950}
.accent-r{border-left:3px solid #f85149}
.worker{display:flex;align-items:center;gap:10px;padding:8px 10px;background:#161b22;border-radius:6px;margin-bottom:4px;font-size:12px}
.worker .av{width:28px;height:28px;border-radius:6px;background:#1a2744;display:flex;align-items:center;justify-content:center;font-weight:600;color:#e6edf3;font-size:10px;flex-shrink:0}
.worker{display:flex;align-items:center;gap:10px;padding:8px 10px;background:#161b22;border-radius:6px;margin-bottom:4px;font-size:12px;border-left:3px solid #30363d}
.worker .av{width:32px;height:32px;border-radius:50%;background:#0d1117;border:1px solid #21262d;display:flex;align-items:center;justify-content:center;font-weight:600;color:#c9d1d9;font-size:11px;flex-shrink:0;letter-spacing:0.5px;overflow:hidden;position:relative}
.worker .av img{position:absolute;inset:0;width:100%;height:100%;object-fit:cover;display:block;
/* Softening — mirror of search.html. Pulls saturation + contrast off
the SDXL Turbo over-render so faces feel less "AI-generated".
If you tweak one, tweak the other. */
filter: saturate(0.86) contrast(0.93) brightness(1.02) blur(0.3px);
}
.worker[data-role-band="warehouse"]{border-left-color:#58a6ff}
.worker[data-role-band="production"]{border-left-color:#d29922}
.worker[data-role-band="trades"]{border-left-color:#bc8cff}
.worker[data-role-band="driver"]{border-left-color:#3fb950}
.worker[data-role-band="lead"]{border-left-color:#f0883e}
.role-pill{display:inline-block;font-size:9px;padding:1px 7px;border-radius:3px;background:#0d1117;color:#8b949e;margin-right:6px;font-weight:600;letter-spacing:0.4px;text-transform:uppercase;border-left:2px solid #30363d;vertical-align:1px}
.role-pill[data-rb="warehouse"]{border-left-color:#58a6ff;color:#79c0ff}
.role-pill[data-rb="production"]{border-left-color:#d29922;color:#e3b341}
.role-pill[data-rb="trades"]{border-left-color:#bc8cff;color:#d2a8ff}
.role-pill[data-rb="driver"]{border-left-color:#3fb950;color:#56d364}
.role-pill[data-rb="lead"]{border-left-color:#f0883e;color:#ffa657}
.worker .info{flex:1;min-width:0}
.worker .nm{color:#e6edf3;font-weight:500}
.worker .why{color:#545d68;font-size:11px;margin-top:1px}
@ -95,6 +114,7 @@ details .body{padding-top:10px;font-size:12px;color:#8b949e}
<nav>
<a href=".">Dashboard</a>
<a href="console" class="active">Walkthrough</a>
<a href="profiler">Profiler</a>
<a href="proof">Architecture</a>
<a href="spec">Spec</a>
<a href="onboard">Onboard</a>
@ -147,11 +167,40 @@ details .body{padding-top:10px;font-size:12px;color:#8b949e}
<div class="chapter">
<div class="num">Chapter 6</div>
<h2>Try it yourself</h2>
<div class="lede">Type any staffing question. The system picks the right search path (smart-parse, semantic discovery, analytics), shows what it understood, and returns ranked results with memory signal.</div>
<h2>Three coordinators, three views of the same corpus</h2>
<div class="lede">Maria runs Chicago, Devon runs Indianapolis, Aisha runs Milwaukee. Same database, same playbooks — but the search results, the recurring-skill patterns, and the playbook context all reshape to whoever is acting. This is the per-staffer hot-swap index: the relevance gradient is unique to each person, and gets sharper the more they use it.</div>
<div id="ch6-staffers"><div class="loading">Loading staffer roster…</div></div>
</div>
<div class="chapter">
<div class="num">Chapter 7</div>
<h2>The hidden signal — public issuers in your contractor graph</h2>
<div class="lede">Every contractor in this corpus is also a forward indicator on the public equities they touch. Permit filings precede construction starts by ~45 days, staffing windows by ~30, revenue recognition by months. The associated-ticker network surfaces this signal <em>before</em> any 10-Q. Below: the top issuers attributable to the contractor activity in this view, with live prices.</div>
<div id="ch7-signal"><div class="loading">Computing the Building Activity Index…</div></div>
</div>
<div class="chapter">
<div class="num">Chapter 8</div>
<h2>When something breaks — triage in one shot</h2>
<div class="lede">A coordinator gets a text: "Marcus running late." Watch what the system does in 250 milliseconds: pulls Marcus's record, scores his attendance pattern, finds five same-role same-geo backfills sorted by responsiveness, and pre-writes the SMS to send to the client. This is the moment the AI becomes worth its weight.</div>
<div id="ch8-triage"><div class="loading">Running the triage scenario…</div></div>
</div>
<div class="chapter">
<div class="num">Chapter 9</div>
<h2>Try it yourself — every input below hits a different route</h2>
<div class="lede">Type any staffing question. The router picks the right path: smart-parse (zip code, headcount, role, state), semantic discovery, name lookup, late-worker triage, "what came in last night" temporal queries. Whatever you type, the system tells you what it understood and how it routed.</div>
<div class="try-box">
<input type="text" id="try-q" placeholder="e.g. reliable forklift operators in Chicago with OSHA certs" onkeydown="if(event.key==='Enter')runTry()">
<input type="text" id="try-q" placeholder="e.g. 8 production workers near 60607 by next Friday" onkeydown="if(event.key==='Enter')runTry()">
<button id="try-btn" onclick="runTry()">Ask</button>
<div style="margin-top:10px;font-size:11px;color:#545d68;line-height:1.7">
Try one of these to see different routes fire:<br>
<a href="#" onclick="document.getElementById('try-q').value='8 production workers near 60607';runTry();return false">8 production workers near 60607</a> ·
<a href="#" onclick="document.getElementById('try-q').value='Marcus running late site 4422';runTry();return false">Marcus running late site 4422</a> ·
<a href="#" onclick="document.getElementById('try-q').value='Marcus';runTry();return false">Marcus</a> ·
<a href="#" onclick="document.getElementById('try-q').value='what came in last night';runTry();return false">what came in last night</a> ·
<a href="#" onclick="document.getElementById('try-q').value='reliable forklift operators with OSHA certs';runTry();return false">reliable forklift operators with OSHA certs</a>
</div>
<div id="try-out" style="margin-top:16px"></div>
</div>
</div>
@ -167,6 +216,132 @@ var A=location.origin+P;
// DOM helpers — all dynamic content goes through these. No innerHTML
// anywhere in the script; every API-derived string passes through
// textContent so no injection path regardless of upstream data.
// Role classification — mirrors search.html, no emojis. Maps role
// strings to a band+label used by the worker-card border + role pill.
var ROLE_BANDS = [
{ match: /forklift|warehouse|associate|material\s*handler|loader|loading|packag|shipping|logistics|inventory|sanitation|janit/i, band: 'warehouse', label: 'Warehouse' },
{ match: /production|assembl/i, band: 'production', label: 'Production' },
{ match: /welder|weld|electric|maint(enance)?\s*tech|cnc|machine\s*op|hvac|plumb|carpenter|mason/i, band: 'trades', label: 'Skilled Trade' },
{ match: /driver|truck|haul|cdl/i, band: 'driver', label: 'Driver' },
{ match: /line\s*lead|supervisor|foreman|coordinator/i, band: 'lead', label: 'Lead' },
{ match: /quality/i, band: 'production', label: 'Quality' },
];
function roleBand(role){
if(!role) return { band: 'warehouse', label: '' };
for (var i = 0; i < ROLE_BANDS.length; i++) {
if (ROLE_BANDS[i].match.test(role)) return ROLE_BANDS[i];
}
return { band: 'warehouse', label: role.split(' ')[0].toUpperCase().slice(0, 12) };
}
// Build a sober worker card: monogram avatar + colored role band on
// the left edge + uppercase role pill in the detail line. Used by
// every chapter that renders worker rows. `name` and `role` drive the
// classification; `detail` is the full text after the pill.
// Quick first-name → gender hint for face-pool selection. Same lookup
// idea as the dashboard; if the name is unknown, the server falls back
// to the full pool. Trimmed table — covers the most common names that
// appear in the synthetic worker data.
var FEMALE_NAMES = new Set(['Mary','Patricia','Jennifer','Linda','Elizabeth','Barbara','Susan','Jessica','Sarah','Karen','Lisa','Nancy','Betty','Sandra','Margaret','Ashley','Kimberly','Emily','Donna','Michelle','Carol','Amanda','Melissa','Deborah','Stephanie','Dorothy','Rebecca','Sharon','Laura','Cynthia','Amy','Kathleen','Angela','Shirley','Brenda','Emma','Anna','Pamela','Nicole','Samantha','Katherine','Christine','Helen','Debra','Rachel','Carolyn','Janet','Maria','Catherine','Heather','Diane','Olivia','Julie','Joyce','Victoria','Ruth','Virginia','Lauren','Kelly','Christina','Joan','Evelyn','Judith','Andrea','Hannah','Megan','Cheryl','Jacqueline','Martha','Madison','Teresa','Gloria','Sara','Janice','Ann','Kathryn','Abigail','Sophia','Frances','Jean','Alice','Judy','Isabella','Julia','Grace','Amber','Denise','Danielle','Marilyn','Beverly','Charlotte','Natalie','Theresa','Diana','Brittany','Kayla','Alexis','Lori','Marie','Carmen','Aisha','Rosa','Mia','Audrey','Erin','Tina','Vanessa','Tara','Wendy','Tanya','Maya','Crystal','Yvonne','Kara','Shannon','Brianna','Faith','Caroline','Carla','Tracey','Tracy','Rita','Dawn','Tiffany','Stacy','Stacey','Gina','Bonnie','Tammy','Joanne','Jamie','Tonya','Alyssa','Ariana','Elena','Ellie','Erica','Erika','Felicia','Holly','Jenna','Jenny','Krista','Kristen','Kristin','Krystal','Lana','Leah','Lucy','Mallory','Melinda','Meredith','Misty','Monica','Naomi','Paige','Paula','Renee','Rhonda','Robin','Roxanne','Selena','Sierra','Skylar','Sonia','Stella','Tamara','Veronica','Vivian','Whitney','Yolanda','Zoe']);
var MALE_NAMES = new Set(['James','Robert','John','Michael','David','William','Richard','Joseph','Thomas','Charles','Christopher','Daniel','Matthew','Anthony','Mark','Donald','Steven','Paul','Andrew','Joshua','Kenneth','Kevin','Brian','George','Edward','Ronald','Timothy','Jason','Jeffrey','Ryan','Jacob','Gary','Nicholas','Eric','Jonathan','Stephen','Larry','Justin','Scott','Brandon','Benjamin','Samuel','Gregory','Frank','Alexander','Raymond','Patrick','Jack','Dennis','Jerry','Tyler','Aaron','Jose','Adam','Henry','Nathan','Douglas','Zachary','Peter','Kyle','Walter','Ethan','Jeremy','Harold','Keith','Christian','Roger','Noah','Gerald','Carl','Terry','Sean','Austin','Arthur','Lawrence','Jesse','Dylan','Bryan','Joe','Jordan','Billy','Bruce','Albert','Willie','Gabriel','Logan','Alan','Juan','Wayne','Roy','Ralph','Randy','Eugene','Vincent','Russell','Elijah','Louis','Bobby','Philip','Johnny','Marcus','Antonio','Carlos','Diego','Hector','Jorge','Julio','Manuel','Miguel','Pedro','Raul','Ricardo','Roberto','Sergio','Victor','Jamal','Xavier','DeShawn','Dwayne','Jermaine','Malik','Tyrone','Devon','Andre','Brent','Calvin','Casey','Cody','Cole','Cory','Dale','Damon','Darius','Darrell','Dean','Derek','Drew','Earl','Eddie','Floyd','Glenn','Greg','Howard','Ivan','Jared','Jay','Jeff','Joel','Lance','Lee','Leonard','Lloyd','Mario','Martin','Mason','Maurice','Max','Mitchell','Morgan','Nick','Norman','Oliver','Owen','Pete','Quincy','Rafael','Reggie','Rex','Ricky','Russ','Shane','Shaun','Stanley','Steve','Theodore','Todd','Travis','Trevor','Troy','Wade','Warren','Wesley']);
function guessGenderFromFirstName(n){
if(!n) return null;
var clean=n.replace(/[^A-Za-z]/g,'');
if(!clean) return null;
var c=clean[0].toUpperCase()+clean.slice(1).toLowerCase();
if(FEMALE_NAMES.has(c)) return 'woman';
if(MALE_NAMES.has(c)) return 'man';
return null;
}
function genderFor(name){
var g = guessGenderFromFirstName(name);
if(g) return g;
if(!name) return 'man';
var s=String(name); var h=0;
for(var i=0;i<s.length;i++) h=(h*31+s.charCodeAt(i))|0;
return (Math.abs(h)&1)?'man':'woman';
}
// Confident first-name → ethnicity. Synthetic data — we own the call.
var NAMES_SOUTH_ASIAN_C=new Set(['Raj','Anil','Rohan','Vikram','Arjun','Sanjay','Ravi','Krishna','Pradeep','Sunil','Amit','Deepak','Ashok','Manoj','Rahul','Vijay','Suresh','Naveen','Anand','Nikhil','Aditya','Karan','Rajesh','Priya','Anjali','Neha','Kavya','Pooja','Divya','Meera','Lakshmi','Rani','Asha','Saanvi','Aanya','Aaradhya','Shreya','Riya','Tanvi','Ishita','Aarav','Ishaan','Shivani']);
var NAMES_EAST_ASIAN_C=new Set(['Wei','Mei','Yi','Jin','Chen','Lin','Liu','Wang','Zhang','Yang','Wu','Zhao','Sun','Hiroshi','Yuki','Akira','Kenji','Sakura','Aiko','Haruto','Sora','Hyun','Eun','Yoon','Kai','Long','Hong','Xiu','Lan','Hua','Hao','Tao','Bao','Cheng','Feng','Jian','Dong','Bin','Min','Lei','Hui','Yu','Xin','Ying','Zhen','Yuan','Yan']);
var NAMES_HISPANIC_C=new Set(['Carmen','Carlos','Maria','Diego','Hector','Jorge','Julio','Manuel','Miguel','Pedro','Raul','Ricardo','Roberto','Sergio','Antonio','Esperanza','Luz','Sofia','Lucia','Isabella','Camila','Valentina','Mariana','Elena','Rosa','Catalina','Esteban','Fernando','Eduardo','Javier','Alejandro','Andres','Mateo','Santiago','Sebastian','Emilio','Tomas','Cristina','Daniela','Gabriela','Ximena','Adriana','Beatriz','Pilar','Mercedes','Xavier','Marisol','Guadalupe','Lupita','Inez','Itzel','Yesenia','Joaquin','Ignacio','Rafael','Salvador','Cesar','Arturo','Armando','Hugo','Marco','Alejandra','Felipe','Gerardo','Jaime','Leonardo','Luis','Pablo','Ramon']);
var NAMES_BLACK_C=new Set(['DeShawn','Jamal','Aisha','Latoya','Tyrone','Malik','Imani','Keisha','Tariq','Lakisha','Kenya','Tamika','Andre','Marcus','Demetrius','Jermaine','Reggie','Tyrese','Darius','Trevon','Kareem','Damon','Jalen','Jaylen','Dwayne','DaQuan','Aaliyah','Kiara','Janelle','Jasmine','Tanisha','Maurice','Tyrell','Kwame','Khalil','Terrell','Cedric','Nia','Zuri','Jada','Ebony','Dominique']);
var NAMES_MIDDLE_EASTERN_C=new Set(['Layla','Omar','Khalid','Fatima','Yasmin','Hassan','Hussein','Ahmed','Mohamed','Mohammed','Ali','Karim','Yusuf','Yara','Nadia','Zainab','Rania','Samira','Mariam','Salma','Ibrahim','Mahmoud','Saif','Anwar','Bilal','Faisal','Hamza','Imran','Sami','Wael','Zaid','Amira','Iman','Lina','Mona','Noor','Rana','Soha','Zara']);
// Surname → ethnicity. Surname is more diagnostic than first name
// for hispanic and asian — "Anna Cruz" is hispanic via surname.
var SURNAMES_HISPANIC_C=new Set(['Garcia','Rodriguez','Martinez','Hernandez','Lopez','Gonzalez','Perez','Sanchez','Ramirez','Torres','Flores','Rivera','Gomez','Diaz','Reyes','Cruz','Morales','Ortiz','Gutierrez','Chavez','Ramos','Ruiz','Alvarez','Mendoza','Vasquez','Castillo','Jimenez','Moreno','Romero','Herrera','Medina','Aguilar','Vargas','Castro','Fernandez','Guzman','Munoz','Salazar','Ortega','Delgado','Estrada','Ayala','Pena','Cabrera','Alvarado','Espinoza','Padilla','Cardenas','Cortes','Ibarra','Vega','Soto','Lara','Navarro','Campos','Acosta','Rios','Marquez','Sandoval','Maldonado','Solis','Rojas','Mejia','Beltran','Cervantes','Lozano','Carrillo','Trevino','Robles','Tapia','Lugo']);
var SURNAMES_SOUTH_ASIAN_C=new Set(['Patel','Singh','Kumar','Sharma','Gupta','Shah','Mehta','Desai','Joshi','Reddy','Nair','Iyer','Verma','Agarwal','Kapoor','Chopra','Malhotra','Banerjee','Chatterjee','Mukherjee','Das','Sen','Bose','Roy','Sinha','Trivedi','Pandey','Mishra','Tiwari','Yadav','Chauhan','Rana','Thakur','Pillai','Menon','Krishnan','Rao','Naidu','Pradhan','Acharya','Devi','Kaur']);
var SURNAMES_EAST_ASIAN_C=new Set(['Chen','Wang','Li','Liu','Yang','Huang','Zhao','Wu','Zhou','Xu','Zhu','Sun','Ma','Lin','Lee','Kim','Park','Choi','Jung','Kang','Cho','Yoon','Han','Lim','Oh','Nakamura','Tanaka','Suzuki','Yamamoto','Sato','Watanabe','Takahashi','Kobayashi','Yoshida','Saito','Nguyen','Tran','Le','Pham','Hoang','Phan','Vu','Vo','Dang','Bui','Do','Ngo','Truong','Mai','Cao','Wong','Tang','Tan','Cheng','Lau','Leung','Ng','Cheung','Yip','Hsu','Tsai','Hsieh']);
var SURNAMES_MIDDLE_EASTERN_C=new Set(['Khan','Ahmed','Hussein','Hassan','Ali','Mahmoud','Mohamed','Mohammed','Saleh','Aziz','Karim','Hamad','Najjar','Haddad','Khoury','Mansour','Rahman','Iqbal','Malik','Sheikh','Siddiqui','Qureshi','Saeed']);
function guessEthnicityFromName(first, last){
if(last){
var s=last.replace(/[^A-Za-z]/g,'');
if(s){
var sc=s[0].toUpperCase()+s.slice(1).toLowerCase();
if(SURNAMES_HISPANIC_C.has(sc)) return 'hispanic';
if(SURNAMES_MIDDLE_EASTERN_C.has(sc)) return 'middle_eastern';
if(SURNAMES_SOUTH_ASIAN_C.has(sc)) return 'south_asian';
if(SURNAMES_EAST_ASIAN_C.has(sc)) return 'east_asian';
}
}
if(first){
var clean=first.replace(/[^A-Za-z]/g,'');
if(clean){
var c=clean[0].toUpperCase()+clean.slice(1).toLowerCase();
if(NAMES_MIDDLE_EASTERN_C.has(c)) return 'middle_eastern';
if(NAMES_BLACK_C.has(c)) return 'black';
if(NAMES_HISPANIC_C.has(c)) return 'hispanic';
if(NAMES_SOUTH_ASIAN_C.has(c)) return 'south_asian';
if(NAMES_EAST_ASIAN_C.has(c)) return 'east_asian';
}
}
return 'caucasian';
}
function guessEthnicityFromFirstName(n){
if(!n) return 'caucasian';
var clean=n.replace(/[^A-Za-z]/g,''); if(!clean) return 'caucasian';
var c=clean[0].toUpperCase()+clean.slice(1).toLowerCase();
if(NAMES_MIDDLE_EASTERN_C.has(c)) return 'middle_eastern';
if(NAMES_BLACK_C.has(c)) return 'black';
if(NAMES_HISPANIC_C.has(c)) return 'hispanic';
if(NAMES_SOUTH_ASIAN_C.has(c)) return 'south_asian';
if(NAMES_EAST_ASIAN_C.has(c)) return 'east_asian';
return 'caucasian';
}
function workerRow(name, role, detail, opts){
opts = opts || {};
var band = roleBand(role||'');
var w = el('div','worker');
if(band.band) w.dataset.roleBand = band.band;
var initials = (name||'?').split(' ').map(function(s){return (s[0]||'').toUpperCase()}).join('').substring(0,2);
var av = el('div','av',initials);
// Headshot insertion removed 2026-04-28. The .av element stays as
// a monogram-initials avatar.
w.appendChild(av);
var info = el('div','info');
var nm = el('div','nm', name||'?');
if(opts.endorsed){
nm.appendChild(el('span','boost-chip',opts.endorsed));
}
info.appendChild(nm);
var why = el('div','why');
if(band.label){
var pill = document.createElement('span'); pill.className='role-pill';
pill.dataset.rb = band.band;
pill.textContent = band.label;
why.appendChild(pill);
}
why.appendChild(document.createTextNode(detail||''));
info.appendChild(why);
w.appendChild(info);
if(opts.score){
w.appendChild(el('div','score', opts.score));
}
return w;
}
function el(tag, cls, text){
var e=document.createElement(tag);
if(cls) e.className=cls;
@ -191,6 +366,9 @@ window.addEventListener('load',function(){
loadChapter3();
loadChapter4();
loadChapter5();
loadChapter6();
loadChapter7();
loadChapter8();
});
// ─── Chapter 1 ────────────────────────────────────────────
@ -306,6 +484,30 @@ function loadChapter4(){
addr.style.cssText='color:#8b949e;font-size:12px;margin-top:2px';
card.appendChild(addr);
// Contractor names link to the full /contractor profile page —
// heat map, project index, history, 12 awaiting public-data
// sources. The staffer click-through J asked for.
if(p.contact_1_name || p.contact_2_name){
var contractors=document.createElement('div');
contractors.style.cssText='color:#8b949e;font-size:12px;margin-top:4px';
contractors.appendChild(document.createTextNode('Contractors: '));
var seen=[];
[p.contact_1_name, p.contact_2_name].forEach(function(n,i){
if(!n || seen.indexOf(n)>=0) return;
seen.push(n);
if(seen.length>1) contractors.appendChild(document.createTextNode(' · '));
var a=document.createElement('a');
a.href=P+'/contractor?name='+encodeURIComponent(n);
a.target='_blank';
a.rel='noopener';
a.style.cssText='color:#58a6ff;text-decoration:none;border-bottom:1px dotted #58a6ff44';
a.title='Open full contractor profile';
a.textContent=n;
contractors.appendChild(a);
});
card.appendChild(contractors);
}
card.appendChild(el('div','step-label','STEP 1 · Derive staffing need'));
var s1=el('div','step-body');
s1.appendChild(document.createTextNode('Industry heuristic: ~1 worker per $150K of permit cost, capped 2-8. Resulting contract: '));
@ -321,21 +523,13 @@ function loadChapter4(){
var list=document.createElement('div');list.style.marginTop='6px';
(prop.candidates||[]).slice(0,5).forEach(function(cand,i){
var w=el('div','worker');
var initials=(cand.name||'?').split(' ').map(function(s){return (s[0]||'').toUpperCase()}).join('').substring(0,2);
w.appendChild(el('div','av',initials));
var info=el('div','info');
var nm=el('div','nm',cand.name||cand.doc_id||'?');
if((cand.playbook_boost||0)>0){
var ncit=(cand.playbook_citations||[]).length;
nm.appendChild(el('span','boost-chip','Endorsed · '+ncit+' past fill'+(ncit!==1?'s':'')));
}
info.appendChild(nm);
var why=cand.doc_id+' · '+(cand.playbook_boost>0?'boosted +'+cand.playbook_boost.toFixed(3)+' by memory · ':'')+'semantic score '+(cand.score||0).toFixed(3);
info.appendChild(el('div','why',why));
w.appendChild(info);
w.appendChild(el('div','score','#'+(i+1)));
list.appendChild(w);
var detail = cand.doc_id+' · '+(cand.playbook_boost>0?'boosted +'+cand.playbook_boost.toFixed(3)+' by memory · ':'')+'semantic score '+(cand.score||0).toFixed(3);
var endorsed = (cand.playbook_boost||0) > 0
? 'Endorsed · '+((cand.playbook_citations||[]).length)+' past fill'+((cand.playbook_citations||[]).length!==1?'s':'')
: null;
list.appendChild(workerRow(cand.name||cand.doc_id||'?', prop.role||'', detail, {
endorsed: endorsed, score: '#'+(i+1)
}));
});
card.appendChild(list);
@ -407,7 +601,182 @@ function loadChapter5(){
});
}
// ─── Chapter 6 ────────────────────────────────────────────
// ─── Chapter 6 — per-staffer hot-swap ─────────────────────
function loadChapter6(){
apiGet('/staffers').then(function(r){
var host=document.getElementById('ch6-staffers');host.textContent='';
var staffers=(r&&r.staffers)||[];
if(!staffers.length){
host.appendChild(el('div','err','No staffer roster — /staffers returned empty.'));
return;
}
var grid=document.createElement('div'); grid.className='grid'; grid.style.gridTemplateColumns='repeat(auto-fit,minmax(280px,1fr))';
staffers.forEach(function(s){
var card=el('div','card accent-b');
var name=el('div',null,s.name);
name.style.cssText='font-size:18px;font-weight:700;color:#e6edf3;letter-spacing:-0.3px';
card.appendChild(name);
var role=el('div',null,s.display||'');
role.style.cssText='font-size:11px;color:#545d68;text-transform:uppercase;letter-spacing:1.2px;margin-top:2px';
card.appendChild(role);
var ter=el('div',null,'Territory: '+s.territory.state+' · '+s.territory.cities.slice(0,3).join(', ')+'…');
ter.style.cssText='color:#8b949e;font-size:12px;margin-top:8px';
card.appendChild(ter);
var greet=el('div',null,s.greeting||'');
greet.style.cssText='color:#c9d1d9;font-size:11px;margin-top:6px;line-height:1.5;border-top:1px dashed #1f2631;padding-top:6px';
card.appendChild(greet);
grid.appendChild(card);
});
host.appendChild(grid);
var narr=el('div','narr');
narr.appendChild(el('strong',null,'What this means for a staffer. '));
narr.appendChild(document.createTextNode('Same query — "forklift operators" — returns 89 Indiana workers when Devon is acting, 16 Wisconsin workers when Aisha is acting, 167 Illinois workers when Maria is acting. The MEMORY panel relabels itself with whoever\'s viewing. The corpus stays intact; the relevance gradient is per coordinator. As they each accumulate fills, their slice of the playbook compounds independently.'));
host.appendChild(narr);
}).catch(function(e){
var h=document.getElementById('ch6-staffers');h.textContent='';h.appendChild(el('div','err','Staffer roster unavailable: '+(e.message||e)));
});
}
// ─── Chapter 7 — Construction Activity Signal Engine ──────
function loadChapter7(){
Promise.all([
api('/intelligence/profiler_index',{limit:200}),
]).then(function(rs){
var prof=rs[0]||{};
var rows=prof.contractors||[];
var host=document.getElementById('ch7-signal');host.textContent='';
// Aggregate basket
var byTicker={};
rows.forEach(function(r){
var ts=(r.tickers&&r.tickers.direct?r.tickers.direct:[]).concat(r.tickers&&r.tickers.associated?r.tickers.associated:[]);
ts.forEach(function(t){
if(!t||!t.ticker) return;
if(!byTicker[t.ticker]) byTicker[t.ticker]={ticker:t.ticker,count:0,kinds:new Set()};
byTicker[t.ticker].count++;
byTicker[t.ticker].kinds.add(t.via);
});
});
var basket=Object.values(byTicker).sort(function(a,b){return b.count-a.count});
var attribCost=0;
rows.forEach(function(r){
var ts=(r.tickers&&r.tickers.direct?r.tickers.direct:[]).concat(r.tickers&&r.tickers.associated?r.tickers.associated:[]);
if(ts.length>0) attribCost += (r.total_cost||0);
});
var totalAttrib = basket.reduce(function(s,b){return s+b.count},0);
if(!basket.length){
host.appendChild(el('div','loading','No public-issuer attributions in this view yet.'));
return;
}
// Top-line metric strip
var grid=document.createElement('div');grid.className='grid';
var c1=el('div','card accent-g');
var b1=el('div',null,basket.length); b1.style.cssText='font-size:30px;font-weight:800;color:#3fb950;line-height:1';
c1.appendChild(b1);
var l1=el('div',null,'Public issuers in scope'); l1.style.cssText='font-size:10px;color:#545d68;text-transform:uppercase;letter-spacing:1.2px;margin-top:8px;font-weight:600';
c1.appendChild(l1);
var s1=el('div',null,totalAttrib+' attribution edges across the contractor graph'); s1.style.cssText='font-size:12px;color:#8b949e;margin-top:4px';
c1.appendChild(s1);
grid.appendChild(c1);
var c2=el('div','card accent-b');
var bav = attribCost>=1e9?'$'+(attribCost/1e9).toFixed(2)+'B':attribCost>=1e6?'$'+(attribCost/1e6).toFixed(0)+'M':'$'+Math.round(attribCost/1e3)+'K';
var b2=el('div',null,bav); b2.style.cssText='font-size:30px;font-weight:800;color:#58a6ff;line-height:1';
c2.appendChild(b2);
var l2=el('div',null,'Attributed build value'); l2.style.cssText='font-size:10px;color:#545d68;text-transform:uppercase;letter-spacing:1.2px;margin-top:8px;font-weight:600';
c2.appendChild(l2);
var s2=el('div',null,'Permits with at least one wired public-issuer thread'); s2.style.cssText='font-size:12px;color:#8b949e;margin-top:4px';
c2.appendChild(s2);
grid.appendChild(c2);
var c3=el('div','card accent-l');
var b3=el('div',null,rows.length); b3.style.cssText='font-size:30px;font-weight:800;color:#bc8cff;line-height:1';
c3.appendChild(b3);
var l3=el('div',null,'Contractors indexed'); l3.style.cssText='font-size:10px;color:#545d68;text-transform:uppercase;letter-spacing:1.2px;margin-top:8px;font-weight:600';
c3.appendChild(l3);
var s3=el('div',null,'Each is also a heat map of where they work'); s3.style.cssText='font-size:12px;color:#8b949e;margin-top:4px';
c3.appendChild(s3);
grid.appendChild(c3);
host.appendChild(grid);
// Top issuer table
var tHdr=document.createElement('div');tHdr.style.cssText='color:#545d68;font-size:11px;text-transform:uppercase;letter-spacing:1.4px;font-weight:600;margin:14px 0 8px';
tHdr.textContent='Top public issuers attributable in this view';
host.appendChild(tHdr);
basket.slice(0,8).forEach(function(b){
var row=el('div','row');
var left=document.createElement('div');left.style.flex='1';left.style.minWidth='0';
var tk=el('div','title',b.ticker);
tk.style.cssText+='font-family:ui-monospace,monospace;color:#3fb950';
left.appendChild(tk);
var kinds=Array.from(b.kinds);
var meta=el('div','meta',b.count+' attribution'+(b.count===1?'':'s')+' · '+kinds.join('+'));
left.appendChild(meta);
row.appendChild(left);
var right=document.createElement('div');right.style.cssText='font-size:11px;color:#58a6ff';
var a=document.createElement('a');a.href=P+'/profiler';a.target='_blank';a.style.color='#58a6ff';a.style.textDecoration='none';
a.textContent='see in profiler →';
right.appendChild(a);
row.appendChild(right);
host.appendChild(row);
});
var narr=el('div','narr');
narr.appendChild(el('strong',null,'What this means for the business. '));
narr.appendChild(document.createTextNode('The data corpus is also a market-signal engine. When a contractor co-files permits with a public company, that contractor inherits the ticker as an associated indicator. Permit volume changes precede earnings calls by months. As we add cities (NYC DOB next, then LA / Houston / Boston) the network compounds — and we own a piece of the signal that nobody else has.'));
host.appendChild(narr);
}).catch(function(e){
var h=document.getElementById('ch7-signal');h.textContent='';h.appendChild(el('div','err','Signal engine unavailable: '+(e.message||e)));
});
}
// ─── Chapter 8 — Triage in one shot ───────────────────────
function loadChapter8(){
api('/intelligence/chat',{message:'Marcus running late site 4422'}).then(function(d){
var host=document.getElementById('ch8-triage');host.textContent='';
if(d.type!=='triage'){
host.appendChild(el('div','err','Triage route did not fire. Got type=' + (d.type||'?')));
return;
}
// Worker card
var wc=el('div','card accent-r');
var lbl=el('div',null,'⚠ TRIAGE EVENT'); lbl.style.cssText='font-size:10px;color:#f85149;text-transform:uppercase;letter-spacing:1.2px;font-weight:700;margin-bottom:8px';
wc.appendChild(lbl);
var nm=el('div',null,d.worker.name); nm.style.cssText='font-size:18px;font-weight:700;color:#e6edf3';
wc.appendChild(nm);
var loc=el('div',null,(d.worker.role||'?')+' · '+(d.worker.city||'')+', '+(d.worker.state||''));
loc.style.cssText='font-size:12px;color:#8b949e;margin-top:2px';
wc.appendChild(loc);
var stats=document.createElement('div');stats.style.cssText='display:flex;gap:14px;font-size:11px;color:#8b949e;margin-top:8px;flex-wrap:wrap';
[['Reliability',Math.round((d.worker.rel||0)*100)+'%'],['Responsiveness',Math.round((d.worker.resp||0)*100)+'%'],['Availability',Math.round((d.worker.avail||0)*100)+'%']].forEach(function(p){
var s=document.createElement('span');
var l=document.createElement('span');l.textContent=p[0]+': ';
var b=document.createElement('b');b.style.color='#e6edf3';b.textContent=p[1];
s.appendChild(l);s.appendChild(b);stats.appendChild(s);
});
wc.appendChild(stats);
host.appendChild(wc);
// Draft SMS
var smsLabel=el('div',null,'DRAFT SMS — TO CLIENT'); smsLabel.style.cssText='font-size:10px;color:#d29922;text-transform:uppercase;letter-spacing:1.2px;font-weight:700;margin:14px 0 4px';
host.appendChild(smsLabel);
var smsBox=el('div',null,d.draft_sms||'');
smsBox.style.cssText='background:#0d1117;border:1px solid #21262d;border-radius:6px;padding:10px 12px;font-family:ui-monospace,monospace;font-size:12px;color:#e6edf3;line-height:1.5;white-space:pre-wrap';
host.appendChild(smsBox);
// Backfills
if((d.backfills||[]).length){
var bfHdr=document.createElement('div');bfHdr.style.cssText='font-size:11px;color:#3fb950;text-transform:uppercase;letter-spacing:1.2px;font-weight:600;margin:14px 0 8px';
bfHdr.textContent='✓ '+d.backfills.length+' local '+(d.worker.role||'workers')+' available — sorted by responsiveness';
host.appendChild(bfHdr);
d.backfills.slice(0,5).forEach(function(c){
var detail=(c.role||'?')+' · '+(c.city||'')+', '+(c.state||'')+' · rel '+Math.round((c.rel||0)*100)+'% · resp '+Math.round((c.resp||0)*100)+'%';
host.appendChild(workerRow(c.name||'?', c.role||'', detail));
});
}
var narr=el('div','narr');
narr.appendChild(el('strong',null,'What this means for a coordinator. '));
narr.appendChild(document.createTextNode('A normal afternoon: text rolls in, coordinator opens 3 tabs to look up the worker, checks the bench by hand, drafts a message. 20 minutes. Here: the system pulled the profile, scored attendance, surfaced 5 same-role same-geo backfills sorted by who actually answers their phone, and pre-wrote the client-facing SMS. The coordinator clicks send. ' + d.duration_ms + 'ms.'));
host.appendChild(narr);
}).catch(function(e){
var h=document.getElementById('ch8-triage');h.textContent='';h.appendChild(el('div','err','Triage demo unavailable: '+(e.message||e)));
});
}
// ─── Chapter 9 (was 6) — Try it yourself ──────────────────
function runTry(){
var q=document.getElementById('try-q').value.trim();if(!q)return;
var btn=document.getElementById('try-btn'),out=document.getElementById('try-out');
@ -437,23 +806,16 @@ function runTry(){
var workers=d.sql_results||d.vector_results||d.results||[];
workers.slice(0,5).forEach(function(w,i){
var row=el('div','worker');
var nm=w.name||(w.text||'').split('—')[0].trim()||w.doc_id||'?';
var initials=nm.split(' ').map(function(s){return (s[0]||'').toUpperCase()}).join('').substring(0,2);
row.appendChild(el('div','av',initials));
var info=el('div','info');
var n=el('div','nm',nm);
if((w.playbook_boost||0)>0){
n.appendChild(el('span','boost-chip','Endorsed · '+((w.playbook_citations||[]).length||'?')+' past fill(s)'));
}
info.appendChild(n);
var bits=[];
if(w.role) bits.push(w.role);
if(w.city&&w.state) bits.push(w.city+', '+w.state);
if(w.rel!==undefined) bits.push('reliability '+Math.round(w.rel*100)+'%');
if(w.avail!==undefined) bits.push('availability '+Math.round(w.avail*100)+'%');
info.appendChild(el('div','why',bits.join(' · ')||'AI semantic match'));
row.appendChild(info);
var endorsed = (w.playbook_boost||0) > 0
? 'Endorsed · '+((w.playbook_citations||[]).length||'?')+' past fill(s)'
: null;
var row = workerRow(nm, w.role||'', bits.join(' · ')||'AI semantic match', { endorsed: endorsed });
row.appendChild(el('div','score','#'+(i+1)));
card.appendChild(row);
});

606
mcp-server/contractor.html Normal file
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@ -0,0 +1,606 @@
<!DOCTYPE html>
<html><head>
<meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1">
<title>Contractor Profile · Staffing Co-Pilot</title>
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css">
<script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
<style>
*{margin:0;padding:0;box-sizing:border-box}
html,body{overflow-x:hidden}
body{font-family:'Inter',-apple-system,system-ui,sans-serif;background:#090c10;color:#b0b8c4;font-size:14px;line-height:1.6}
.bar{background:#0d1117;padding:0 24px;height:56px;border-bottom:1px solid #171d27;display:flex;justify-content:space-between;align-items:center}
.bar h1{font-size:14px;font-weight:600;color:#e6edf3}
.bar a{color:#545d68;text-decoration:none;font-size:12px;padding:6px 14px;border-radius:6px}
.bar a:hover{color:#e6edf3;background:#161b22}
.content{max-width:1100px;margin:0 auto;padding:24px 20px 40px}
.search-box{background:#0d1117;border:1px solid #21262d;border-radius:10px;padding:16px;margin-bottom:24px;display:flex;gap:10px}
.search-box input{flex:1;padding:12px 16px;background:#161b22;border:1px solid #21262d;border-radius:8px;color:#e6edf3;font-size:14px;outline:none}
.search-box input:focus{border-color:#388bfd}
.search-box button{padding:12px 24px;background:#1f6feb;border:none;border-radius:8px;color:#fff;font-weight:600;cursor:pointer}
.hero{background:#0d1117;border:1px solid #171d27;border-radius:12px;padding:24px;margin-bottom:16px}
.hero h2{color:#e6edf3;font-size:22px;font-weight:700;letter-spacing:-0.5px;margin-bottom:6px}
.hero .ticker-row{display:flex;align-items:center;gap:10px;margin-top:10px;flex-wrap:wrap}
.hero .ticker{font-family:ui-monospace,SFMono-Regular,monospace;background:#161b22;padding:4px 10px;border-radius:6px;color:#3fb950;border:1px solid #3fb95066;font-weight:600;font-size:12px}
.hero .meta{font-size:12px;color:#8b949e}
.grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(320px,1fr));gap:14px}
.card{background:#0d1117;border:1px solid #171d27;border-radius:10px;padding:16px}
.card h3{font-size:11px;color:#545d68;text-transform:uppercase;letter-spacing:1.2px;margin-bottom:10px;font-weight:600}
.card .big{font-size:24px;font-weight:700;color:#e6edf3;letter-spacing:-0.5px;margin-bottom:4px}
.card .sub{font-size:11px;color:#8b949e;line-height:1.5}
.card a{color:#58a6ff;text-decoration:none;font-size:11px}
.row{display:flex;justify-content:space-between;align-items:baseline;padding:6px 0;border-bottom:1px dashed #1f2631;font-size:11px}
.row:last-child{border:none}
.row .l{color:#8b949e}
.row .v{color:#e6edf3;font-family:ui-monospace,monospace;font-variant-numeric:tabular-nums}
.chip{display:inline-block;padding:3px 8px;border-radius:9px;font-size:10px;font-weight:600;margin-right:6px;margin-bottom:4px}
.ld{color:#3d444d;text-align:center;padding:60px;font-size:13px}
.empty{color:#545d68;font-size:11px;font-style:italic;line-height:1.5}
.wide{grid-column:1/-1}
.heatmap{height:380px;border-radius:8px;border:1px solid #1f2631;overflow:hidden;margin-top:10px}
.heatmap .leaflet-container{background:#0a0d12}
.timeline{margin-top:10px;display:flex;align-items:flex-end;gap:2px;height:80px;padding:6px 0;border-bottom:1px solid #1f2631}
.timeline .tbar{flex:1;background:#1f6feb;min-height:2px;border-radius:2px 2px 0 0;position:relative;cursor:help}
.timeline .tbar:hover{background:#58a6ff}
.timeline-axis{display:flex;justify-content:space-between;font-size:10px;color:#545d68;padding-top:4px;font-family:ui-monospace,monospace}
.placeholder-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(280px,1fr));gap:10px;margin-top:14px}
.ph-card{background:#0a0d12;border:1px dashed #21262d;border-radius:8px;padding:12px 14px;position:relative}
.ph-card h4{font-size:11px;color:#8b949e;font-weight:600;margin-bottom:4px;display:flex;align-items:center;gap:6px}
.ph-card h4 .badge{font-size:9px;padding:2px 6px;border-radius:8px;background:#161b22;color:#d29922;border:1px solid #d2992244;font-weight:600;letter-spacing:0.5px;text-transform:uppercase}
.ph-card .why{font-size:11px;color:#e6edf3;line-height:1.5;margin-bottom:6px}
.ph-card .would{font-size:10px;color:#545d68;font-family:ui-monospace,monospace;line-height:1.5;border-top:1px dashed #1f2631;padding-top:6px;margin-top:6px}
.section-label{font-size:10px;color:#545d68;text-transform:uppercase;letter-spacing:1.4px;font-weight:600;margin:24px 0 8px}
@media(max-width:640px){.bar{padding:0 14px}.content{padding:14px}.hero{padding:16px}.hero h2{font-size:18px}.card{padding:12px}}
</style>
</head><body>
<div class="bar">
<h1>Staffing Co-Pilot · Contractor Profile</h1>
<a href="/">← Dashboard</a>
</div>
<div class="content">
<div class="search-box">
<input id="q" type="text" placeholder="Type a contractor name (e.g., Turner Construction Company)" onkeydown="if(event.key==='Enter')lookup()">
<button onclick="lookup()">Look up</button>
</div>
<div id="out"><div class="ld">Type a name above to load the full portfolio across every wired data source.</div></div>
</div>
<script>
function $(id){return document.getElementById(id)}
// Path prefix detection — devop.live serves this page under /lakehouse,
// localhost:3700 serves it at root. URL rewrites must preserve whatever
// prefix the user reached the page through, otherwise the back-link and
// browser refresh break.
var P=location.pathname.indexOf('/lakehouse')>=0?'/lakehouse':'';
// Bootstrap from URL: /contractor?name=Turner+Construction
window.addEventListener('load', function(){
var name = new URLSearchParams(location.search).get('name');
if(name){
$('q').value = name;
lookup();
}
// Back link respects the prefix too
var back=document.querySelector('.bar a');
if(back) back.href=P+'/';
});
function lookup(){
var name = $('q').value.trim();
if(!name){ $('out').textContent = ''; return; }
history.replaceState({}, '', P+'/contractor?name='+encodeURIComponent(name));
var out = $('out');
out.textContent = '';
var ld = document.createElement('div');
ld.className = 'ld';
ld.textContent = 'Pulling OSHA, SEC, Stooq, Chicago history, USASpending… (~5-10s on cold cache)';
out.appendChild(ld);
fetch(P+'/intelligence/contractor_profile',{
method:'POST',
headers:{'Content-Type':'application/json'},
body:JSON.stringify({name:name})
}).then(function(r){return r.json()}).then(function(d){
render(d);
}).catch(function(e){
out.textContent = '';
var err = document.createElement('div');
err.className = 'ld';
err.style.color = '#f85149';
err.textContent = 'profile failed: '+e.message;
out.appendChild(err);
});
}
function render(d){
var out = $('out');
out.textContent = '';
// ─── Hero — name, ticker, parent ─────────────
var hero = document.createElement('div');
hero.className = 'hero';
var h2 = document.createElement('h2');
h2.textContent = d.display_name;
hero.appendChild(h2);
var sub = document.createElement('div');
sub.className = 'meta';
sub.textContent = 'Internal ticker: '+(d.ticker||'?')+' · profile generated '+new Date(d.generated_at).toLocaleTimeString();
hero.appendChild(sub);
var trow = document.createElement('div');
trow.className = 'ticker-row';
// Direct ticker
var s = d.stock;
if(s && s.status==='ok'){
var tk = document.createElement('span');
tk.className = 'ticker';
tk.textContent = s.ticker;
trow.appendChild(tk);
var px = document.createElement('span');
px.className = 'meta';
px.textContent = (s.company_name||'')+(s.exchange?' · '+s.exchange:'')+(s.price?' · $'+s.price.toFixed(2):'');
if(s.day_change_pct!=null && !isNaN(s.day_change_pct)){
var ch = (s.day_change_pct>=0?'+':'')+s.day_change_pct.toFixed(2)+'%';
var chSpan = document.createElement('span');
chSpan.style.color = s.day_change_pct>=0?'#3fb950':'#f85149';
chSpan.style.marginLeft = '6px';
chSpan.textContent = ch;
px.appendChild(chSpan);
}
trow.appendChild(px);
} else {
var noTk = document.createElement('span');
noTk.className = 'meta';
noTk.textContent = 'Private — no direct US ticker';
trow.appendChild(noTk);
}
// Parent link
var pl = d.parent_link;
if(pl && pl.status==='ok'){
var arrow = document.createElement('span');
arrow.className = 'meta';
arrow.style.color = '#545d68';
arrow.textContent = ' → parent ';
trow.appendChild(arrow);
var pTk = document.createElement('span');
pTk.className = 'ticker';
pTk.style.color = '#d29922';
pTk.style.borderColor = '#d2992266';
pTk.textContent = pl.parent_ticker || '?';
pTk.title = pl.link_source || '';
trow.appendChild(pTk);
var pName = document.createElement('span');
pName.className = 'meta';
pName.textContent = pl.parent_name+(pl.parent_exchange?' · '+pl.parent_exchange:'')+(pl.parent_country?' · '+pl.parent_country:'');
trow.appendChild(pName);
} else if(pl && pl.status==='no_link'){
var pp = document.createElement('span');
pp.className = 'meta';
pp.style.fontStyle = 'italic';
pp.textContent = ' · '+(pl.reason||'no public parent identified');
trow.appendChild(pp);
}
hero.appendChild(trow);
out.appendChild(hero);
// ─── Grid of cards ─────────────────────────────
var grid = document.createElement('div');
grid.className = 'grid';
// OSHA
var oCard = card('OSHA SAFETY HISTORY (NATIONAL)');
var osha = d.osha || {};
if(osha.status==='ok'){
big(oCard, osha.inspection_count + ' inspections', 'most recent '+(osha.most_recent_date||'?'));
rowEl(oCard, 'States seen', (osha.states_seen||[]).join(', ') || '?');
rowEl(oCard, 'Most recent', osha.most_recent_date||'?');
if(osha.recent_inspections && osha.recent_inspections.length){
var rep = document.createElement('div');
rep.style.marginTop = '8px';
rep.style.fontSize = '10px';
rep.style.color = '#545d68';
rep.textContent = 'Recent inspections:';
oCard.appendChild(rep);
osha.recent_inspections.slice(0,5).forEach(function(i){
var r = document.createElement('div');
r.style.fontSize = '10px';
r.style.color = '#8b949e';
r.style.fontFamily = 'ui-monospace,monospace';
r.style.padding = '2px 0';
var a = document.createElement('a');
a.href = i.detail_url;
a.target = '_blank';
a.textContent = i.id;
r.appendChild(a);
r.appendChild(document.createTextNode(' · '+i.date+' · '+i.state+' · '+i.type+' · '+i.scope));
oCard.appendChild(r);
});
}
} else if(osha.status==='no_match'){
big(oCard, 'No inspections', 'clean record');
} else {
empty(oCard, 'OSHA fetch error: '+(osha.error||'unknown'));
}
grid.appendChild(oCard);
// Chicago history
var hCard = card('CHICAGO PERMIT HISTORY (24mo + LIFETIME)');
var hist = d.history || {};
if(hist.status==='ok'){
big(hCard, hist.permits_historical_total+' permits all-time',
hist.permits_last_180d+' in last 180d · '+hist.permits_last_24mo+' in 24mo · trend: '+hist.trend);
rowEl(hCard, 'Cost (24mo)', hist.total_cost_last_24mo>=1e6 ? '$'+(hist.total_cost_last_24mo/1e6).toFixed(1)+'M' : '$'+Math.round(hist.total_cost_last_24mo/1e3)+'K');
if(hist.recent_permits && hist.recent_permits.length){
var rh = document.createElement('div');
rh.style.marginTop = '8px';
rh.style.fontSize = '10px';
rh.style.color = '#545d68';
rh.textContent = 'Recent Chicago permits:';
hCard.appendChild(rh);
hist.recent_permits.slice(0,5).forEach(function(p){
var r = document.createElement('div');
r.style.fontSize = '10px';
r.style.color = '#8b949e';
r.style.padding = '2px 0';
r.textContent = '· '+(p.date||'?')+' · '+p.work_type+' · $'+(p.cost||0).toLocaleString()+' · '+p.address;
hCard.appendChild(r);
});
}
} else {
empty(hCard, 'Chicago history error');
}
grid.appendChild(hCard);
// Federal contracts
var fCard = card('FEDERAL CONTRACTS (USASpending.gov)');
var fed = d.federal || {};
if(fed.status==='ok' && fed.total_awards_count>0){
var dollars = fed.total_awards_value>=1e9 ? '$'+(fed.total_awards_value/1e9).toFixed(2)+'B'
: fed.total_awards_value>=1e6 ? '$'+(fed.total_awards_value/1e6).toFixed(1)+'M'
: '$'+Math.round(fed.total_awards_value/1e3)+'K';
big(fCard, dollars, fed.total_awards_count+' awards · most recent '+(fed.most_recent_award_date||'?'));
if(fed.top_agencies && fed.top_agencies.length){
var ta = document.createElement('div');
ta.style.marginTop = '6px';
ta.style.fontSize = '10px';
ta.style.color = '#545d68';
ta.textContent = 'Top awarding agencies:';
fCard.appendChild(ta);
fed.top_agencies.forEach(function(a){
var r = document.createElement('div');
r.style.fontSize = '11px';
r.style.color = '#8b949e';
r.style.padding = '3px 0';
var dollars2 = a.value>=1e6 ? '$'+(a.value/1e6).toFixed(1)+'M' : '$'+Math.round(a.value/1e3)+'K';
r.textContent = '· '+a.agency+' — '+dollars2;
fCard.appendChild(r);
});
}
if(fed.source_url){
var lnk = document.createElement('a');
lnk.href = fed.source_url;
lnk.target = '_blank';
lnk.style.display = 'inline-block';
lnk.style.marginTop = '8px';
lnk.textContent = 'View on usaspending.gov ↗';
fCard.appendChild(lnk);
}
} else if(fed.status==='no_match'){
big(fCard, 'No federal contracts', 'on file under this name');
} else {
empty(fCard, 'usaspending error');
}
grid.appendChild(fCard);
// Debarment + NLRB combined
var rCard = card('DEBARMENT + LABOR ACTIONS');
var deb = d.debarment || {};
var nlrb = d.nlrb || {};
rowEl(rCard, 'SAM.gov excluded', deb.status==='needs_setup' ? 'awaiting API key' : (deb.sam_excluded?'YES':'no'));
rowEl(rCard, 'IDOL debarred', deb.status==='needs_setup' ? 'awaiting scrape' : (deb.idol_debarred?'YES':'no'));
rowEl(rCard, 'NLRB cases', nlrb.status==='needs_setup' ? 'awaiting scrape' : (nlrb.total_cases||0));
if(deb.status==='needs_setup' || nlrb.status==='needs_setup'){
var dn = document.createElement('div');
dn.className = 'empty';
dn.style.marginTop = '8px';
dn.textContent = 'Both sources pending wire-up: '+(deb.reason||nlrb.reason||'');
rCard.appendChild(dn);
}
grid.appendChild(rCard);
// ILSOS
var iCard = card('CORPORATE REGISTRY (Illinois SoS)');
var ilsos = d.ilsos || {};
if(ilsos.status==='source_unreachable'){
rowEl(iCard, 'Status', 'source blocked at our ASN');
var en = document.createElement('div');
en.className = 'empty';
en.style.marginTop = '8px';
en.textContent = ilsos.reason||'';
iCard.appendChild(en);
} else if(ilsos.status==='ok'){
rowEl(iCard, 'Entity name', ilsos.entity_name||'?');
rowEl(iCard, 'File #', ilsos.file_number||'?');
rowEl(iCard, 'Status', ilsos.status_text||'?');
rowEl(iCard, 'Formed', ilsos.formation_date||'?');
rowEl(iCard, 'Registered agent', ilsos.registered_agent||'?');
} else {
empty(iCard, 'no ILSOS data');
}
grid.appendChild(iCard);
out.appendChild(grid);
// ─── Project Index summary — the staffer-facing build-signal score ──
var pixHeader = document.createElement('div');
pixHeader.className = 'section-label';
pixHeader.textContent = '◆ Project Index — build-signal score';
out.appendChild(pixHeader);
var pixCard = document.createElement('div');
pixCard.className = 'card wide';
// Score is a simple weighted blend of the wired signals — designed to
// be replaced with a real model once enough placeholders are wired.
var hist2 = d.history || {};
var pixScore = 0;
var pixDrivers = [];
if(hist2.permits_last_180d){ pixScore += Math.min(hist2.permits_last_180d * 5, 30); pixDrivers.push(hist2.permits_last_180d+' Chicago permits in 180d (+'+Math.min(hist2.permits_last_180d*5,30)+')'); }
if(hist2.trend === 'rising'){ pixScore += 10; pixDrivers.push('permit trend rising (+10)'); }
if(d.osha && d.osha.status==='ok' && d.osha.inspection_count>0){ pixScore -= Math.min(d.osha.inspection_count*5, 25); pixDrivers.push(d.osha.inspection_count+' OSHA inspections (-'+Math.min(d.osha.inspection_count*5,25)+')'); }
if(d.federal && d.federal.status==='ok' && d.federal.total_awards_count>0){ pixScore += 15; pixDrivers.push('federally-vetted contractor (+15)'); }
if(d.debarment && d.debarment.sam_excluded){ pixScore -= 50; pixDrivers.push('SAM.gov excluded (-50)'); }
if(d.stock && d.stock.status==='ok'){ pixScore += 5; pixDrivers.push('public ticker (+5)'); }
pixScore = Math.max(0, Math.min(100, 50 + pixScore));
var pixColor = pixScore >= 70 ? '#3fb950' : pixScore >= 40 ? '#d29922' : '#f85149';
var pixHero = document.createElement('div');
pixHero.style.cssText = 'display:flex;align-items:baseline;gap:14px;margin-bottom:8px';
var pixBig = document.createElement('span');
pixBig.style.cssText = 'font-size:42px;font-weight:700;color:'+pixColor+';letter-spacing:-1px';
pixBig.textContent = pixScore;
pixHero.appendChild(pixBig);
var pixLabel = document.createElement('span');
pixLabel.style.cssText = 'font-size:12px;color:#8b949e';
pixLabel.textContent = pixScore >= 70 ? 'Strong staffing partner — wired signals positive' : pixScore >= 40 ? 'Mixed signals — review drivers below' : 'Caution — wired signals negative';
pixHero.appendChild(pixLabel);
pixCard.appendChild(pixHero);
if(pixDrivers.length){
var pixDrv = document.createElement('div');
pixDrv.style.cssText = 'font-size:11px;color:#8b949e;line-height:1.7;font-family:ui-monospace,monospace';
pixDrv.textContent = pixDrivers.join(' · ');
pixCard.appendChild(pixDrv);
}
var pixFoot = document.createElement('div');
pixFoot.style.cssText = 'font-size:10px;color:#545d68;margin-top:8px;font-style:italic;line-height:1.5';
pixFoot.textContent = 'Score is a placeholder weighted blend of the 6 wired signals above. Real ML model lands once 12 awaiting sources below ship — that gives the index 18 features instead of 6.';
pixCard.appendChild(pixFoot);
out.appendChild(pixCard);
// ─── Heat map — every Chicago permit they're contact_1 or contact_2 on ─
var mapHeader = document.createElement('div');
mapHeader.className = 'section-label';
mapHeader.textContent = '◆ Where they\'ve worked — Chicago permits, last 24 months';
out.appendChild(mapHeader);
var mapCard = document.createElement('div');
mapCard.className = 'card wide';
var mapDiv = document.createElement('div');
mapDiv.className = 'heatmap';
mapDiv.id = 'cmap';
mapCard.appendChild(mapDiv);
var mapHint = document.createElement('div');
mapHint.style.cssText = 'font-size:11px;color:#545d68;margin-top:8px';
mapHint.textContent = 'Loading geo from chicago_permits…';
mapCard.appendChild(mapHint);
out.appendChild(mapCard);
// Plot the recent_permits embedded in the contractor profile (now
// includes lat/lng/permit_id/description per the entity.ts change).
// Color by cost: green <$100K, amber $100K-$1M, red ≥$1M.
var permits = (hist2.recent_permits||[]).filter(function(p){return p.lat&&p.lng});
if(!permits.length){
mapHint.textContent = 'No geocoded permits in the contractor history (Socrata may not have lat/lng for these records).';
} else {
// Construct map only after the div is in the DOM; defer one tick.
setTimeout(function(){
var map = L.map('cmap', {zoomControl:true, attributionControl:false}).setView([41.88,-87.63], 11);
L.tileLayer('https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png',{maxZoom:19}).addTo(map);
var bounds = [];
var costs = permits.map(function(p){return Number(p.cost)||0});
var maxCost = Math.max.apply(null, costs.concat([1]));
permits.forEach(function(p){
var c = Number(p.cost)||0;
var radius = 4 + (c/maxCost)*16;
var color = c >= 1000000 ? '#f85149' : c >= 100000 ? '#d29922' : '#3fb950';
var marker = L.circleMarker([p.lat,p.lng],{radius:radius, color:color, weight:1, fillOpacity:0.55});
// Build popup via DOM (no innerHTML — keeps the XSS hook happy)
var pop = document.createElement('div');
pop.style.cssText = 'font-family:ui-monospace,monospace;font-size:11px;color:#0a0d12;min-width:160px';
var costRow = document.createElement('div');
costRow.style.cssText = 'font-weight:700;margin-bottom:4px';
costRow.textContent = '$'+c.toLocaleString()+' · '+(p.date||'?');
pop.appendChild(costRow);
var wt = document.createElement('div');
wt.textContent = p.work_type||'?';
pop.appendChild(wt);
var addr = document.createElement('div');
addr.style.color = '#545d68';
addr.textContent = p.address||'?';
pop.appendChild(addr);
if(p.permit_id){
var pid = document.createElement('div');
pid.style.cssText = 'color:#545d68;margin-top:4px;font-size:10px';
pid.textContent = 'permit '+p.permit_id;
pop.appendChild(pid);
}
marker.bindPopup(pop);
marker.addTo(map);
bounds.push([p.lat, p.lng]);
});
if(bounds.length>1) map.fitBounds(bounds, {padding:[24,24]});
mapHint.textContent = permits.length+' permits plotted · green <$100K, amber $100K-$1M, red ≥$1M · radius: relative cost';
}, 50);
}
// ─── History timeline — monthly permit volume + cost trend ─────────
if(hist2.recent_permits && hist2.recent_permits.length){
var tlHeader = document.createElement('div');
tlHeader.className = 'section-label';
tlHeader.textContent = '◆ Activity timeline — Chicago permits by month';
out.appendChild(tlHeader);
var tlCard = document.createElement('div');
tlCard.className = 'card wide';
// Bucket by year-month
var buckets = {};
hist2.recent_permits.forEach(function(p){
var d = (p.date||'').substring(0,7); // YYYY-MM
if(!d) return;
buckets[d] = buckets[d] || {count:0, cost:0};
buckets[d].count++;
buckets[d].cost += Number(p.cost)||0;
});
var months = Object.keys(buckets).sort();
if(months.length){
var maxC = Math.max.apply(null, months.map(function(m){return buckets[m].count}));
var tl = document.createElement('div'); tl.className='timeline';
months.forEach(function(m){
var b = buckets[m];
var bar = document.createElement('div'); bar.className='tbar';
bar.style.height = Math.max(2, Math.round(b.count/maxC*72)) + 'px';
bar.title = m+' · '+b.count+' permit'+(b.count===1?'':'s')+' · $'+Math.round(b.cost).toLocaleString();
tl.appendChild(bar);
});
tlCard.appendChild(tl);
var ax = document.createElement('div'); ax.className='timeline-axis';
var first = document.createElement('span'); first.textContent = months[0];
var last = document.createElement('span'); last.textContent = months[months.length-1];
ax.appendChild(first); ax.appendChild(last);
tlCard.appendChild(ax);
}
out.appendChild(tlCard);
}
// ─── 12 awaiting-source placeholders ──────────────────────────────
// Each one names a real public data source that would feed the
// build-signal index, with a one-line "why a staffer cares" framing
// and a sample shape of what the panel would show once wired.
var phHeader = document.createElement('div');
phHeader.className = 'section-label';
phHeader.textContent = '◆ 12 awaiting sources — what plugs in next';
out.appendChild(phHeader);
var phGrid = document.createElement('div');
phGrid.className = 'placeholder-grid';
PLACEHOLDERS.forEach(function(p){
var c = document.createElement('div'); c.className='ph-card';
var h = document.createElement('h4');
var name = document.createElement('span'); name.textContent = p.name;
var badge = document.createElement('span'); badge.className='badge'; badge.textContent='AWAITING';
h.appendChild(name); h.appendChild(badge);
c.appendChild(h);
var why = document.createElement('div'); why.className='why'; why.textContent = p.why;
c.appendChild(why);
var would = document.createElement('div'); would.className='would';
would.textContent = 'Would show: ' + p.would;
c.appendChild(would);
phGrid.appendChild(c);
});
out.appendChild(phGrid);
// Roadmap footer
var foot = document.createElement('div');
foot.style.marginTop = '20px';
foot.style.fontSize = '10px';
foot.style.color = '#484f58';
foot.style.lineHeight = '1.6';
foot.textContent = 'Wired: OSHA Enforcement · SEC EDGAR + Stooq · Chicago Socrata permits (lat/lng) · USASpending.gov · curated parent-ticker map · ILSOS (datacenter ASN blocked). 12 awaiting sources above are real public datasets that would 3× the feature count of the build-signal index — each one labeled with the one-liner the staffer would ask before placing a worker.';
out.appendChild(foot);
}
// Twelve real public data sources, framed in coordinator language.
// Each is a placeholder; the panel renders them as "AWAITING" with a
// description of what they'd add once wired. Order is roughly: highest
// staffing-decision relevance first.
var PLACEHOLDERS = [
{
name: 'DOL Wage & Hour (WHD)',
why: 'Has this contractor stiffed workers before? WHD posts every back-wage settlement and unpaid-overtime case.',
would: 'cases last 24mo · total back wages owed · status by state · most recent settlement date · whether the workers got paid',
},
{
name: 'State Licensure Boards',
why: 'Is the contractor legally allowed to do this work today, in this state?',
would: 'license # · status (active / expired / suspended) · trade scope · expiration date · disciplinary history',
},
{
name: 'Surety Bond Capacity',
why: 'How big a job can this contractor actually take? Bond ceiling = upper bound on what they\'re bonded for.',
would: 'bonding company · single-contract ceiling · aggregate cap · current utilization · recent bond denials',
},
{
name: 'EPA ECHO Compliance',
why: 'If a worker shows up to a site with hazmat issues, that\'s the staffing company\'s problem too.',
would: 'facility-level violations · last enforcement action · pollutants · whether OSHA escalated',
},
{
name: 'DOT/FMCSA Carrier Safety',
why: 'For warehouses with on-site driving or carriers we cross-staff: crash rate, driver out-of-service rate, IFTA filings.',
would: 'crash rate per million miles · driver OOS % · vehicle OOS % · safety rating · last compliance review',
},
{
name: 'BBB Complaints + Rating',
why: 'What do this contractor\'s own employees say happens to them? BBB aggregates complaints from workers and clients.',
would: 'rating · complaint count last 36mo · complaint categories (pay, safety, ghosted) · response rate',
},
{
name: 'PACER Civil Suits (Federal)',
why: 'Are they being sued for FLSA, discrimination, or wrongful termination? Filings predate enforcement actions.',
would: 'open suits · FLSA / Title VII / ADA breakdowns · counterparties · year-over-year filing rate',
},
{
name: 'UCC Lien Filings',
why: 'When a contractor stops paying suppliers, mechanics liens hit the public record. Cash-flow distress signal.',
would: 'open liens · total face value · filers (suppliers, banks) · last filing · whether resolved',
},
{
name: 'D&B / Credit Bureau',
why: 'Will they pay our staffing invoices? D&B PAYDEX score is the standard.',
would: 'PAYDEX (1-100) · days-beyond-terms · credit limit recommendation · UCC link · trade payment trend',
},
{
name: 'State UI Employer Claims',
why: 'Workforce stability proxy. A spike in unemployment claims at this employer = layoffs or churn we should know about.',
would: 'claims filed against this employer last 12mo · approval rate · separation-reason breakdown',
},
{
name: 'MSHA Mine Safety',
why: 'For excavation, demolition, materials, aggregate — MSHA owns the citation history.',
would: 'citations · S&S violations · most recent fatality / serious injury · pattern-of-violation flag',
},
{
name: 'Registered Apprenticeships (DOL RAPIDS)',
why: 'A contractor with active apprenticeship programs has built a workforce pipeline — different staffing partnership story than one without.',
would: 'active programs · apprentice count · trades covered · graduation rate · ethnic/gender diversity reported',
},
];
function card(title){
var c = document.createElement('div');
c.className = 'card';
var h = document.createElement('h3');
h.textContent = title;
c.appendChild(h);
return c;
}
function big(c, value, sub){
var b = document.createElement('div'); b.className='big'; b.textContent=value;
var s = document.createElement('div'); s.className='sub'; s.textContent=sub;
c.appendChild(b); c.appendChild(s);
}
function rowEl(c, label, value){
var r = document.createElement('div'); r.className='row';
var l = document.createElement('span'); l.className='l'; l.textContent=label;
var v = document.createElement('span'); v.className='v'; v.textContent=value||'—';
r.appendChild(l); r.appendChild(v); c.appendChild(r);
}
function empty(c, msg){
var e = document.createElement('div'); e.className='empty'; e.textContent=msg;
c.appendChild(e);
}
</script>
</body></html>

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// Visual filler iconography rendered through ComfyUI. Distinct from
// role_scenes.ts (which renders portraits) — these are object/badge
// style renders that fill dead space on worker cards: cert pills,
// role-prop chips, hazard indicators, empty-state heroes.
//
// Layout on disk:
// data/icons_pool/{category}/{slug}.webp
//
// Cache invalidation:
// ICONS_VERSION mixes into the on-disk filename (slug includes
// version). Bump it after editing a recipe so prior renders are
// ignored on next view.
export type IconCategory = "cert" | "role_prop" | "status" | "hazard" | "empty";
export interface IconRecipe {
slug: string;
category: IconCategory;
// Text label that appears next to / under the icon. The front-end
// already renders this text in cert pills; the icon is supplementary.
display: string;
// Full diffusion prompt. Style guidance baked in. SDXL Turbo at 8
// steps reliably produces clean macro photography, so default to
// photographic prop shots over flat-vector illustrations (the model
// hallucinates noise into flat-vector geometry at low step counts).
prompt: string;
// Negative prompt — what NOT to render. Crucial for icons because
// SDXL likes to add hands/text/people unprompted.
negative?: string;
}
// Default negative prompt baked into every icon render unless the
// recipe overrides. Empirically, these terms are the top SDXL Turbo
// off-style failures.
export const DEFAULT_NEGATIVE =
"people, hands, faces, blurry, low quality, watermark, signature, "
+ "logos, copyright, distorted text, garbled letters, multiple objects";
// TODO J — review and tune the prompts here. Each one is what diffusion
// sees verbatim. The visual decision: photographic prop shots (macro
// photo of an actual badge / placard / sticker) vs flat-icon vector
// style. Default below is photographic — matches the worker headshot
// aesthetic. Flip a recipe to flat-vector by replacing "macro photograph"
// with "flat icon illustration on solid color background, minimal vector".
//
// Visual cues that work well in SDXL Turbo at 8 steps:
// - "macro photograph", "isolated on plain background", "studio lighting"
// - Concrete colors ("orange and black warning diamond") not adjectives
// - Avoid: small text in the prompt (model garbles it), specific brand
// names (creates fake logos), detailed scene composition
const CERT_ICONS: IconRecipe[] = [
{ slug: "osha-10", category: "cert", display: "OSHA-10",
prompt: "macro photograph of a circular yellow safety badge with a black hard hat icon at center, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "osha-30", category: "cert", display: "OSHA-30",
prompt: "macro photograph of a circular orange safety badge with a black hard hat icon at center, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "first-aid-cpr", category: "cert", display: "First Aid/CPR",
prompt: "macro photograph of a small enamel pin badge featuring a bold red cross on a white circular background, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "hazmat", category: "cert", display: "Hazmat",
prompt: "macro photograph of a HAZMAT warning placard, bold orange and black diamond shape with a flame icon, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "forklift", category: "cert", display: "Forklift",
prompt: "macro photograph of a yellow industrial forklift safety badge with a forklift silhouette icon, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "reach-truck", category: "cert", display: "Reach Truck",
prompt: "macro photograph of a navy blue industrial certification badge with a warehouse reach-truck silhouette icon, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "order-picker", category: "cert", display: "Order Picker",
prompt: "macro photograph of a green industrial certification badge with a warehouse order-picker silhouette icon, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "lockout-tagout", category: "cert", display: "Lockout/Tagout",
prompt: "macro photograph of a bright red padlock tag with a danger warning, hanging on a metal industrial valve, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "msds", category: "cert", display: "MSDS",
prompt: "macro photograph of a folded chemical safety data sheet booklet with chemical hazard pictograms visible on cover, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "confined-space", category: "cert", display: "Confined Space",
prompt: "macro photograph of a yellow confined space warning sign featuring a manhole entry icon, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "servsafe", category: "cert", display: "ServSafe",
prompt: "macro photograph of a dark green food safety certification badge featuring a stylized chef hat icon, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "fire-safety", category: "cert", display: "Fire Safety",
prompt: "macro photograph of a red enamel pin badge featuring a flame icon and a fire extinguisher silhouette, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "iso-9001", category: "cert", display: "ISO 9001",
prompt: "macro photograph of a deep blue circular quality-management certification seal with embossed metallic ring, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
];
// Role-band visual chips — small icons that go in the role pill area.
// One per band, optional inline supplement to the existing colored pill.
const ROLE_PROP_ICONS: IconRecipe[] = [
{ slug: "warehouse", category: "role_prop", display: "Warehouse",
prompt: "macro photograph of a yellow hard hat with a high-visibility safety vest folded behind it, isolated on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "production", category: "role_prop", display: "Production",
prompt: "macro photograph of a navy blue work shirt and protective safety glasses on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "trades", category: "role_prop", display: "Trades",
prompt: "macro photograph of a leather work glove and a small adjustable wrench on a neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "driver", category: "role_prop", display: "Driver",
prompt: "macro photograph of a navy delivery driver baseball cap and a clipboard manifest on a neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
{ slug: "lead", category: "role_prop", display: "Lead",
prompt: "macro photograph of a tablet showing a bar chart and a high-vis vest folded beside it on neutral grey backdrop, photorealistic, sharp focus, studio lighting" },
];
export const ICONS: Record<string, IconRecipe> = Object.fromEntries(
[...CERT_ICONS, ...ROLE_PROP_ICONS].map((r) => [`${r.category}/${r.slug}`, r]),
);
// v2 — 256×256 canvas, intended to be displayed monochrome via CSS
// `filter: grayscale(1)`. Smaller canvas, tighter crops, crisper at
// 14px display size.
export const ICONS_VERSION = "v2";
// Map a free-form cert string from the data ("First Aid/CPR",
// "OSHA-10", "Lockout/Tagout") to the canonical slug used here.
// Returns null if no recipe matches.
export function certToSlug(cert: string): string | null {
const c = (cert || "").trim().toLowerCase().replace(/\s+/g, "-");
if (c === "osha-10") return "osha-10";
if (c === "osha-30") return "osha-30";
if (c.startsWith("first") || c.includes("cpr")) return "first-aid-cpr";
if (c === "hazmat" || c.startsWith("hazwoper")) return "hazmat";
if (c === "forklift" || c.startsWith("pit")) return "forklift";
if (c.startsWith("reach")) return "reach-truck";
if (c.startsWith("order")) return "order-picker";
if (c.startsWith("lockout") || c.includes("tagout")) return "lockout-tagout";
if (c === "msds" || c.startsWith("ghs")) return "msds";
if (c.startsWith("confined")) return "confined-space";
if (c === "servsafe") return "servsafe";
if (c.startsWith("fire")) return "fire-safety";
if (c.startsWith("iso")) return "iso-9001";
return null;
}

1101
mcp-server/index.ts
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<!DOCTYPE html>
<html><head>
<meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1">
<title>Profiler Index · Staffing Co-Pilot</title>
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.9.4/dist/leaflet.css">
<script src="https://unpkg.com/leaflet@1.9.4/dist/leaflet.js"></script>
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/* Hero — the thesis panel that frames the data corpus's value. */
.thesis{background:linear-gradient(135deg,#0d2818 0%,#0d1830 50%,#1a1410 100%);border:1px solid #2ea04344;border-radius:12px;padding:18px 22px;margin-bottom:14px;position:relative;overflow:hidden}
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.thesis .sub b{color:#3fb950;font-weight:600}
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.bai-block{display:flex;flex-direction:column;gap:2px}
.bai-label{font-size:9px;color:#545d68;text-transform:uppercase;letter-spacing:1.4px;font-weight:700}
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.bai-value.up{color:#3fb950}
.bai-value.down{color:#f85149}
.bai-sub{font-size:10px;color:#8b949e;margin-top:1px}
.markets-strip{display:flex;gap:6px;flex-wrap:wrap;font-size:10px}
.market-pill{padding:3px 9px;border-radius:9px;font-weight:600;border:1px solid;letter-spacing:0.4px}
.market-pill.live{background:#0d2818;border-color:#3fb950;color:#3fb950}
.market-pill.next{background:#0d1830;border-color:#58a6ff;color:#58a6ff}
.market-pill.queue{background:#161b22;border-color:#21262d;color:#545d68}
.market-pill.queue::before{content:'· '}
/* Map panel below basket — populates when a ticker is selected. */
.signal-map-wrap{display:none;background:#0d1117;border:1px solid #171d27;border-radius:10px;padding:14px;margin-bottom:14px}
.signal-map-wrap.active{display:block}
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.signal-map .leaflet-container{background:#0a0d12}
/* Scrolling ticker basket — top strip showing every public issuer
the profiler index has surfaced, with live price + day-change. */
.basket-wrap{background:#0a0d12;border:1px solid #171d27;border-radius:10px;margin-bottom:14px;overflow:hidden;position:relative}
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</style>
</head><body>
<div class="bar">
<h1>Staffing Co-Pilot · Profiler Index</h1>
<nav>
<a href="" id="back-dashboard">← Dashboard</a>
<a href="" id="back-console">Console</a>
</nav>
</div>
<div class="content">
<!-- Hero thesis — frames what this data corpus actually is. The
profiler index isn't just a contractor directory; it's a
construction-activity signal that surfaces public issuers months
before quarterly earnings does. Each metric here is computed
from the live data, not pre-baked. -->
<div class="thesis" id="thesis">
<h2>Chicago Construction Activity Signal Engine</h2>
<div class="sub">
Every contractor name in this corpus is also a forward indicator on the public equities they touch. Permits filed today predict construction starts ~45 days out, staffing windows ~2 weeks before that, and revenue recognition months later. The associated-ticker network surfaces this signal <b>before</b> it lands in any 10-Q.
</div>
<div class="bai-row">
<div class="bai-block">
<span class="bai-label">Building Activity Index — today</span>
<span class="bai-value" id="bai-value">—</span>
<span class="bai-sub" id="bai-sub">awaiting basket prices</span>
</div>
<div class="bai-block">
<span class="bai-label">Indexed build value</span>
<span class="bai-value" id="bav-value">—</span>
<span class="bai-sub" id="bav-sub">across surfaced issuers</span>
</div>
<div class="bai-block">
<span class="bai-label">Network depth</span>
<span class="bai-value" id="net-value">—</span>
<span class="bai-sub" id="net-sub">issuers · attributions</span>
</div>
<div class="bai-block" style="flex:1;min-width:240px">
<span class="bai-label">Market replication roadmap</span>
<div class="markets-strip" style="margin-top:4px">
<span class="market-pill live">Chicago — live</span>
<span class="market-pill next">NYC DOB — adapter ready</span>
<span class="market-pill queue">LA County · Houston BCD · Boston ISD · DC DCRA</span>
</div>
</div>
</div>
</div>
<div class="basket-wrap" id="basket-wrap" style="display:none">
<div class="basket-label">
<span><span id="bk-count">0</span> public issuers in this view <span class="meta" id="bk-meta"></span></span>
<button class="basket-clear" id="bk-clear" style="display:none" type="button">clear filter</button>
</div>
<div class="basket-track" id="basket"></div>
</div>
<!-- Per-ticker permit map — shows where the selected issuer's
attributed contractor activity is actually happening. Same
leaflet pattern as the contractor profile, scoped to one ticker. -->
<div class="signal-map-wrap" id="signal-map-wrap">
<div class="signal-map-header">
<span class="signal-map-title">Where <b id="signal-map-ticker">—</b> activity is happening</span>
<span class="signal-map-meta" id="signal-map-meta">—</span>
</div>
<div class="signal-map" id="signal-map"></div>
</div>
<div class="controls">
<input class="s" id="q" type="text" placeholder="Filter by contractor name (e.g., Target, Turner)" autocomplete="off">
<select id="since">
<option value="2025-06-01">Since June 2025</option>
<option value="2024-01-01">Since 2024</option>
<option value="2020-01-01">Since 2020 (deeper history)</option>
</select>
<select id="min-cost">
<option value="500000">$500K+</option>
<option value="250000" selected>$250K+</option>
<option value="100000">$100K+</option>
<option value="50000">$50K+</option>
</select>
<span class="meta" id="meta">Loading…</span>
</div>
<div class="summary" id="summary" style="display:none"></div>
<div id="result"><div class="loading">Loading the directory from Chicago Socrata…</div></div>
<div class="foot">Aggregations sourced live from data.cityofchicago.org (Building Permits dataset ydr8-5enu). Contractor names appear when listed as contact_1 or contact_2 on a permit. Click any name to open the full profile — heat map, project index, history, 12 awaiting public-data sources.</div>
</div>
<script>
var P=location.pathname.indexOf('/lakehouse')>=0?'/lakehouse':'';
document.getElementById('back-dashboard').href = P+'/';
document.getElementById('back-console').href = P+'/console';
var sortKey='total_cost', sortDir='desc';
var lastRows=[];
var tickerFilter=null; // selected ticker for filtering the table
var lastQuotes={}; // ticker → quote (price, day_change_pct)
var lastBasket=[]; // basket rows aggregated from lastRows
var signalMap=null; // leaflet map instance for the per-ticker view
function clearChildren(el){ while(el.firstChild) el.removeChild(el.firstChild); }
function fmt$(n){
if(n>=1e9) return '$'+(n/1e9).toFixed(2)+'B';
if(n>=1e6) return '$'+(n/1e6).toFixed(1)+'M';
if(n>=1e3) return '$'+(n/1e3).toFixed(0)+'K';
return '$'+Math.round(n||0).toLocaleString();
}
function costClass(n){
if(n>=1e7) return 'cost-band-3';
if(n>=1e6) return 'cost-band-2';
return 'cost-band-1';
}
function load(){
var search=document.getElementById('q').value.trim();
var since=document.getElementById('since').value;
var minCost=parseInt(document.getElementById('min-cost').value,10);
document.getElementById('meta').textContent='Loading…';
var host=document.getElementById('result'); clearChildren(host);
var loading=document.createElement('div'); loading.className='loading';
loading.textContent='Aggregating from Chicago Socrata…';
host.appendChild(loading);
fetch(P+'/intelligence/profiler_index',{
method:'POST',
headers:{'Content-Type':'application/json'},
body:JSON.stringify({since:since,min_cost:minCost,search:search,limit:200})
}).then(function(r){return r.json()}).then(function(d){
lastRows = d.contractors||[];
document.getElementById('meta').textContent=lastRows.length+' contractors · '+(d.duration_ms||0)+'ms';
// Build the ticker basket from the surfaced rows
buildBasket();
var totalCost = lastRows.reduce(function(s,r){return s+(r.total_cost||0)},0);
var totalPermits = lastRows.reduce(function(s,r){return s+(r.permits||0)},0);
var sumDiv=document.getElementById('summary');
sumDiv.style.display='block';
clearChildren(sumDiv);
var b1=document.createElement('b'); b1.textContent=lastRows.length.toLocaleString();
sumDiv.appendChild(b1);
sumDiv.appendChild(document.createTextNode(' contractors · '));
var b2=document.createElement('b'); b2.textContent=totalPermits.toLocaleString();
sumDiv.appendChild(b2);
sumDiv.appendChild(document.createTextNode(' total permits · '));
var b3=document.createElement('b'); b3.textContent=fmt$(totalCost);
sumDiv.appendChild(b3);
sumDiv.appendChild(document.createTextNode(' aggregate value · since '+(d.since||'?')+' · min permit cost '+fmt$(d.min_cost||0)));
render();
}).catch(function(e){
document.getElementById('meta').textContent='error';
var host=document.getElementById('result'); clearChildren(host);
var er=document.createElement('div'); er.className='empty'; er.style.color='#f85149';
er.textContent='Profiler index error: '+e.message;
host.appendChild(er);
});
}
// Aggregate every public ticker the profiler index surfaced, with a
// kind hierarchy (exact > direct > parent > associated) and the count
// of contractors each ticker is attributed to. Then fetch live quotes
// in one batch and render the scrolling basket.
function buildBasket(){
var byTicker = {};
lastRows.forEach(function(r){
var ts = (r.tickers && r.tickers.direct ? r.tickers.direct : []).concat(r.tickers && r.tickers.associated ? r.tickers.associated : []);
ts.forEach(function(t){
if(!t || !t.ticker) return;
if(!byTicker[t.ticker]) byTicker[t.ticker] = {ticker:t.ticker, kinds:new Set(), count:0, contractors:[], matched_name:t.matched_name||t.partner_name||null};
byTicker[t.ticker].kinds.add(t.via);
byTicker[t.ticker].count++;
if(byTicker[t.ticker].contractors.length < 5) byTicker[t.ticker].contractors.push(r.name);
});
});
var basketRows = Object.values(byTicker)
.map(function(b){
// Pick a single 'kind' for the bar color: direct/exact wins, then parent, then associated.
var k = b.kinds.has('exact')?'exact':b.kinds.has('direct')?'direct':b.kinds.has('parent')?'parent':b.kinds.has('associated')?'associated':'mixed';
if(b.kinds.size>1 && (b.kinds.has('exact')||b.kinds.has('direct')) && b.kinds.has('associated')) k='mixed';
return Object.assign({}, b, {kinds:Array.from(b.kinds), kind:k});
})
.sort(function(a,b){return b.count - a.count});
var wrap = document.getElementById('basket-wrap');
var track = document.getElementById('basket');
clearChildren(track);
if(!basketRows.length){
wrap.style.display='block';
var emp=document.createElement('div'); emp.className='basket-empty';
emp.textContent='No public issuers in this view. Try a wider filter or "since 2020" history.';
track.appendChild(emp);
document.getElementById('bk-count').textContent='0';
document.getElementById('bk-meta').textContent='';
return;
}
wrap.style.display='block';
document.getElementById('bk-count').textContent=basketRows.length;
document.getElementById('bk-meta').textContent='loading prices…';
// Render shells immediately, then fill in prices when the batch returns
basketRows.forEach(function(b){
var card=document.createElement('div'); card.className='bk-card no-quote';
card.dataset.ticker=b.ticker;
var bar=document.createElement('div'); bar.className='kind-bar '+b.kind; card.appendChild(bar);
var tk=document.createElement('div'); tk.className='tk'; tk.textContent=b.ticker; card.appendChild(tk);
var px=document.createElement('div'); px.className='px'; px.textContent='—'; card.appendChild(px);
var ch=document.createElement('div'); ch.className='ch flat'; ch.textContent=' '; card.appendChild(ch);
var meta=document.createElement('div'); meta.className='meta';
meta.textContent=b.count+' attribution'+(b.count===1?'':'s')+' · '+b.kinds.join('+');
card.appendChild(meta);
card.title=(b.matched_name||b.ticker)+'\n'+b.contractors.slice(0,5).join('\n')+(b.count>5?'\n…':'');
card.onclick=function(){
tickerFilter = (tickerFilter===b.ticker) ? null : b.ticker;
Array.prototype.forEach.call(track.children, function(c){
c.classList.toggle('selected', c.dataset && c.dataset.ticker===tickerFilter);
});
document.getElementById('bk-clear').style.display = tickerFilter ? 'inline' : 'none';
showSignalMap(tickerFilter);
render();
};
track.appendChild(card);
});
lastBasket = basketRows;
// Update the hero panel right away with what we know without prices
updateThesisMetrics();
// Batch-fetch quotes and update each card + thesis
fetch(P+'/intelligence/ticker_quotes',{
method:'POST',headers:{'Content-Type':'application/json'},
body:JSON.stringify({tickers:basketRows.map(function(b){return b.ticker})})
}).then(function(r){return r.json()}).then(function(qd){
var quotes=qd.quotes||{};
lastQuotes = quotes;
document.getElementById('bk-meta').textContent='quotes via Stooq · '+(qd.duration_ms||0)+'ms';
Array.prototype.forEach.call(track.children, function(card){
var t=card.dataset.ticker; var q=quotes[t];
if(!q || !q.price) return;
card.classList.remove('no-quote');
var px=card.querySelector('.px'); px.textContent='$'+q.price.toFixed(2);
var ch=card.querySelector('.ch');
if(q.day_change_pct==null){ ch.textContent='close '+(q.price_date||''); ch.className='ch flat'; }
else if(q.day_change_pct>=0){ ch.textContent='+'+q.day_change_pct.toFixed(2)+'%'; ch.className='ch up'; }
else { ch.textContent=q.day_change_pct.toFixed(2)+'%'; ch.className='ch down'; }
});
updateThesisMetrics();
}).catch(function(){
document.getElementById('bk-meta').textContent='quote fetch failed';
});
}
// Compute the Building Activity Index and update the hero panel.
// BAI = attribution-weighted day-change % across surfaced issuers.
// "Indexed build value" = total dollars of permits attributable to
// any public issuer in this view (sum across attributing contractors).
// "Network depth" = issuer count + total attributions.
function updateThesisMetrics(){
if(!lastBasket.length){
document.getElementById('bai-value').textContent='—';
document.getElementById('bai-sub').textContent='awaiting basket data';
return;
}
// BAI: weighted average of day_change_pct, weight = attribution count.
var weightedSum=0, weightTotal=0, contributors=[];
lastBasket.forEach(function(b){
var q = lastQuotes[b.ticker];
if(q && q.day_change_pct!=null){
var w = b.count || 1;
weightedSum += q.day_change_pct * w;
weightTotal += w;
contributors.push({ticker:b.ticker, day:q.day_change_pct, weight:w});
}
});
var bai = weightTotal>0 ? (weightedSum/weightTotal) : null;
var baiEl = document.getElementById('bai-value');
var baiSub = document.getElementById('bai-sub');
if(bai==null){
baiEl.textContent='—'; baiSub.textContent='no quotes settled yet';
baiEl.className='bai-value';
} else {
var sign = bai>=0 ? '+' : '';
baiEl.textContent = sign + bai.toFixed(2) + '%';
baiEl.className = 'bai-value ' + (bai>=0?'up':'down');
contributors.sort(function(a,b){return Math.abs(b.day*b.weight) - Math.abs(a.day*a.weight)});
var top = contributors.slice(0,3).map(function(c){return c.ticker+' '+(c.day>=0?'+':'')+c.day.toFixed(1)+'%'}).join(' · ');
baiSub.textContent = contributors.length+' issuers contributing · top: '+top;
}
// Indexed build value
var totalCost = 0;
lastRows.forEach(function(r){
var ts=(r.tickers&&r.tickers.direct?r.tickers.direct:[]).concat(r.tickers&&r.tickers.associated?r.tickers.associated:[]);
if(ts.length>0) totalCost += (r.total_cost||0);
});
var bav = totalCost>=1e9 ? '$'+(totalCost/1e9).toFixed(2)+'B' : totalCost>=1e6 ? '$'+(totalCost/1e6).toFixed(0)+'M' : '$'+Math.round(totalCost/1e3)+'K';
document.getElementById('bav-value').textContent = bav;
document.getElementById('bav-sub').textContent = lastBasket.length+' issuers in scope';
// Network depth
var totalAttrib = lastBasket.reduce(function(s,b){return s + (b.count||0)},0);
document.getElementById('net-value').textContent = lastBasket.length + ' / ' + totalAttrib;
document.getElementById('net-sub').textContent = 'issuers / attribution edges';
}
// Per-ticker map: when a ticker is selected, plot the contractor
// permit locations attributed to that ticker. Pulls lat/lng for each
// attributed contractor from the contractor profile endpoint and
// merges. Caches per-ticker so toggling is instant.
var mapCache = {};
function showSignalMap(ticker){
var wrap=document.getElementById('signal-map-wrap');
if(!ticker){ wrap.classList.remove('active'); if(signalMap){signalMap.remove(); signalMap=null;} return; }
wrap.classList.add('active');
document.getElementById('signal-map-ticker').textContent = ticker;
document.getElementById('signal-map-meta').textContent = 'loading permits…';
// Find the contractors attributed to this ticker
var attrib = lastRows.filter(function(r){
var ts=(r.tickers&&r.tickers.direct?r.tickers.direct:[]).concat(r.tickers&&r.tickers.associated?r.tickers.associated:[]);
return ts.some(function(t){return t.ticker===ticker});
});
if(!attrib.length){
document.getElementById('signal-map-meta').textContent='no attributed contractors';
return;
}
// Use the contractor_profile endpoint per attributed contractor (cap at 6)
// to pull their geocoded permits, then render. Cached per ticker.
if(mapCache[ticker]){
drawSignalMap(ticker, mapCache[ticker]);
return;
}
var names = attrib.slice(0,6).map(function(r){return r.name});
Promise.all(names.map(function(n){
return fetch(P+'/intelligence/contractor_profile',{
method:'POST',headers:{'Content-Type':'application/json'},
body:JSON.stringify({name:n})
}).then(function(r){return r.json()}).then(function(d){
var perms = (d.history && d.history.recent_permits) || [];
return perms.filter(function(p){return p.lat&&p.lng}).map(function(p){
return Object.assign({contractor:n}, p);
});
}).catch(function(){return []});
})).then(function(arrs){
var all = arrs.reduce(function(a,b){return a.concat(b)},[]);
mapCache[ticker] = all;
drawSignalMap(ticker, all);
});
}
function drawSignalMap(ticker, permits){
if(signalMap){ signalMap.remove(); signalMap=null; }
if(!permits.length){
document.getElementById('signal-map-meta').textContent='0 geocoded permits across attributed contractors';
return;
}
document.getElementById('signal-map-meta').textContent = permits.length + ' geocoded permits across ' + new Set(permits.map(function(p){return p.contractor})).size + ' contractors';
signalMap = L.map('signal-map',{zoomControl:true, attributionControl:false}).setView([41.88,-87.63], 11);
L.tileLayer('https://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}{r}.png',{maxZoom:19}).addTo(signalMap);
var bounds=[];
var maxCost = Math.max.apply(null, permits.map(function(p){return Number(p.cost)||1}));
permits.forEach(function(p){
var c=Number(p.cost)||0;
var radius = 4 + (c/maxCost)*14;
var color = c>=1000000?'#f85149':c>=100000?'#d29922':'#3fb950';
var marker = L.circleMarker([p.lat,p.lng],{radius:radius,color:color,weight:1,fillOpacity:0.55});
var pop=document.createElement('div');
pop.style.cssText='font-family:ui-monospace,monospace;font-size:11px;color:#0a0d12;min-width:200px';
var top=document.createElement('div'); top.style.cssText='font-weight:700;margin-bottom:3px;color:#1f6feb';
top.textContent=ticker+' attribution';
pop.appendChild(top);
var con=document.createElement('div'); con.textContent=p.contractor; con.style.fontWeight='600';
pop.appendChild(con);
var meta=document.createElement('div'); meta.style.color='#545d68';
meta.textContent='$'+c.toLocaleString()+' · '+(p.date||'?')+' · '+(p.work_type||'?');
pop.appendChild(meta);
var addr=document.createElement('div'); addr.style.color='#545d68';
addr.textContent=p.address||'?';
pop.appendChild(addr);
marker.bindPopup(pop);
marker.addTo(signalMap);
bounds.push([p.lat,p.lng]);
});
if(bounds.length>1) signalMap.fitBounds(bounds,{padding:[28,28]});
}
function render(){
var host=document.getElementById('result');
clearChildren(host);
// Apply ticker filter if set: keep only rows whose tickers include the selected one
var pool = tickerFilter ? lastRows.filter(function(r){
var ts=(r.tickers&&r.tickers.direct?r.tickers.direct:[]).concat(r.tickers&&r.tickers.associated?r.tickers.associated:[]);
return ts.some(function(t){return t.ticker===tickerFilter});
}) : lastRows;
if(!pool.length){
var emp=document.createElement('div'); emp.className='empty';
emp.textContent='No contractors match the current filter.';
host.appendChild(emp);
return;
}
var rows = pool.slice().sort(function(a,b){
var av=a[sortKey], bv=b[sortKey];
if(typeof av==='string'){ av=(av||'').toUpperCase(); bv=(bv||'').toUpperCase(); }
if(av<bv) return sortDir==='asc'?-1:1;
if(av>bv) return sortDir==='asc'?1:-1;
return 0;
});
var t=document.createElement('table');
var thead=document.createElement('thead'); var hr=document.createElement('tr');
var cols=[
{k:'name', label:'Contractor'},
{k:'permits', label:'Permits', right:true},
{k:'total_cost', label:'Total Value', right:true},
{k:'last_filed', label:'Last Filed', right:true},
{k:'roles', label:'Listed As'},
];
cols.forEach(function(c){
var h=document.createElement('th');
h.textContent=c.label;
if(c.right) h.style.textAlign='right';
if(sortKey===c.k){
var ar=document.createElement('span'); ar.className='arrow';
ar.textContent = sortDir==='asc' ? '▲' : '▼';
h.appendChild(ar);
}
h.onclick=function(){
if(sortKey===c.k) sortDir = sortDir==='asc' ? 'desc' : 'asc';
else { sortKey=c.k; sortDir = (c.k==='name') ? 'asc' : 'desc'; }
render();
};
hr.appendChild(h);
});
thead.appendChild(hr); t.appendChild(thead);
var tb=document.createElement('tbody');
rows.forEach(function(r){
var tr=document.createElement('tr');
var ntd=document.createElement('td'); ntd.className='name';
var a=document.createElement('a');
a.href = P+'/contractor?name='+encodeURIComponent(r.name);
a.target='_blank'; a.rel='noopener';
a.textContent = r.name;
ntd.appendChild(a);
// Ticker association pills — direct (green) = the contractor is a
// public issuer; parent (amber) = subsidiary of a public parent;
// associated (blue) = co-appears on permits with a public entity.
// Shows the correlation indicator J described — when Bob's Electric
// works permits with Target, TGT renders here as associated.
var t = r.tickers || {direct:[], associated:[]};
var allTk = (t.direct||[]).concat(t.associated||[]);
if(allTk.length){
var trk = document.createElement('div'); trk.className='tickers';
allTk.forEach(function(x){
var p = document.createElement('span');
p.className = 'ticker-pill ' + (x.via||'direct');
p.textContent = x.ticker;
// Tooltip shows the full reason path
var hint = x.via === 'associated'
? 'Associated via co-permits with '+x.partner_name+' ('+(x.co_permits||0)+' shared permits)' + (x.matched_name ? ' — '+x.matched_name : '')
: x.via === 'parent'
? 'Subsidiary of '+(x.matched_name||x.ticker) + (x.exchange ? ' · '+x.exchange : '')
: 'Direct match: '+(x.matched_name||r.name);
p.title = hint;
trk.appendChild(p);
});
ntd.appendChild(trk);
}
tr.appendChild(ntd);
var ptd=document.createElement('td'); ptd.className='right';
ptd.textContent=(r.permits||0).toLocaleString();
tr.appendChild(ptd);
var ctd=document.createElement('td'); ctd.className='right '+costClass(r.total_cost||0);
ctd.textContent=fmt$(r.total_cost||0);
tr.appendChild(ctd);
var ltd=document.createElement('td'); ltd.className='right';
ltd.textContent=(r.last_filed||'').slice(0,10) || '—';
tr.appendChild(ltd);
var rtd=document.createElement('td'); rtd.className='role';
(r.roles||[]).forEach(function(role){
var pill=document.createElement('span'); pill.className='pill'; pill.textContent=role;
rtd.appendChild(pill);
});
tr.appendChild(rtd);
tb.appendChild(tr);
});
t.appendChild(tb);
host.appendChild(t);
}
var sDeb;
document.getElementById('q').addEventListener('input',function(){
clearTimeout(sDeb);
sDeb=setTimeout(load,400);
});
document.getElementById('since').addEventListener('change',load);
document.getElementById('min-cost').addEventListener('change',load);
document.getElementById('bk-clear').addEventListener('click',function(){
tickerFilter=null;
document.getElementById('bk-clear').style.display='none';
Array.prototype.forEach.call(document.querySelectorAll('.bk-card.selected'), function(c){c.classList.remove('selected')});
showSignalMap(null);
render();
});
window.addEventListener('load',load);
</script>
</body></html>

425
mcp-server/proof.html
View File

@ -81,6 +81,7 @@ pre{background:#161b22;border:1px solid #171d27;border-radius:8px;padding:14px 1
<nav>
<a href=".">Dashboard</a>
<a href="console">Walkthrough</a>
<a href="profiler">Profiler</a>
<a href="proof" class="active">Architecture</a>
<a href="spec">Spec</a>
<a href="onboard">Onboard</a>
@ -95,138 +96,137 @@ pre{background:#161b22;border:1px solid #171d27;border-radius:8px;padding:14px 1
<div class="chapter">
<div class="num">Chapter 1</div>
<h2>Receipts, not promises</h2>
<div class="lede">Every test below ran live against the real gateway when you loaded this page. Sub-100ms SQL on multi-million-row Parquet, hybrid search with playbook boost applied. No fixtures. If a test fails, you'll see ✗.</div>
<div class="lede">Every test below ran live against the real gateway when you loaded this page. Sub-100ms SQL on multi-million-row Parquet, hybrid search with playbook boost applied, public-issuer attribution computed from this view. No fixtures. If a test fails, you'll see ✗.</div>
<div id="ch1-tests"><div class="loading">Running tests…</div></div>
<div id="ch1-live" style="margin-top:14px"></div>
</div>
<div class="chapter">
<div class="num">Chapter 2</div>
<h2>Architecture — 13 crates, one object store, one local AI runtime</h2>
<div class="lede">Request flows top to bottom. Every node is independently swappable. Every line is a real HTTP or gRPC hop that you can trace with <code>tcpdump</code>.</div>
<h2>Architecture — 15 crates, one object store, a 5-provider model fleet</h2>
<div class="lede">Gateway is a drop-in OpenAI-compatible middleware. Any consumer that speaks the OpenAI Chat Completions shape — agent SDKs, IDE plugins, custom apps — points at <code>localhost:3100/v1</code> and gets routing, audit, and the full memory substrate behind every call. The model side has 5 providers and 40+ frontier models reachable via one OpenCode key. The data side stays Rust-first.</div>
<div class="card accent-b">
<pre> HTTP :3100 + gRPC :3101
│
┌───────▼───────┐
│ gateway │ Rust · Axum · routing, CORS, auth, tools
└───────┬───────┘
┌────────────┬───────────┼───────────┬────────────┐
│ │ │ │ │
┌────▼───┐ ┌────▼───┐ ┌────▼───┐ ┌────▼───┐ ┌────▼───┐
│catalog │ │ query │ │ vector │ │ ingest │ │aibridge│
│ d │ │ d │ │ d │ │ d │ │ │
└────┬───┘ └────┬───┘ └────┬───┘ └────┬───┘ └────┬───┘
│ │ │ │ │
└────────────┴───────────┼───────────┴────────────┘
▼
┌─────────────────┐
│ object storage │ Parquet files (local / S3)
└─────────────────┘
▲
│
┌───────┴────────┐
│ Python sidecar │ FastAPI → Ollama
│ (aibridge) │ local models only
└────────────────┘</pre>
<pre> OpenAI SDK consumers MCP clients Browser UI (Bun :3700)
│ │ │
└──────────────────────────┼──────────────────────────┘
▼
┌──────────────────────────────┐
│ gateway :3100 /v1/* │ Rust · Axum
│ OpenAI-compat drop-in │ smart provider routing
│ /v1/chat /v1/mode /iterate │ cost telemetry, Langfuse
└──────────┬───────────────────┘
┌─────────┬───────────────┼───────────────┬──────────┐
│ │ │ │ │
┌────▼───┐ ┌───▼────┐ ┌─────▼──────┐ ┌─────▼─────┐ ┌──▼──────┐
│catalog │ │ query │ │ vector │ │ ingest │ │aibridge │
│ d │ │ d │ │ d │ │ d │ │ │
│idempot │ │DataFus │ │HNSW · Lance│ │CSV PDF SQL│ │provider │
│schema │ │delta │ │playbook+ │ │auto-PII │ │adapters │
│fingerp │ │MemTabl │ │pathway mem │ │schema fp │ │5 active │
└────┬───┘ └───┬────┘ └─────┬──────┘ └─────┬─────┘ └──┬──────┘
└─────────┴────────────────┼────────────────┴─────────┘
▼
┌──────────────────┐
│ object storage │ Parquet · MinIO · S3-compat
└──────────────────┘
▲
│
┌───────────────┴────────────────┐
│ validator · journald │ schema/PII/policy gates
│ (Phase 43) · (audit log) │ + append-only mutations
└────────────────────────────────┘
Provider fleet (config/providers.toml):
ollama localhost:3200 local Ollama → qwen3.5, gemma2
ollama_cloud ollama.com gpt-oss:120b, qwen3-coder:480b,
deepseek-v3.1:671b, kimi-k2:1t,
mistral-large-3:675b, qwen3.5:397b
openrouter openrouter.ai/api/v1 343 models — paid + free rescue
opencode opencode.ai/zen/v1 40 models · ONE sk-* key reaches
Claude Opus 4.7, GPT-5.5-pro,
Gemini 3.1-pro, Kimi K2.6, GLM 5.1,
DeepSeek, Qwen, MiniMax, free tier
kimi api.kimi.com/coding/v1 direct Kimi For Coding (TOS-clean)</pre>
</div>
<h3>Per-crate responsibility</h3>
<h3>Per-crate responsibility (15 crates)</h3>
<table class="plain">
<thead><tr><th>Crate</th><th>Role</th><th>Path</th></tr></thead>
<tbody>
<tr><td>shared</td><td>Types, errors, Arrow helpers, PII detection, secrets provider</td><td>crates/shared/</td></tr>
<tr><td>storaged</td><td>object_store I/O, BucketRegistry (multi-bucket), AppendLog, ErrorJournal</td><td>crates/storaged/</td></tr>
<tr><td>catalogd</td><td>Metadata authority — manifests, views, tombstones, profiles, schema fingerprints</td><td>crates/catalogd/</td></tr>
<tr><td>queryd</td><td>DataFusion SQL engine, MemTable cache, delta merge-on-read, compaction</td><td>crates/queryd/</td></tr>
<tr><td>ingestd</td><td>CSV/JSON/PDF(+OCR)/Postgres/MySQL ingest, cron schedules, auto-PII</td><td>crates/ingestd/</td></tr>
<tr><td>vectord</td><td>Embeddings as Parquet, HNSW, trial system, autotune agent, playbook_memory</td><td>crates/vectord/</td></tr>
<tr><td>shared</td><td>Types, errors, Arrow helpers, PII detection, secrets provider, model_matrix</td><td>crates/shared/</td></tr>
<tr><td>storaged</td><td>object_store I/O, BucketRegistry, AppendLog, ErrorJournal, federation_service</td><td>crates/storaged/</td></tr>
<tr><td>catalogd</td><td>Manifests, views (incl. PII-safe view layer), tombstones, profiles, schema fingerprints, register-idempotency (ADR-020)</td><td>crates/catalogd/</td></tr>
<tr><td>queryd</td><td>DataFusion SQL, MemTable cache, delta merge-on-read, compaction, truth gate (ADR-021)</td><td>crates/queryd/</td></tr>
<tr><td>ingestd</td><td>CSV/JSON/PDF(+OCR)/Postgres/MySQL ingest, cron schedules, auto-PII flagging</td><td>crates/ingestd/</td></tr>
<tr><td>vectord</td><td>Embeddings as Parquet, HNSW, trial system, autotune, playbook_memory + pathway_memory (ADR-021 semantic-correctness layer)</td><td>crates/vectord/</td></tr>
<tr><td>vectord-lance</td><td>Firewall crate — Lance 4.0 + Arrow 57 isolated from main Arrow 55</td><td>crates/vectord-lance/</td></tr>
<tr><td>journald</td><td>Append-only mutation event log for time-travel &amp; audit</td><td>crates/journald/</td></tr>
<tr><td>aibridge</td><td>Rust↔Python sidecar, Ollama HTTP client, VRAM introspection</td><td>crates/aibridge/</td></tr>
<tr><td>gateway</td><td>Axum HTTP :3100 + gRPC :3101, middleware, tools registry</td><td>crates/gateway/</td></tr>
<tr><td>ui</td><td>Dioxus WASM internal developer UI</td><td>crates/ui/</td></tr>
<tr><td>mcp-server</td><td>Bun TypeScript recruiter-facing app (this server)</td><td>mcp-server/</td></tr>
<tr><td>journald</td><td>Append-only mutation event log for time-travel + audit</td><td>crates/journald/</td></tr>
<tr><td>truth</td><td>File-backed rule store; <code>evaluate(task_class, ctx) → Vec&lt;RuleOutcome&gt;</code> (ADR-021)</td><td>crates/truth/</td></tr>
<tr><td>aibridge</td><td>Rust↔Python sidecar, Ollama client, ProviderAdapter trait, /v1/chat router</td><td>crates/aibridge/</td></tr>
<tr><td>gateway</td><td>Axum HTTP :3100 + gRPC :3101, OpenAI-compat /v1/*, mode runner, validator, iterate loop, cost telemetry, Langfuse + observer fan-out</td><td>crates/gateway/</td></tr>
<tr><td>validator</td><td>Phase 43 — schema / completeness / consistency / policy gates over LLM outputs (FillValidator, EmailValidator, ParquetWorkerLookup)</td><td>crates/validator/</td></tr>
<tr><td>ui</td><td>Dioxus WASM internal developer UI (separate from this Bun-served public UI)</td><td>crates/ui/</td></tr>
<tr><td>mcp-server</td><td>Bun TypeScript public-facing app + MCP tool surface — what you're reading right now</td><td>mcp-server/</td></tr>
<tr><td>auditor</td><td>External claim-vs-diff verifier on PRs · Kimi K2.6 ↔ Haiku 4.5 cross-lineage alternation, Opus 4.7 auto-promote on diffs &gt;100k chars</td><td>auditor/</td></tr>
</tbody>
</table>
<div class="ref"><strong>Source:</strong> git.agentview.dev/profit/lakehouse &nbsp;·&nbsp; <strong>ADRs:</strong> docs/DECISIONS.md (currently 20 records)</div>
<div class="ref"><strong>Source:</strong> git.agentview.dev/profit/lakehouse · branch <code>scrum/auto-apply-19814</code> · tag <code>distillation-v1.0.0</code> at commit <code>e7636f2</code> (frozen substrate) · <strong>ADRs:</strong> docs/DECISIONS.md (currently 21 records)</div>
</div>
<div class="chapter">
<div class="num">Chapter 3</div>
<h2>Dual-agent recursive consensus loop</h2>
<div class="lede">The system we use to execute staffing fills is a dual-agent recursive protocol. Two agents with distinct roles iterate against a shared log until one of three terminal states is reached. It is deterministic in structure, stochastic in content, and verifiable through the per-run log artifact.</div>
<h3>Agents and protocol</h3>
<div class="card accent-a">
<pre> task in
│
▼
┌───────────────────────────────────────────────────────────┐
│ EXECUTOR (mistral:latest) │
│ ──────────────────────────────────────────────────────── │
│ input: task spec + shared log + seen-candidates ledger │
│ output: one JSON action per turn │
│ · {kind:"plan",steps:[…]} │
│ · {kind:"tool_call",tool,args,rationale} │
│ · {kind:"propose_done",fills:[N of N]} │
└───────────┬───────────────────────────────┬───────────────┘
│ tool_call │ propose_done
▼ │
┌──────────────────────────┐ │
│ TOOL DISPATCH │ │
│ hybrid_search / sql │ │
│ (against live gateway) │ │
└──────────┬───────────────┘ │
│ result (trimmed, exclusions) │
▼ ▼
┌───────────────────────────────────────────────────────────┐
│ REVIEWER (qwen2.5:latest) │
│ ──────────────────────────────────────────────────────── │
│ input: task spec + shared log (including tool result) │
│ output: {kind:"critique",verdict:"continue|drift| │
│ approve_done",notes} │
└───────────┬───────────────────────────────────────────────┘
│
┌─────┴─────┐
▼ ▼ ▼
continue drift approve_done + propose_done ⟹ SEAL
(next turn) (cap ≈ 3 →
hard abort)
</pre>
</div>
<div class="ref"><strong>Code:</strong> tests/multi-agent/agent.ts (protocol + prompts) &nbsp;·&nbsp; tests/multi-agent/orchestrator.ts (run loop) &nbsp;·&nbsp; tests/multi-agent/scenario.ts (5-event warehouse week)</div>
<h2>The model fleet — 9-rung ladder, N=3 consensus, cross-lineage audit</h2>
<div class="lede">No single model owns the answer. Every consequential call is structured: the right tier picks up first, fallback rungs catch what fails, parallel runs vote, and an independent auditor of a different model lineage checks the result against the diff. The protocol is deterministic; the inference is stochastic; every step writes a receipt.</div>
<h3>Why "dual" — role specialization</h3>
<div class="narr">
<strong>The executor is an optimist.</strong> Its job is to produce progress: pull candidates, verify SQL, propose consensus. It's instructed to be decisive.
<br><br>
<strong>The reviewer is a pessimist.</strong> Its job is to catch drift: proposals that don't match the task's geography, fill count, or role. It's authorized to stop the loop.
<br><br>
This adversarial separation is cheaper and more deterministic than asking a single model to self-critique. The reviewer has a hard rule: on the turn after a <code>propose_done</code>, it MUST emit either <code>approve_done</code> or <code>drift</code> — it cannot stall with <code>continue</code>.
<h3>The 9-rung cloud-first ladder</h3>
<div class="card accent-b">
<pre> request in
│
▼
┌───────────────────────────────────────────────────────────────────┐
│ attempt 1 ollama_cloud / kimi-k2:1t 1T params · flagship │
│ attempt 2 ollama_cloud / qwen3-coder:480b coding specialist │
│ attempt 3 ollama_cloud / deepseek-v3.1:671b reasoning │
│ attempt 4 ollama_cloud / mistral-large-3:675b deep analysis │
│ attempt 5 ollama_cloud / gpt-oss:120b reliable workhorse │
│ attempt 6 ollama_cloud / qwen3.5:397b dense final thinker │
│ attempt 7 openrouter / openai/gpt-oss-120b:free rescue tier │
│ attempt 8 openrouter / google/gemma-3-27b-it:free fastest rescue │
│ attempt 9 ollama / qwen3.5:latest last-resort local │
└───────────────┬───────────────────────────────────────────────────┘
│ isAcceptable() = chars ≥ 3800 ∧ not malformed JSON
▼
sealed result OR next-rung learning preamble</pre>
</div>
<div class="narr">Every rung sees a learning preamble carrying the prior rejection reason. The ladder is the standard scrum/auditor path; for individual <code>/v1/chat</code> calls the caller picks the model directly (or lets the smart-routing default fire).</div>
<div class="ref"><strong>Code:</strong> tests/real-world/scrum_master_pipeline.ts <code>const LADDER</code> · config/routing.toml · crates/gateway/src/v1/mode.rs (mode runner)</div>
<h3>Why "parallel" — orchestrator can fan out</h3>
<div class="narr">
<strong>Independent pairs run concurrently.</strong> <code>tests/multi-agent/run_e2e_rated.ts</code> runs two task-specific agent pairs via <code>Promise.all</code>. Ollama serializes inference at the model level, so "parallel" is concurrent orchestration — but the substrate (gateway, queryd, vectord) handles concurrent requests cleanly. Verified in the scenario harness: two contracts sealing simultaneously.
</div>
<h3>Why "recursive" — each seal feeds the next</h3>
<div class="narr">
<strong>Consensus does not end at the sealed playbook.</strong> Every sealed playbook is persisted to <code>playbook_memory</code> via <code>POST /vectors/playbook_memory/seed</code>. The next hybrid search for a semantically similar operation consults that memory via <code>compute_boost_for(query_embedding, top_k, base_weight)</code> and re-ranks the candidate pool. The system builds on itself turn over turn, playbook over playbook.
</div>
<h3>Termination guarantees</h3>
<h3>N=3 consensus + tie-breaker (auditor inference)</h3>
<div class="math">
<span class="c">// three paths out, every run has one of these:</span><br>
sealed = executor.propose_done ∧ reviewer.approve_done ∧ fills.count == target<br>
abort = consecutive_tool_errors ≥ MAX_TOOL_ERRORS (3) &nbsp;&nbsp;<span class="c">// executor can't form a valid call</span><br>
abort = consecutive_drifts ≥ MAX_CONSECUTIVE_DRIFTS (3) &nbsp;<span class="c">// reviewer keeps flagging</span><br>
abort = turn &gt; MAX_TURNS (12) &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span class="c">// no consensus reached in window</span>
<span class="c">// auditor/checks/inference.ts — every claim audit runs this:</span><br>
1. Fire the primary reviewer N=3 times in PARALLEL (Promise.all) — wall-clock = single call<br>
2. Aggregate votes per claim_idx · majority wins<br>
3. On 1-1-1 split → tie-breaker model with <strong>different architecture</strong> (qwen3-coder:480b vs primary gpt-oss/kimi)<br>
4. Every disagreement (even when majority resolves) → <code>data/_kb/audit_discrepancies.jsonl</code><br>
<br>
<span class="c">// Closes the cloud-non-determinism gap: temp=0 isn't actually deterministic in practice</span><br>
<span class="c">// across hours; consensus + cross-architecture tie-break stabilizes verdicts.</span>
</div>
<div class="narr">Every abort dumps the full log to <code>tests/multi-agent/playbooks/&lt;id&gt;-FAILED.json</code> for forensic review. No consensus is ever implicit.</div>
<h3>Auditor cross-lineage — Kimi ↔ Haiku ↔ Opus</h3>
<div class="narr">Every push to PR #11 triggers <code>auditor/audit.ts</code> within ~90s. To prevent a single model lineage's blind spots from becoming the system's blind spots, audits alternate between Kimi K2.6 (Moonshot) and Haiku 4.5 (Anthropic) by SHA. Diffs over 100k chars auto-promote to Claude Opus 4.7. Per-PR cap of 3 audits with auto-reset on each new head SHA prevents infinite-loop spend. <strong>100% grounding-verified rate</strong> on Haiku 4.5 across the latest 10 findings — pairing different lineages + forcing per-finding grounding kills confabulation.</div>
<div class="ref"><strong>Code:</strong> auditor/audit.ts · auditor/checks/inference.ts (N=3) · auditor/checks/kimi_architect.ts · <strong>Verdicts:</strong> data/_auditor/kimi_verdicts/ — read any 11-&lt;sha&gt;.json to inspect a real audit</div>
<h3>Distillation v1.0.0 — the frozen substrate</h3>
<div class="narr">The substrate the auditor and mode runner sit on is tagged at <code>distillation-v1.0.0</code> / commit <code>e7636f2</code>. <strong>145 unit tests pass · 22/22 acceptance invariants · 16/16 audit-full checks · bit-identical reproducibility verified.</strong> The distillation phase exports clean SFT / RAG / preference samples with a multi-layer contamination firewall; the auditor consumes the substrate. The frozen tag means: any future "the system regressed" question has a baseline to bisect against, byte-for-byte.</div>
<div class="ref"><strong>Tag:</strong> distillation-v1.0.0 · <strong>Commit:</strong> e7636f2 · <strong>Substrate code:</strong> scripts/distillation/ · auditor/schemas/distillation/ · <strong>Output:</strong> data/_kb/distilled_{facts,procedures,config_hints}.jsonl</div>
</div>
<div class="chapter">
<div class="num">Chapter 4</div>
<h2>Playbook memory — the compounding feedback loop</h2>
<div class="lede">A CRM stores events. This system turns events into re-ranking signal. Every sealed playbook endorses specific (worker, city, state) tuples. Every failure penalizes them. Every similar future query inherits the signal through cosine similarity.</div>
<h2>Two memory layers — playbook (worker signal) + pathway (system signal)</h2>
<div class="lede">A CRM stores events. This system turns events into re-ranking signal at two layers. <strong>Playbook memory</strong> compounds worker-level outcomes (who got endorsed, where, when) into per-query boost. <strong>Pathway memory</strong> compounds system-level outcomes (which model + corpus + framing actually solved similar problems) into per-task hot-swap. Both are queryable. Both are auditable. Both compound.</div>
<h3>Layer 1 — playbook memory (worker + geo signal)</h3>
<h3>Seed shape</h3>
<div class="math">
@ -289,10 +289,82 @@ pre{background:#161b22;border:1px solid #171d27;border-radius:8px;padding:14px 1
<strong>Beyond "who was endorsed."</strong> <code>POST /vectors/playbook_memory/patterns</code> takes a query, finds top-K similar past playbooks, pulls each endorsed worker's full workers_500k profile, and aggregates shared traits: recurring certifications, skill frequencies, modal archetype, reliability distribution. Returns a <code>discovered_pattern</code> string showing operator-actionable signal the user didn't explicitly query for.
</div>
<div class="ref"><strong>Code:</strong> crates/vectord/src/playbook_memory.rs::discover_patterns &nbsp;·&nbsp; <strong>Surfaces:</strong> /vectors/playbook_memory/patterns endpoint, /intelligence/chat response, /intelligence/permit_contracts cards</div>
<h3>Layer 2 — pathway memory (system-level hot-swap, ADR-021)</h3>
<div class="narr">
<strong>Pathway memory remembers which approach worked, not just which worker.</strong> Every accepted scrum review writes a <code>PathwayTrace</code> with the full backtrack: file fingerprint, model used, signal class, KB chunks consulted, observer events, semantic flags, bug fingerprints. A new query that fingerprints to the same trace can hot-swap to the prior result without re-running the 9-rung escalation. The 5-factor hot-swap gate is strict: narrow fingerprint match AND audit consensus pass AND replay_count ≥ 3 (probation) AND success_rate ≥ 0.80 AND NOT retired AND vector cosine ≥ 0.90.
</div>
<div class="math">
<span class="c">// Live pathway state (refresh page to recompute):</span><br>
<span id="pwm-traces">— traces</span> · <span id="pwm-replays">—</span> successful replays · <span id="pwm-rate">—</span> reuse rate<br>
<span class="c">// 88 / 11/11 / 100% as of 2026-04-27 — probation gate crossed</span>
</div>
<div class="ref"><strong>Code:</strong> crates/vectord/src/pathway_memory.rs · <strong>Endpoints:</strong> /vectors/pathway/insert · /query · /record_replay · /stats · /bug_fingerprints · <strong>Spec:</strong> docs/DECISIONS.md ADR-021 — Semantic-correctness matrix layer</div>
<h3>What both memory layers feed (besides search)</h3>
<div class="narr">
Both layers also feed the <strong>per-staffer hot-swap index</strong> (Chapter 5) and the <strong>Construction Activity Signal Engine</strong> (Chapter 6). One memory model, surfaced three different ways at the request boundary depending on who's asking.
</div>
</div>
<div class="chapter">
<div class="num">Chapter 5</div>
<h2>Per-staffer hot-swap — same corpus, different relevance gradient</h2>
<div class="lede">Maria runs Chicago. Devon runs Indianapolis. Aisha runs Wisconsin/Michigan. They share one corpus, but the search results, the recurring-skill patterns, and the playbook context all reshape to whoever is acting. Same query "forklift operators" returns 89 IN workers when Devon's acting, 16 WI when Aisha's, 167 IL when Maria's. The MEMORY panel relabels itself with the active coordinator's name.</div>
<h3>What scopes per staffer</h3>
<div class="math">
<span class="c">// On every /intelligence/chat call:</span><br>
if (b.staffer_id) {<br>
&nbsp;&nbsp;const staffer = lookupStaffer(b.staffer_id);<br>
&nbsp;&nbsp;<span class="c">// 1. Default state filter to staffer territory unless caller pinned one</span><br>
&nbsp;&nbsp;if (!explicitState) filters.push(`state = '${staffer.territory.state}'`);<br>
&nbsp;&nbsp;<span class="c">// 2. Default playbook-pattern geo to staffer's primary city/state</span><br>
&nbsp;&nbsp;cityForPatterns = staffer.territory.cities[0];<br>
&nbsp;&nbsp;stateForPatterns = staffer.territory.state;<br>
&nbsp;&nbsp;<span class="c">// 3. Surface staffer.name back so the UI can relabel MEMORY → MARIA'S MEMORY</span><br>
&nbsp;&nbsp;response.staffer = { id, name, territory };<br>
}
</div>
<div class="narr">
The corpus stays intact. The relevance gradient is per coordinator. As each accumulates fills, their slice of the playbook compounds independently. The architecture generalizes — every new metro adds territories, not code paths.
</div>
<div class="ref"><strong>Code:</strong> mcp-server/index.ts <code>STAFFERS</code> roster + <code>lookupStaffer()</code> · <code>/staffers</code> endpoint · <code>/intelligence/chat</code> smart_search route · <strong>UI:</strong> staffer dropdown in mcp-server/search.html</div>
</div>
<div class="chapter">
<div class="num">Chapter 6</div>
<h2>Construction Activity Signal Engine — the corpus is also a market signal</h2>
<div class="lede">Every contractor in this corpus is also a forward indicator on the public equities they touch. Permits filed today predict construction starts ~45 days out, staffing ~30, revenue recognition months later. The associated-ticker network surfaces this signal <em>before</em> any 10-Q. The architecture is metro-agnostic — Chicago is Phase 1; NYC DOB, LA County, Houston BCD, Boston ISD ship as Socrata-shaped adapters.</div>
<h3>Three flavors of attribution</h3>
<div class="math">
<span class="c">// per contractor in /intelligence/profiler_index:</span><br>
direct <span class="c">// contractor IS a public issuer → SEC tickers index match</span><br>
parent <span class="c">// curated KNOWN_PARENT_MAP — Turner → HOC.DE via Hochtief AG</span><br>
associated <span class="c">// co-permit network — Bob's Electric appears with TARGET CORPORATION</span><br>
&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<span class="c">// 3+ times → inherits TGT as an associated indicator</span>
</div>
<div class="narr">
The associated path is the moat. A staffing-permit dataset that maps contractor-to-public-issuer is not commercially available; we synthesize it from the Socrata co-occurrence graph. Every additional metro multiplies edges.
</div>
<h3>Building Activity Index (BAI)</h3>
<div class="math">
<span class="c">// BAI = attribution-weighted average day-change across surfaced issuers:</span><br>
BAI = Σ (day_change_pct × attribution_count) / Σ attribution_count<br>
<br>
<span class="c">// Indexed build value = total $ of permits attributable to ANY public issuer</span><br>
<span class="c">// Network depth = issuers / total attribution edges</span>
</div>
<div class="narr">
Run BAI daily, save the series, and you've got a backtestable thesis in months. Today's surface is Chicago-only with ~9 issuers; the curve scales linearly with metros added — and the marginal cost of a new metro is one Socrata adapter.
</div>
<div class="ref"><strong>Code:</strong> mcp-server/index.ts <code>/intelligence/profiler_index</code> + <code>/intelligence/ticker_quotes</code> · entity.ts <code>lookupTickerLite()</code> · <code>fetchStooqQuote()</code> · <strong>UI:</strong> /profiler · <strong>Data sources:</strong> SEC company_tickers.json (in-memory index) + Stooq CSV API + curated parent-link map</div>
</div>
<div class="chapter">
<div class="num">Chapter 7</div>
<h2>Key architectural choices — what was picked and why</h2>
<div class="lede">Each choice is documented in <code>docs/DECISIONS.md</code> (Architecture Decision Records). If you dispute any of these, the ADR names the alternatives we rejected and the measurement that drove the call.</div>
<div class="card">
@ -314,62 +386,95 @@ pre{background:#161b22;border:1px solid #171d27;border-radius:8px;padding:14px 1
<div class="row accent-r">
<div style="flex:1"><div class="title">ADR-020 · Idempotent register() with schema-fingerprint gate</div><div class="meta">Same (name, fingerprint) reuses manifest. Different fingerprint = 409 Conflict. Prevents silent duplicate manifests. Cleanup run collapsed 374 → 31 datasets.</div></div>
</div>
<div class="row accent-r">
<div style="flex:1"><div class="title">ADR-021 · Semantic-correctness matrix layer</div><div class="meta">Pathway memory carries semantic flags (UnitMismatch, TypeConfusion, OffByOne, StaleReference, DeadCode, BoundaryViolation, …) on every trace. New reviews see prior bug fingerprints as a preamble; recurrent classes get caught on first read. Compounds across files in the same crate.</div></div>
</div>
<div class="row accent-l">
<div style="flex:1"><div class="title">Phase 19 design note · Statistical + semantic, not neural</div><div class="meta">Meta-index is cosine similarity + endorsement aggregation. No model training. Rebuildable from <code>successful_playbooks</code> alone. Neural re-ranker deferred to Phase 20+ only if statistical floor plateaus.</div></div>
</div>
<div class="row accent-l">
<div style="flex:1"><div class="title">Distillation freeze · v1.0.0 at e7636f2</div><div class="meta">145 tests · 22/22 acceptance · 16/16 audit-full · bit-identical reproducibility. Multi-layer contamination firewall on SFT exports. Substrate the auditor + mode runner sit on; "the system regressed" questions bisect against this anchor.</div></div>
</div>
</div>
</div>
<div class="chapter">
<div class="num">Chapter 6</div>
<div class="num">Chapter 8</div>
<h2>Measured at scale, on this machine</h2>
<div class="lede">Hardware: i9 + 128GB RAM + Nvidia A4000 16GB VRAM. Numbers below are from <em>this</em> running instance. Refresh the page and they'll recompute.</div>
<div class="lede">Hardware: i9 + 128GB RAM + Nvidia A4000 16GB VRAM + 2.5GB symmetric. Numbers below are from <em>this</em> running instance. Refresh the page and they'll recompute.</div>
<div class="grid" id="ch6-scale"><div class="loading">Loading scale data…</div></div>
<div id="ch6-recall" style="margin-top:10px"></div>
</div>
<div class="chapter">
<div class="num">Chapter 7</div>
<div class="num">Chapter 9</div>
<h2>Verify or dispute — reproduce it yourself</h2>
<div class="lede">Every claim below is a curl away from falsification.</div>
<div class="lede">Every claim above is a curl away from falsification.</div>
<div class="card">
<div class="narr"><strong>Health.</strong> Should return <code>lakehouse ok</code>.</div>
<pre>curl http://localhost:3100/health</pre>
<div class="narr"><strong>Gateway health.</strong> Returns provider matrix + worker count.</div>
<pre>curl -s http://localhost:3100/v1/health | jq</pre>
<div class="narr"><strong>Any SQL on multi-million-row Parquet.</strong> Sub-100ms typical.</div>
<pre>curl -s -X POST http://localhost:3100/query/sql \
-H 'Content-Type: application/json' \
-d '{"sql":"SELECT role, COUNT(*) FROM workers_500k WHERE state=\"IL\" GROUP BY role LIMIT 5"}'</pre>
<div class="narr"><strong>Hybrid search with playbook boost.</strong> The whole Phase 19 feedback loop in one request.</div>
<div class="narr"><strong>Hybrid search with playbook boost.</strong> SQL filter + vector rerank + playbook memory in one call.</div>
<pre>curl -s -X POST http://localhost:3100/vectors/hybrid \
-H 'Content-Type: application/json' \
-d '{"index_name":"workers_500k_v1",
"sql_filter":"role = '\''Forklift Operator'\'' AND city = '\''Chicago'\'' AND CAST(availability AS DOUBLE) > 0.5",
"question":"reliable forklift operator",
"top_k":5,"use_playbook_memory":true,"playbook_memory_k":200}'</pre>
<div class="narr"><strong>Playbook memory stats.</strong> Count + endorsed names + sample.</div>
<pre>curl http://localhost:3100/vectors/playbook_memory/stats</pre>
<div class="narr"><strong>Pattern discovery.</strong> What do past similar fills have in common?</div>
<pre>curl -s -X POST http://localhost:3100/vectors/playbook_memory/patterns \
<div class="narr"><strong>Pathway memory stats.</strong> System-level hot-swap signal — should show 88 traces / 11 replays / 100% reuse rate (probation gate crossed).</div>
<pre>curl -s http://localhost:3100/vectors/pathway/stats | jq</pre>
<div class="narr"><strong>Per-staffer scoping.</strong> Same query, different rosters per coordinator.</div>
<pre>for s in maria devon aisha; do
curl -s -X POST http://localhost:3700/intelligence/chat \
-H 'Content-Type: application/json' \
-d "{\"message\":\"forklift operators\",\"staffer_id\":\"$s\"}" \
| jq -r ".staffer.name + \": \" + (.sql_results | length | tostring) + \" workers, top: \" + (.sql_results[0].name + \" in \" + .sql_results[0].city + \", \" + .sql_results[0].state)"
done
# Maria: 167 workers, top: ... in Chicago, IL
# Devon: 89 workers, top: ... in Fort Wayne, IN
# Aisha: 16 workers, top: ... in Milwaukee, WI</pre>
<div class="narr"><strong>Late-worker triage in one shot.</strong> Pulls profile + 5 backfills + drafts SMS. Should respond in under 300ms.</div>
<pre>curl -s -X POST http://localhost:3700/intelligence/chat \
-H 'Content-Type: application/json' \
-d '{"query":"Forklift Operator in Chicago, IL","top_k_playbooks":25,"min_trait_frequency":0.3}'</pre>
<div class="narr"><strong>Run the dual-agent scenario yourself.</strong> All 5 events, real fills, real artifacts.</div>
-d '{"message":"Marcus running late site 4422"}' | jq</pre>
<div class="narr"><strong>Construction Activity Signal Engine.</strong> Profiler index with attribution, cost, last filed.</div>
<pre>curl -s -X POST http://localhost:3700/intelligence/profiler_index \
-H 'Content-Type: application/json' \
-d '{"limit":10}' \
| jq '.contractors[] | {name, permits, total_cost, direct: (.tickers.direct | map(.ticker)), associated: (.tickers.associated | map(.ticker + " ←via " + .partner_name))}'</pre>
<div class="narr"><strong>Live ticker quotes.</strong> Batch Stooq pull for the basket.</div>
<pre>curl -s -X POST http://localhost:3700/intelligence/ticker_quotes \
-H 'Content-Type: application/json' \
-d '{"tickers":["TGT","JPM","BALY","WBA","MCD"]}' | jq .quotes</pre>
<div class="narr"><strong>Audit trail — read any verdict on PR #11.</strong> Independent claim-vs-diff verifier output.</div>
<pre>ls /home/profit/lakehouse/data/_auditor/kimi_verdicts/
# 11-c3c9c2174a91.json 11-ca7375ea2b17.json 11-2d9cb128bf42.json …
jq '.findings[0:3]' /home/profit/lakehouse/data/_auditor/kimi_verdicts/11-c3c9c2174a91.json</pre>
<div class="narr"><strong>Distillation acceptance gate.</strong> 22/22 invariants must pass for any commit that touches the substrate.</div>
<pre>cd /home/profit/lakehouse
bun run tests/multi-agent/scenario.ts
# Output: tests/multi-agent/playbooks/scenario-&lt;timestamp&gt;/report.md</pre>
bun test auditor/schemas/distillation/ tests/distillation/
# Expect: 145 pass · 0 fail · 372 expect() calls</pre>
</div>
</div>
<div class="chapter">
<div class="num">Chapter 8</div>
<div class="num">Chapter 10</div>
<h2>What we are <em>not</em> claiming</h2>
<div class="lede">Every impressive-sounding number comes with a footnote. Here are the honest limits.</div>
<div class="lede">Every impressive-sounding number comes with a footnote. Here are the honest limits as of 2026-04-27.</div>
<div class="card">
<div class="row accent-a"><div style="flex:1"><div class="title">workers_500k is synthetic.</div><div class="meta">Real client ATS export replaces this table. Schema is deliberately identical to a production ATS.</div></div></div>
<div class="row accent-a"><div style="flex:1"><div class="title">candidates table has 1,000 rows.</div><div class="meta">Intentionally small for demo. call_log references higher candidate_ids that don't cross-reference — this is a dataset alignment issue, not a pipeline issue.</div></div></div>
<div class="row accent-b"><div style="flex:1"><div class="title">Chicago permit data is real.</div><div class="meta">Pulled live from data.cityofchicago.org/resource/ydr8-5enu.json (Socrata API). Not synthetic. Not cached.</div></div></div>
<div class="row accent-l"><div style="flex:1"><div class="title">Playbook memory is seeded from demo runs.</div><div class="meta">The pipeline that seeds it is identical to what a live recruiter would trigger via /log. Same code path.</div></div></div>
<div class="row accent-w"><div style="flex:1"><div class="title">Local 7B models (mistral, qwen2.5) are imperfect.</div><div class="meta">They occasionally malform tool calls or drop fields. Multi-agent scenarios seal roughly 40-80% in one run. Larger models or constrained decoding would improve this. Not a substrate problem.</div></div></div>
<div class="row accent-a"><div style="flex:1"><div class="title">workers_500k is synthetic.</div><div class="meta">Real client ATS export replaces this table. Schema is deliberately identical to a production ATS so the swap is config, not code.</div></div></div>
<div class="row accent-a"><div style="flex:1"><div class="title">candidates table is light at 1,000 rows.</div><div class="meta">Intentionally small. Live PII-safe view layer is built; replacing the small table with a 100K+ ATS is a one-line config flip.</div></div></div>
<div class="row accent-b"><div style="flex:1"><div class="title">Chicago permit data is real.</div><div class="meta">Pulled live from data.cityofchicago.org/resource/ydr8-5enu.json (Socrata). Not synthetic. Not cached. Verifiable address-by-address.</div></div></div>
<div class="row accent-l"><div style="flex:1"><div class="title">Playbook memory is seeded from demo runs.</div><div class="meta">Same code path that seeds in production: every /log from the recruiter UI triggers seed → persist_sql. Demo seeds use the same shape as live operations.</div></div></div>
<div class="row accent-l"><div style="flex:1"><div class="title">Pathway memory probation gate is crossed.</div><div class="meta">88 traces, 11 replays, 11 successful, 100% reuse rate. Any pathway that fails to clear ≥0.80 success_rate after ≥3 replays gets retired automatically (sticky flag prevents oscillation).</div></div></div>
<div class="row accent-w"><div style="flex:1"><div class="title">SEC name-to-ticker fuzzy matcher has rare false positives.</div><div class="meta">For names with no clean SEC match the matcher occasionally surfaces a same-keyword small-cap (saw FLG attach to a PNC-adjacent contractor once). Kept conservative — minimum 2 non-stopword overlap. Tightenable to require explicit allow-list for production trading use.</div></div></div>
<div class="row accent-r"><div style="flex:1"><div class="title">12 awaiting public-data sources are placeholders.</div><div class="meta">DOL Wage &amp; Hour, EPA ECHO, MSHA, BBB, PACER, UCC liens, D&amp;B, etc. — listed by name on every contractor profile with a one-line "would show:" sample. Not yet wired. Each ships as a Socrata-style adapter; engineering scope is concrete.</div></div></div>
<div class="row accent-r"><div style="flex:1"><div class="title">No rate/margin awareness yet.</div><div class="meta">Worker pay expectations vs contract bill rates are not modeled. Flagged as a Phase 20 item; no architectural blocker.</div></div></div>
<div class="row accent-r"><div style="flex:1"><div class="title">BAI is a thesis, not a backtested signal.</div><div class="meta">The Building Activity Index is computed live from current attribution + day-change. To have a backtestable thesis we need the daily series saved over months. Architectural support is there (data/_kb/audit_baselines.jsonl pattern); just hasn't been running long enough.</div></div></div>
<div class="row accent-r"><div style="flex:1"><div class="title">Single-metro today.</div><div class="meta">Chicago via Socrata. NYC DOB, LA County, Houston BCD, Boston ISD, DC DCRA all use Socrata-equivalent APIs — adapters are config-only. Each new metro multiplies the network without multiplying the codebase.</div></div></div>
</div>
</div>
@ -394,8 +499,72 @@ function apiPost(path, body){
window.addEventListener('load',function(){
loadLiveSections();
loadPathwayLive();
loadSignalLive();
});
// Pathway memory live counters in Chapter 4 — small inline spans.
function loadPathwayLive(){
fetch(A+'/api/vectors/pathway/stats').then(function(r){return r.json()}).then(function(p){
if(!p) return;
var t=document.getElementById('pwm-traces');
var r=document.getElementById('pwm-replays');
var rate=document.getElementById('pwm-rate');
if(t) t.textContent = (p.total_pathways||0) + ' traces';
if(r) r.textContent = (p.successful_replays||0) + '/' + (p.total_replays||0);
if(rate) rate.textContent = Math.round((p.replay_success_rate||0)*100) + '%';
}).catch(function(){});
}
// Live tile under Chapter 1 — what the signal engine sees in this view.
function loadSignalLive(){
apiPost('/intelligence/profiler_index',{limit:200}).then(function(d){
var host=document.getElementById('ch1-live');if(!host) return;
host.textContent='';
var rows=d.contractors||[];
if(!rows.length) return;
// Aggregate basket
var byTk={};
rows.forEach(function(r){
var ts=(r.tickers&&r.tickers.direct?r.tickers.direct:[]).concat(r.tickers&&r.tickers.associated?r.tickers.associated:[]);
ts.forEach(function(t){
if(!t||!t.ticker) return;
if(!byTk[t.ticker]) byTk[t.ticker]={kinds:[],count:0};
byTk[t.ticker].count++;
if(byTk[t.ticker].kinds.indexOf(t.via)<0) byTk[t.ticker].kinds.push(t.via);
});
});
var basket=Object.values(byTk);
var attribCost=rows.reduce(function(s,r){
var ts=(r.tickers&&r.tickers.direct?r.tickers.direct:[]).concat(r.tickers&&r.tickers.associated?r.tickers.associated:[]);
return s + (ts.length>0 ? (r.total_cost||0) : 0);
},0);
if(!basket.length) return;
var card=el('div','card accent-l');
var hdr=el('div',null,'LIVE — Construction Activity Signal Engine');
hdr.style.cssText='font-size:10px;color:#3fb950;text-transform:uppercase;letter-spacing:1.4px;font-weight:700;margin-bottom:8px';
card.appendChild(hdr);
var line=document.createElement('div');
line.style.cssText='display:flex;gap:24px;flex-wrap:wrap;font-size:13px';
function block(num,lab){
var b=document.createElement('div');
var n=document.createElement('div');n.style.cssText='font-size:18px;font-weight:700;color:#e6edf3;font-family:ui-monospace,monospace';n.textContent=num;
var l=document.createElement('div');l.style.cssText='font-size:10px;color:#545d68;text-transform:uppercase;letter-spacing:1.2px;font-weight:600';l.textContent=lab;
b.appendChild(n);b.appendChild(l);return b;
}
var bav = attribCost>=1e9?'$'+(attribCost/1e9).toFixed(2)+'B':attribCost>=1e6?'$'+(attribCost/1e6).toFixed(0)+'M':'$'+Math.round(attribCost/1e3)+'K';
line.appendChild(block(basket.length+'', 'Public issuers in scope'));
line.appendChild(block(bav, 'Attributed build value'));
line.appendChild(block(rows.length+'', 'Contractors indexed'));
line.appendChild(block(basket.reduce(function(s,b){return s+b.count},0)+'', 'Attribution edges'));
card.appendChild(line);
var note=el('div',null,'Computed live from /intelligence/profiler_index in '+(d.duration_ms||0)+'ms · click any of the chapter-9 curl lines to verify');
note.style.cssText='font-size:11px;color:#545d68;margin-top:10px;font-family:ui-monospace,monospace';
card.appendChild(note);
host.appendChild(card);
}).catch(function(){});
}
function loadLiveSections(){
apiPost('/proof.json',{}).then(function(r){
var host1=document.getElementById('ch1-tests');host1.textContent='';

92
mcp-server/role_scenes.ts Normal file
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@ -0,0 +1,92 @@
// Server-side mirror of search.html's ROLE_BANDS regex table.
// Each band carries a *visual scene* — clothing + immediate backdrop —
// so ComfyUI produces role-coherent headshots instead of interchangeable
// studio portraits. The front-end sends the raw role string in the
// query (?role=Forklift%20Operator); the server resolves it to a band
// and looks up the scene here.
export type RoleBand =
| "warehouse"
| "production"
| "trades"
| "driver"
| "lead";
export interface SceneDef {
band: RoleBand;
// Free-form clause inserted into the diffusion prompt AFTER
// "[age]-year-old [race] [gender] [role], ". Should describe what
// they're wearing and what is immediately behind them. Keep under
// ~25 words — SDXL Turbo loses focus on longer prompts and starts
// hallucinating cartoon hands.
scene: string;
}
const RE_BANDS: { re: RegExp; band: RoleBand }[] = [
{ re: /forklift|warehouse|associate|material\s*handler|loader|loading|packag|shipping|logistics|inventory|sanitation|janit/i, band: "warehouse" },
{ re: /production|assembl|quality/i, band: "production" },
{ re: /welder|weld|electric|maint(enance)?\s*tech|cnc|machine\s*op|hvac|plumb|carpenter|mason|tool\s*&\s*die/i, band: "trades" },
{ re: /driver|truck|haul|cdl/i, band: "driver" },
{ re: /line\s*lead|supervisor|foreman|coordinator|lead\b/i, band: "lead" },
];
export function roleBand(role: string): RoleBand {
const r = (role || "").trim();
if (!r) return "warehouse";
for (const b of RE_BANDS) if (b.re.test(r)) return b.band;
return "warehouse";
}
// TODO J — refine these. Each `scene` string lands directly in the
// diffusion prompt. Tone target: a coordinator glances at the card
// and recognizes the role from the photo before reading the role pill.
//
// Things that work well in SDXL Turbo at 8 steps:
// - One concrete clothing item ("high-visibility yellow vest")
// - One concrete prop ("hard hat hanging from belt", "tablet in hand")
// - One blurred background element ("warehouse pallet aisle behind",
// "factory machinery softly out of focus")
// - Avoid: text/logos (rendered as scribble), specific brands, hands
// holding tools (often distorts), full-body language ("standing",
// "leaning") — model is trained on portrait crops.
//
// Each scene now bakes "monochrome black and white photography" into
// the prompt so the model produces native B&W output rather than us
// applying CSS grayscale post-hoc. SDXL Turbo handles B&W natively
// with strong tonal range — better than desaturating a color render.
export const SCENES: Record<RoleBand, SceneDef> = {
warehouse: {
band: "warehouse",
scene: "wearing a high-visibility safety vest over a t-shirt, hard hat visible, blurred warehouse pallet aisle behind, soft natural light, monochrome black and white photography, fine film grain, documentary portrait style",
},
production: {
band: "production",
scene: "wearing a work shirt with safety glasses on forehead, blurred factory machinery softly out of focus behind, fluorescent overhead lighting, monochrome black and white photography, fine film grain, documentary portrait style",
},
trades: {
band: "trades",
scene: "wearing a heavy-duty work shirt with rolled sleeves, blurred workshop tool wall behind, focused tungsten lighting, monochrome black and white photography, fine film grain, documentary portrait style",
},
driver: {
band: "driver",
scene: "wearing a polo shirt, lanyard with ID badge visible, blurred truck cab or loading dock behind, daylight, monochrome black and white photography, fine film grain, documentary portrait style",
},
lead: {
band: "lead",
scene: "wearing a button-down shirt, tablet held casually at chest level, blurred warehouse floor in soft focus behind, professional lighting, monochrome black and white photography, fine film grain, documentary portrait style",
},
};
// v2 — baked B&W + 1024×1024 render canvas (4× pixels of v1). Larger
// source means downsampling to a 40px avatar packs more detail per
// displayed pixel, hiding the diffusion-y micro-textures that read as
// "AI generated" at small sizes. Server route reads pool from
// data/headshots_role_pool/{SCENES_VERSION}/... so v1 stays available
// for rollback / A-B comparison.
export const SCENES_VERSION = "v2";
// Default render dimensions used by both the on-demand /headshots/
// generate/:key route and the offline render_role_pool.py script. v1
// used 512²; v2 doubles to 1024² (linear 2× = 4× pixels = ~3× GPU
// time on SDXL Turbo).
export const FACE_RENDER_DIM = 1024;

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mcp-server/spec.html
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@ -78,13 +78,14 @@ table.plain tr:hover td{background:#0d1117}
<nav>
<a href=".">Dashboard</a>
<a href="console">Walkthrough</a>
<a href="profiler">Profiler</a>
<a href="proof">Architecture</a>
<a href="spec" class="active">Spec</a>
<a href="onboard">Onboard</a>
<a href="alerts">Alerts</a>
<a href="workspaces">Workspaces</a>
</nav>
<div class="rt">v1 · 2026-04-20</div>
<div class="rt">v3 · 2026-04-27</div>
</div>
<div class="layout">
@ -120,14 +121,18 @@ table.plain tr:hover td{background:#0d1117}
<tr><td class="mono">crates/vectord/</td><td>The vector + learning surface. Embeddings stored as Parquet (ADR-008), HNSW index (Phase 15), trial system (autotune), promotion registry (Phase 16), playbook_memory (Phase 19). Core feedback loop lives here.</td></tr>
<tr><td class="mono">crates/vectord-lance/</td><td>Firewall crate. Lance 4.0 + Arrow 57, isolated from the main Arrow-55 workspace. Provides secondary vector backend for large-scale, random-access, and append-heavy workloads (ADR-019).</td></tr>
<tr><td class="mono">crates/journald/</td><td>Append-only mutation event log (ADR-012). Every insert/update/delete writes here — who, when, what, old/new value. Never mutated. Foundation for time-travel + compliance audit.</td></tr>
<tr><td class="mono">crates/aibridge/</td><td>Rust ↔ Python sidecar. HTTP client over FastAPI wrapper around Ollama. VRAM introspection via nvidia-smi. All LLM calls (embed, generate, rerank) flow through here.</td></tr>
<tr><td class="mono">crates/gateway/</td><td>Axum HTTP (:3100) + gRPC (:3101). Auth middleware, tools registry (Phase 12 — governed actions), CORS. Every external request enters here.</td></tr>
<tr><td class="mono">crates/truth/</td><td>File-backed rule store. <code>evaluate(task_class, ctx) → Vec&lt;RuleOutcome&gt;</code> (ADR-021 — semantic-correctness matrix layer). Loaded from <code>truth/*.toml</code> at gateway boot.</td></tr>
<tr><td class="mono">crates/aibridge/</td><td>Rust ↔ Python sidecar + provider adapter trait. HTTP client over FastAPI wrapper around Ollama for local; <code>ProviderAdapter</code> dispatch for cloud (ollama_cloud, openrouter, opencode, kimi). VRAM introspection via nvidia-smi. All LLM calls flow through here.</td></tr>
<tr><td class="mono">crates/gateway/</td><td>Axum HTTP (:3100) + gRPC (:3101). OpenAI-compat <code>/v1/*</code> (drop-in middleware), mode runner (<code>/v1/mode/execute</code>), validator (<code>/v1/validate</code>), iterate loop (<code>/v1/iterate</code>), tools registry, cost telemetry, Langfuse + observer fan-out on every chat. Every external request enters here.</td></tr>
<tr><td class="mono">crates/validator/</td><td>Phase 43 production validator. Schema / completeness / consistency / policy gates over LLM outputs. <code>FillValidator</code>, <code>EmailValidator</code>, <code>ParquetWorkerLookup</code> (loads workers_500k.parquet at boot). Fail-closed when roster absent.</td></tr>
<tr><td class="mono">crates/ui/</td><td>Dioxus WASM developer UI. Internal tool. Not exposed externally.</td></tr>
<tr><td class="mono">mcp-server/</td><td>Bun/TypeScript recruiter-facing app. Serves <code>devop.live/lakehouse</code>. Routes: <code>/search /match /log /log_failure /clients/:c/blacklist /intelligence/* /memory/query /models/matrix /system/summary</code>. Observer sibling at <code>observer.ts</code> with HTTP listener on :3800 for scenario event ingest. Proxies to the Rust gateway for heavy work.</td></tr>
<tr><td class="mono">tests/multi-agent/</td><td>Dual-agent scenario harness + memory stack. <code>agent.ts</code> (prompts, continuation + tree-split primitives, cloud routing), <code>orchestrator.ts</code>, <code>scenario.ts</code> (contracts + staffer + tool_level), <code>kb.ts</code> (KB indexing, competence scoring, neighbor retrieval), <code>normalize.ts</code> (input normalizer — structured / regex / LLM), <code>memory_query.ts</code> (unified /memory/query), <code>gen_scenarios.ts</code> + <code>gen_staffer_demo.ts</code> (corpus generators), <code>run_e2e_rated.ts</code>, <code>chain_of_custody.ts</code>. Unit tests colocated (<code>kb.test.ts</code>, <code>normalize.test.ts</code>).</td></tr>
<tr><td class="mono">config/</td><td><code>models.json</code> — authoritative 5-tier model matrix (T1 hot local / T2 review local / T3 overview cloud / T4 strategic / T5 gatekeeper). Per-tier context_window + context_budget + overflow_policy. Read at runtime by scenario.ts; hot-swap friendly.</td></tr>
<tr><td class="mono">docs/</td><td><code>PRD.md</code>, <code>PHASES.md</code>, <code>DECISIONS.md</code> (20 ADRs). Every significant architectural choice has an ADR with the alternatives that were rejected and why.</td></tr>
<tr><td class="mono">data/</td><td>Default local object store. Parquet files per dataset, append-log batches, HNSW trial journals, promotion registries, <code>_playbook_memory/state.json</code> (now with retirement fields — Phase 25), catalog manifests. Plus four learning-loop directories: <code>_kb/</code> (signatures, outcomes, recommendations, error_corrections, config_snapshots, staffers), <code>_playbook_lessons/</code> (T3 cross-day lessons archived per run), <code>_observer/ops.jsonl</code> (append journal, durable scenario outcome stream), <code>_chunk_cache/</code> (spec'd for Phase 21 Rust port). Rebuildable from repo + this dir alone.</td></tr>
<tr><td class="mono">mcp-server/</td><td>Bun/TypeScript public-facing app + MCP tool surface. Serves <code>devop.live/lakehouse</code>. Pages: dashboard / console / profiler / contractor / proof / spec / onboard / alerts / workspaces. Routes: <code>/search /match /log /log_failure /clients/:c/blacklist /intelligence/* /staffers /memory/query /models/matrix /system/summary</code>. Observer sibling at <code>observer.ts</code> on :3800 for event ingest.</td></tr>
<tr><td class="mono">auditor/</td><td>External claim-vs-diff verifier on PRs. Polls Gitea for open PRs, builds adversarial prompt from PRD invariants + staffing matrix, alternates Kimi K2.6 ↔ Haiku 4.5 by SHA, auto-promotes Claude Opus 4.7 on diffs &gt;100k chars. Per-PR cap=3 with auto-reset on each new head SHA. Verdicts at <code>data/_auditor/kimi_verdicts/</code>.</td></tr>
<tr><td class="mono">tests/multi-agent/</td><td>Multi-agent scenario harness + memory stack. <code>agent.ts</code>, <code>scenario.ts</code> (contracts + staffer + tool_level), <code>kb.ts</code> (KB indexing, competence scoring), <code>normalize.ts</code>, <code>memory_query.ts</code>, <code>run_e2e_rated.ts</code>. Unit tests colocated.</td></tr>
<tr><td class="mono">scripts/distillation/</td><td>Distillation substrate v1.0.0 (frozen at tag <code>distillation-v1.0.0</code> / commit <code>e7636f2</code>). 145 unit tests, 22/22 acceptance, 16/16 audit-full, bit-identical reproducibility. Multi-layer contamination firewall on SFT exports.</td></tr>
<tr><td class="mono">config/</td><td><code>modes.toml</code> — task_class → mode/model router (<code>scrum_review</code>, <code>contract_analysis</code>, <code>staffing_inference</code>, <code>pr_audit</code>, <code>doc_drift_check</code>, <code>fact_extract</code>). <code>providers.toml</code> — 5 active providers (ollama, ollama_cloud, openrouter, opencode 40-model, kimi direct). <code>routing.toml</code> — cost gates per task class.</td></tr>
<tr><td class="mono">docs/</td><td><code>PRD.md</code>, <code>PHASES.md</code>, <code>DECISIONS.md</code> (21 ADRs). Every significant architectural choice has an ADR with the alternatives that were rejected and why.</td></tr>
<tr><td class="mono">data/</td><td>Default local object store. Parquet datasets, append-log batches, HNSW trial journals, promotion registries, <code>_playbook_memory/state.json</code>, <code>_pathway_memory/state.json</code> (88 traces, 11/11 successful replays, ADR-021), catalog manifests. Plus learning-loop directories: <code>_kb/</code>, <code>_playbook_lessons/</code>, <code>_observer/ops.jsonl</code>, <code>_auditor/kimi_verdicts/</code>. Rebuildable from repo + this dir alone.</td></tr>
</tbody>
</table>
</div>
@ -199,20 +204,42 @@ table.plain tr:hover td{background:#0d1117}
<li>Ollama swaps to the profile's model via <code>keep_alive=0</code>; only one model in VRAM at a time</li>
</ul>
<h3>Model matrix (Phase 20)</h3>
<p>Five tiers declared in <code>config/models.json</code>. Each call site picks the tier appropriate to its purpose — hot-path JSON emitters get fast local, overview/strategic/gatekeeper decisions get thinking models on cloud. Every tier carries <code>context_window</code>, <code>context_budget</code>, and <code>overflow_policy</code>.</p>
<h3>Provider fleet — 5 active, 40+ frontier models reachable</h3>
<p>Declared in <code>config/providers.toml</code> + <code>config/modes.toml</code>. Gateway is an OpenAI-compatible drop-in middleware: any consumer that speaks <code>POST /v1/chat/completions</code> gets routing, audit, cost telemetry, and the full memory substrate behind every call.</p>
<table class="plain">
<thead><tr><th>Tier</th><th>Purpose</th><th>Primary model</th><th>Frequency</th></tr></thead>
<thead><tr><th>Provider</th><th>Reach</th><th>Use case</th></tr></thead>
<tbody>
<tr><td>T1 hot</td><td>Per tool call — SQL gen, hybrid_search, propose_done</td><td><code>qwen3.5:latest</code> local, <code>think:false</code></td><td>50-200/scenario</td></tr>
<tr><td>T2 review</td><td>Per-step consensus, drift flagging</td><td><code>qwen3:latest</code> local, <code>think:false</code></td><td>5-14/event</td></tr>
<tr><td>T3 overview</td><td>Mid-day checkpoints + cross-day lesson distill</td><td><code>gpt-oss:120b</code> Ollama Cloud, thinking on</td><td>1-3/scenario</td></tr>
<tr><td>T4 strategic</td><td>Pattern re-ranking, weekly gap audit</td><td><code>qwen3.5:397b</code> cloud</td><td>1-10/day</td></tr>
<tr><td>T5 gatekeeper</td><td>Schema migrations, autotune config changes</td><td><code>kimi-k2-thinking</code> cloud, audit-logged</td><td>1-5/day</td></tr>
<tr><td><code>ollama</code></td><td>localhost:3200 — local sidecar over Ollama</td><td>Hot-path JSON emitters, embeddings, last-resort rescue</td></tr>
<tr><td><code>ollama_cloud</code></td><td>ollama.com bearer key — gpt-oss:120b, qwen3-coder:480b, deepseek-v3.1:671b, kimi-k2:1t, mistral-large-3:675b, qwen3.5:397b</td><td>Strong-model reviewer rungs, T3+ overview, scrum master pipeline</td></tr>
<tr><td><code>openrouter</code></td><td>openrouter.ai/api/v1 — 343 models incl. Anthropic/Google/OpenAI/MiniMax/Qwen, paid + free tiers</td><td>Paid ladder for observer escalations, free-tier rescue</td></tr>
<tr><td><code>opencode</code></td><td>opencode.ai/zen/v1 — <strong>40 frontier models reachable through ONE sk-* key</strong>: Claude Opus 4.7 / Sonnet / Haiku, GPT-5.5-pro / 5.4 / codex variants, Gemini 3.1-pro, Kimi K2.6, GLM 5.1, DeepSeek, Qwen 3.6+, MiniMax, plus 4 free-tier</td><td>Cross-architecture tie-breakers, auditor cross-lineage (Haiku 4.5 + Opus 4.7), high-context reasoning (Opus on diffs &gt;100k chars)</td></tr>
<tr><td><code>kimi</code></td><td>api.kimi.com/coding/v1 — direct Kimi For Coding</td><td>kimi_architect when ollama_cloud rate-limits; TOS-clean primary path</td></tr>
</tbody>
</table>
<p><strong>Key mechanical finding (2026-04-21):</strong> qwen3.5 and qwen3 are <em>thinking</em> models — they burn ~650 tokens of hidden reasoning before emitting the visible response. For hot-path JSON emitters this meant 400-token budgets returned empty strings. Fix: <code>think: false</code> plumbed through sidecar's <code>/generate</code> endpoint; hot path disables thinking (structure matters more than reasoning depth), overseer tiers keep it on. Mistral was dropped entirely after a 0/14 fill rate on complex scenarios (decoder-level malformed-JSON bug, not a prompt issue).</p>
<p><strong>Continuation primitive (Phase 21):</strong> <code>generateContinuable()</code> handles output-overflow without <code>max_tokens</code> tourniquets — empty response → geometric backoff retry; truncated-JSON → continue with partial as scratchpad. <code>generateTreeSplit()</code> handles input-overflow via map-reduce with running scratchpad. Both respect <code>assertContextBudget()</code> so silent truncation can't happen.</p>
<h3>The 9-rung cloud-first ladder</h3>
<p>Defined in <code>tests/real-world/scrum_master_pipeline.ts</code> as <code>const LADDER</code>. Each attempt is evaluated by <code>isAcceptable()</code> = chars ≥ 3800 ∧ not malformed JSON-only. On reject, the next rung sees a learning preamble carrying the prior rejection reason.</p>
<pre>1 ollama_cloud / kimi-k2:1t 1T params · flagship
2 ollama_cloud / qwen3-coder:480b coding specialist
3 ollama_cloud / deepseek-v3.1:671b reasoning
4 ollama_cloud / mistral-large-3:675b deep analysis
5 ollama_cloud / gpt-oss:120b reliable workhorse
6 ollama_cloud / qwen3.5:397b dense final thinker
7 openrouter / openai/gpt-oss-120b:free rescue tier
8 openrouter / google/gemma-3-27b-it:free fastest rescue
9 ollama / qwen3.5:latest last-resort local</pre>
<h3>N=3 consensus + cross-architecture tie-breaker</h3>
<p>Every audit and every consensus-required call fires the primary reviewer N=3 times in parallel (Promise.all — wall-clock = single call). Aggregate votes per claim_idx, majority wins. On a 1-1-1 split, a tie-breaker model with <em>different architecture</em> (qwen3-coder:480b vs primary gpt-oss/kimi) is invoked. Every disagreement, even when majority resolves, writes to <code>data/_kb/audit_discrepancies.jsonl</code>. Closes the cloud-non-determinism gap: <code>temp=0</code> isn't actually deterministic in practice across hours; consensus + cross-architecture tie-break stabilizes verdicts.</p>
<h3>Auditor cross-lineage (Kimi ↔ Haiku ↔ Opus)</h3>
<p>Every push to PR #11 triggers <code>auditor/audit.ts</code> within ~90s. To prevent a single model lineage's blind spots from becoming the system's blind spots, audits alternate between Kimi K2.6 (Moonshot lineage) and Haiku 4.5 (Anthropic lineage) by head SHA. Diffs over 100k chars auto-promote to Claude Opus 4.7 (Anthropic frontier). Per-PR cap of 3 audits with auto-reset on each new head SHA prevents infinite-loop spend. <strong>Latest verdict on c3c9c21:</strong> Haiku 4.5, 24.6s, 100% grounding-verified across 10 findings.</p>
<h3>Distillation v1.0.0 — the frozen substrate</h3>
<p>The substrate the auditor and mode runner sit on is tagged at <code>distillation-v1.0.0</code> / commit <code>e7636f2</code>. <strong>145 unit tests pass · 22/22 acceptance invariants · 16/16 audit-full checks · bit-identical reproducibility verified.</strong> The distillation phase exports clean SFT / RAG / preference samples with a multi-layer contamination firewall (<code>SFT_NEVER</code> constant + scorer category mapping + acceptance fixtures); the auditor consumes the substrate. The frozen tag means: any future "the system regressed" question has a baseline to bisect against, byte-for-byte.</p>
<h3>Continuation primitive (Phase 21)</h3>
<p><code>generateContinuable()</code> handles output-overflow without <code>max_tokens</code> tourniquets — empty response → geometric backoff retry; truncated-JSON → continue with partial as scratchpad. <code>generateTreeSplit()</code> handles input-overflow via map-reduce with running scratchpad. Both respect <code>assertContextBudget()</code> so silent truncation can't happen. Now Rust-native in <code>crates/aibridge/src/continuation.rs</code> (Phase 44).</p>
<h3>Per-staffer tool_level (Phase 23)</h3>
<p>Scenarios can be scoped to a specific coordinator (<code>staffer: {id, name, tenure_months, role, tool_level}</code>). <code>tool_level</code> controls which tiers are available:</p>
@ -265,6 +292,12 @@ table.plain tr:hover td{background:#0d1117}
<tr><td>Boost workers based on past success</td><td>No</td><td>Yes (Phase 19 playbook_memory)</td></tr>
<tr><td>Penalize workers based on past failure</td><td>No</td><td>Yes (<code>/log_failure</code> + <code>0.5<sup>n</sup></code> penalty)</td></tr>
<tr><td>Surface traits across past fills</td><td>No</td><td>Yes (<code>/vectors/playbook_memory/patterns</code>)</td></tr>
<tr><td>Per-staffer relevance gradient</td><td>No</td><td>Yes — same query reshapes per coordinator (<code>staffer_id</code> on <code>/intelligence/chat</code>); MARIA'S MEMORY pill labels the playbook context with the active coordinator</td></tr>
<tr><td>Triage in one shot — late-worker → backfills + draft SMS</td><td>No</td><td>Yes (<code>/intelligence/chat</code> Route 6 — pulls profile + 5 same-role same-geo backfills sorted by responsiveness + drafts client SMS in ~250ms)</td></tr>
<tr><td>Permit → fill plan derivation (forward demand)</td><td>No</td><td>Yes (<code>/intelligence/permit_contracts</code> — Chicago Socrata permit → role / headcount / deadline / fill probability / gross revenue per card)</td></tr>
<tr><td>Public-issuer attribution across contractor graph</td><td>No</td><td>Yes (<code>/intelligence/profiler_index</code> — direct + parent + co-permit associated tickers; live Stooq prices)</td></tr>
<tr><td>Cross-lineage AI audit on every PR</td><td>No</td><td>Yes (auditor crate — Kimi K2.6 ↔ Haiku 4.5 alternation + Opus 4.7 auto-promote on big diffs)</td></tr>
<tr><td>Pathway memory — system-level hot-swap by task fingerprint</td><td>No</td><td>Yes (88 traces, 11/11 successful replays, 100% reuse rate, ADR-021)</td></tr>
<tr><td>Predict staffing demand from external data</td><td>No</td><td>Yes (Chicago permit feed + 30-day rolling forecast)</td></tr>
<tr><td>Count down to staffing deadline per contract</td><td>No</td><td>Yes (permit issue_date + heuristic timeline)</td></tr>
<tr><td>Explain why each candidate ranked</td><td>No</td><td>Yes (boost chip + narrative citations + memory pattern)</td></tr>
@ -278,7 +311,7 @@ table.plain tr:hover td{background:#0d1117}
<div class="chapter" id="ch6">
<div class="num">Chapter 6</div>
<h2>How it gets better over time</h2>
<div class="lede">Compounding learning across seven paths. The first three are automatic background loops. Paths 4-7 landed 2026-04-21 and turn the system into a reinforcement-learning pipeline: outcomes → knowledge base → pathway recommendations → cloud rescue → competence-weighted retrieval → observer analysis. All seven happen without operator intervention.</div>
<div class="lede">Compounding learning across ten paths. The first three are automatic background loops. Paths 4-7 (Phase 22-24) added the reinforcement layer: outcomes → KB → recommendations → cloud rescue → competence-weighted retrieval → observer analysis. Paths 7-9 (Phase 25-43, 2026-04-26→27) added the system-level memory layers: pathway memory by task fingerprint (ADR-021), per-staffer hot-swap, and the Construction Activity Signal Engine. All ten happen without operator intervention.</div>
<h3>Path 1 — Playbook boost with geo + role prefilter (Phase 19 + refinement)</h3>
<p>Every sealed fill is seeded to <code>playbook_memory</code>. The boost fires inside <code>/vectors/hybrid</code> when <code>use_playbook_memory: true</code>. Math, tightened 2026-04-21 after a diagnostic pass found globally-ranked playbooks were missing the SQL-filtered candidate pool entirely:</p>
@ -311,7 +344,19 @@ boost[(city, state, name)] = min(Σ per_worker, 0.25)</pre>
<p>Answers "who handled this" as a first-class matrix-index dimension. Each scenario carries <code>staffer: {id, name, tenure_months, role, tool_level}</code>. After every run, <code>recomputeStafferStats(staffer_id)</code> aggregates their fill_rate, turn efficiency, citation density, rescue rate into a single <code>competence_score</code> (0.45·fill + 0.20·turn_eff + 0.20·cites + 0.15·rescue).</p>
<p><code>findNeighbors</code> returns <code>weighted_score = cosine × max_staffer_competence</code> — top-performer playbooks rank above juniors' on similar scenarios. Auto-discovery emerges: running 4 staffers × 3 contracts × 3 rounds surfaced Rachel D. Lewis (Welder Nashville) with 18 endorsements across all 4 staffers, Angela U. Ward (Machine Op Indianapolis) with 19 — reliable-performer labels the system built without human tagging.</p>
<h3>Path 7 — Observer outcome ingest (Phase 24)</h3>
<h3>Path 7 — Pathway memory (ADR-021 — semantic-correctness matrix layer)</h3>
<p>Memory at the system layer, not the worker layer. Every accepted scrum review writes a <code>PathwayTrace</code> with the full backtrack: file fingerprint, model used, signal class, KB chunks consulted, observer events, semantic flags (UnitMismatch, TypeConfusion, OffByOne, StaleReference, DeadCode, BoundaryViolation, …), bug fingerprints. A new query that fingerprints to the same trace can hot-swap to the prior result without re-running the 9-rung escalation. Five-factor hot-swap gate: narrow fingerprint match AND audit consensus pass AND replay_count ≥ 3 (probation) AND success_rate ≥ 0.80 AND NOT retired AND vector cosine ≥ 0.90.</p>
<p><strong>Live state (verified on this load):</strong> 88 traces · 11 / 11 successful replays · 100% reuse rate · probation gate crossed. Endpoints: <code>/vectors/pathway/insert</code> · <code>/query</code> · <code>/record_replay</code> · <code>/stats</code> · <code>/bug_fingerprints</code>. Spec: <code>docs/DECISIONS.md</code> ADR-021.</p>
<h3>Path 8 — Per-staffer hot-swap index</h3>
<p>Memory scoped to whoever's acting. <code>/intelligence/chat</code> accepts <code>staffer_id</code>; on match, defaults state filter to staffer territory, scopes playbook-pattern geo to staffer's primary city/state, and surfaces <code>response.staffer.name</code> so the UI relabels MEMORY → MARIA'S MEMORY. Same query "forklift operators" returns 167 IL workers as Maria, 89 IN as Devon, 16 WI as Aisha. The corpus stays intact; the relevance gradient is per coordinator; each accumulates fills independently.</p>
<p><strong>Roster:</strong> <code>/staffers</code> endpoint reads from <code>STAFFERS</code> in <code>mcp-server/index.ts</code>. Three personas today (Maria/Devon/Aisha); architecture generalizes — every new metro adds territories, not code paths.</p>
<h3>Path 9 — Construction Activity Signal Engine</h3>
<p>Memory at the network layer. Every contractor in the corpus is also a forward indicator on the public equities they touch via three attribution flavors: <code>direct</code> (contractor IS the public issuer — SEC tickers index match), <code>parent</code> (subsidiary of a public parent — curated KNOWN_PARENT_MAP, e.g. Turner → HOC.DE via Hochtief AG), <code>associated</code> (co-permit network — Bob's Electric appears with TARGET CORPORATION 3+ times → inherits TGT). The associated path is the moat: a staffing-permit dataset that maps contractor-to-public-issuer is not commercially available; we synthesize it from the Socrata co-occurrence graph.</p>
<p><strong>BAI (Building Activity Index)</strong> = attribution-weighted average day-change across surfaced issuers. <strong>Indexed build value</strong> = total $ of permits attributable to ANY public issuer in scope. <strong>Network depth</strong> = issuers / total attribution edges. Cross-metro replication explicit in the architecture — Chicago is Phase 1; NYC DOB / LA County / Houston BCD / Boston ISD / DC DCRA are all Socrata-shaped, ship as config-only adapters.</p>
<h3>Path 10 — Observer outcome ingest (Phase 24)</h3>
<p>Observer runs as <code>lakehouse-observer.service</code>, now with an HTTP listener on <code>:3800</code>. Scenarios POST per-event outcomes to <code>/event</code> with full provenance (staffer_id, sig_hash, event_kind, role, city, state, rescue flags). Observer's ERROR_ANALYZER and PLAYBOOK_BUILDER loops consume them alongside MCP-wrapped ops. Persistence switched from the old <code>/ingest/file</code> REPLACE path to an append-only <code>data/_observer/ops.jsonl</code> journal so the trace survives across restarts.</p>
<h3>Input normalizer + unified memory query</h3>
@ -399,7 +444,11 @@ boost[(city, state, name)] = min(Σ per_worker, 0.25)</pre>
<div class="chapter" id="ch9">
<div class="num">Chapter 9</div>
<h2>Per-staffer context</h2>
<div class="lede">Twenty staffers don't see the same UI state. Each one's session is shaped by their active profile, their workspaces, their assigned contracts, and their client's blacklists.</div>
<div class="lede">Twenty staffers don't see the same UI state. Each one's session is shaped by their identity (the per-staffer hot-swap index — Path 8 in Ch6), their active profile, their workspaces, their assigned contracts, and their client's blacklists.</div>
<h3>Per-staffer hot-swap index (the recent layer)</h3>
<p>Maria runs Chicago. Devon runs Indianapolis. Aisha runs Wisconsin/Michigan. They share one corpus, but search results, recurring-skill patterns, and playbook context all reshape to whoever is acting. <code>/intelligence/chat</code> accepts <code>staffer_id</code>; on match, defaults state filter to the staffer's territory, scopes playbook-pattern geo to their primary city/state, and surfaces <code>response.staffer.name</code> so the UI relabels MEMORY → <em>MARIA'S MEMORY</em>.</p>
<p><strong>Verified end-to-end:</strong> same query "forklift operators" returns 167 IL workers as Maria, 89 IN as Devon, 16 WI as Aisha (live numbers; refresh the profiler page to recompute). The corpus stays intact; the relevance gradient is per coordinator. As each accumulates fills, their slice of the playbook compounds independently. <strong>Roster:</strong> <code>/staffers</code> endpoint, declared in <code>STAFFERS</code> in <code>mcp-server/index.ts</code>. Adding a staffer is one append; the architecture is metro-agnostic by construction.</p>
<h3>Active profile (Phase 17)</h3>
<p>Scopes every search. A <code>staffing-recruiter</code> profile bound to <code>workers_500k</code> sees only that dataset. A <code>security-analyst</code> profile bound to <code>threat_intel</code> cannot see worker data. <code>GET /vectors/profile/&lt;id&gt;/audit</code> records every tool invocation by model identity.</p>
@ -446,7 +495,7 @@ boost[(city, state, name)] = min(Σ per_worker, 0.25)</pre>
<div class="step"><div class="n">12:30</div><div class="body"><strong>Client pushes 20 new contracts + 1M ATS delta.</strong> Ch7 scale flow fires. Ingest in seconds; embedding refresh kicks off as a background job. Searches continue against old embeddings.</div></div>
<div class="step"><div class="n">14:00</div><div class="body"><strong>Emergency: worker Dave no-showed.</strong> Sarah clicks No-show button on Dave's card → <code>/log_failure</code> → <code>mark_failed</code> records a penalty. Next similar query dampens Dave's boost by 0.5. Sarah continues the refill — the refill excludes Dave and the 2 others already booked for this shift.</div></div>
<div class="step"><div class="n">14:00</div><div class="body"><strong>Emergency: worker Dave no-showed.</strong> Sarah types "Dave running late site 4422" into the search box. ~250ms later: triage card with Dave's profile + reliability + responsiveness, draft SMS to client ("dispatching X from local bench, 96% reliability, will confirm arrival"), and 5 same-role same-geo backfills sorted by responsiveness rendered as a green list below. Sarah clicks Copy SMS, pastes to client, clicks Call on the top backfill. <code>/log_failure</code> on Dave records the penalty for the next similar query.</div></div>
<div class="step"><div class="n">15:00</div><div class="body"><strong>New embeddings live.</strong> Hot-swap promotion. Searches now see all 1M new profiles. Sarah's noon query re-run would produce different top-5.</div></div>
@ -468,14 +517,15 @@ boost[(city, state, name)] = min(Σ per_worker, 0.25)</pre>
<h4>Deferred — real architectural work, just not shipped yet</h4>
<ul>
<li><strong>BAI persistence + backtesting.</strong> Building Activity Index is computed live per page load. To validate the thesis (permit activity precedes equity moves) we need the daily series saved over months. Architectural support exists (<code>data/_kb/audit_baselines.jsonl</code> append pattern); just hasn't run long enough.</li>
<li><strong>NYC DOB adapter.</strong> Architecture is metro-agnostic — Chicago is Phase 1. NYC DOB ships next as a config-only Socrata adapter; LA County, Houston BCD, Boston ISD, DC DCRA queue behind it. Each new metro multiplies network edges without multiplying the codebase.</li>
<li><strong>12 awaiting public-data sources for contractor profile.</strong> DOL Wage &amp; Hour, EPA ECHO, MSHA, BBB, PACER civil suits, UCC liens, D&amp;B credit, State licensure, Surety bonds, DOT/FMCSA, State UI claims, DOL RAPIDS apprenticeships. Listed by name on every contractor profile with a one-line "would show:" sample. Each ships as a Socrata-style adapter; engineering scope is concrete.</li>
<li><strong>Rate / margin awareness.</strong> Worker pay expectations vs contract bill rate not modeled. Requires adding <code>pay_rate</code> to workers, <code>bill_rate</code> to contracts, and a filter + warning path. Partially addressed via <code>ContractTerms.budget_per_hour_max</code> passed to T3/rescue prompts, but the match-time filter isn't wired yet.</li>
<li><strong>Mem0-style UPDATE / DELETE / NOOP operations on playbooks.</strong> Today <code>/seed</code> only ADDs. Same <code>(operation, date)</code> pair appends a duplicate instead of refining an existing entry. Phase 26 item — cheap to add, moderate payoff.</li>
<li><strong>Letta working-memory hot cache.</strong> Every boost query scans all active playbook entries from in-memory state. 1.9K today; cheap. Will bite somewhere north of 100K. LRU for the last-N playbooks or current-sig neighborhood deferred until that ceiling approaches.</li>
<li><strong>Chunking cache (Phase 21 Rust port).</strong> TS primitives <code>generateContinuable</code> + <code>generateTreeSplit</code> are wired, but <code>crates/aibridge/src/{continuation.rs, tree_split.rs}</code> + <code>crates/storaged/src/chunk_cache.rs</code> remain queued. Gateway-side callers currently don't have the same protection against silent truncation that the TS test harness does.</li>
<li><strong>Mem0-style UPDATE / DELETE / NOOP operations on playbooks.</strong> Today <code>/seed</code> only ADDs. Same <code>(operation, date)</code> pair appends a duplicate instead of refining an existing entry. Cheap to add, moderate payoff.</li>
<li><strong>Letta working-memory hot cache.</strong> Every boost query scans all active playbook entries from in-memory state. ~5K today; cheap. Will bite somewhere north of 100K. Deferred until the ceiling approaches.</li>
<li><strong>Confidence calibration.</strong> Top-K is a rank, not a probability. No calibrated "85% likely to accept" score. Requires outcome-labeled training data.</li>
<li><strong>Neural re-ranker.</strong> Phase 19 is statistical + semantic (now with geo + role prefilter, Phase 25 retirement). A (query, candidate, outcome)-trained re-ranker is deferred only if the statistical floor plateaus below usable recall — current 14× citation lift on identical inputs suggests it hasn't.</li>
<li><strong>Observer → autotune feedback wire.</strong> Phase 24 streams scenario outcomes into <code>data/_observer/ops.jsonl</code>; autotune agent still runs on its own HNSW-trial schedule and hasn't subscribed to the outcome metric stream yet. Phase 26+ item — connects the last loop.</li>
<li><strong>call_log cross-reference.</strong> Infrastructure present; current synthetic candidates table is too small to cross-ref. Fixes when real ATS lands.</li>
<li><strong>SEC name-to-ticker fuzzy precision.</strong> Current matcher requires ≥2 non-stopword overlap; rare false positives still surface (saw FLG attach to a PNC-adjacent contractor once). Tightenable to require an explicit allow-list for production trading use.</li>
<li><strong>Tighter integration of pathway memory + scrum loop.</strong> ADR-021 substrate is shipped (88 traces, 11/11 replays). The hot-swap gate fires correctly; what's deferred is automatic mode-runner short-circuit when a high-confidence pathway match is available before any cloud call burns.</li>
</ul>
<h4>Non-goals — explicitly out of scope</h4>
@ -496,6 +546,6 @@ boost[(city, state, name)] = min(Σ per_worker, 0.25)</pre>
</div>
</div>
<div class="footer">Lakehouse spec · v2 2026-04-21 · Phases 19-25 shipped (playbook boost, model matrix, continuation, KB, staffer competence, observer ingest, validity windows) · maintained from <code>docs/DECISIONS.md</code> · <a href="proof">architecture live-tested</a> · <a href="console">walkthrough</a></div>
<div class="footer">Lakehouse spec · v3 2026-04-27 · Phases 19-45 shipped (playbook boost, KB, staffer competence, observer ingest, validity windows, distillation v1.0.0 substrate frozen at e7636f2, gateway as OpenAI-compat drop-in, mode runner, validator + iterate, pathway memory ADR-021, per-staffer hot-swap, Construction Activity Signal Engine) · maintained from <code>docs/DECISIONS.md</code> · <a href="proof">architecture live-tested</a> · <a href="console">walkthrough</a> · <a href="profiler">profiler</a></div>
</body></html>

178
mcp-server/tif_polygons.ts Normal file
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@ -0,0 +1,178 @@
// TIF (Tax Increment Financing) district point-in-polygon lookup.
// Given a property's lat/long, returns which Chicago TIF district (if
// any) contains it. TIF districts are public-subsidy zones — a property
// inside one is receiving city tax-increment funding for its build.
// Strong "this project has financial backing" signal for the Project Index.
//
// Data: data/_entity_cache/tif_districts.geojson (Chicago Open Data
// dataset eejr-xtfb, 100 active districts, 3.2MB). Refresh by re-running
// `curl ... eejr-xtfb.geojson > tif_districts.geojson` — districts
// change rarely (only when city council approves new ones or repeals).
//
// Algorithm: classic ray-casting. For each MultiPolygon's outer ring,
// count edge crossings of an east-going horizontal ray from the point.
// Odd crossings = inside. Holes (inner rings) flip the parity. Library-
// free; correct for arbitrary polygons including the irregular Chicago
// shapes which often have many small detours.
import { readFile } from "node:fs/promises";
import { existsSync } from "node:fs";
import { join } from "node:path";
const TIF_GEOJSON = join("/home/profit/lakehouse/data/_entity_cache", "tif_districts.geojson");
type LngLat = [number, number]; // GeoJSON convention: [longitude, latitude]
type Ring = LngLat[];
type Polygon = Ring[]; // outer ring + optional inner rings (holes)
type MultiPolygon = Polygon[];
type TifFeature = {
name: string;
trim_name?: string;
ref?: string;
approval_date?: string;
expiration?: string;
type?: string; // T-1xx etc.
comm_area?: string;
wards?: string;
// Bounding box for quick reject
bbox: { minLon: number; minLat: number; maxLon: number; maxLat: number };
geometry: MultiPolygon;
};
let tifIdx: TifFeature[] | null = null;
function bboxOfMultiPolygon(mp: MultiPolygon): TifFeature["bbox"] {
let minLon = Infinity, minLat = Infinity, maxLon = -Infinity, maxLat = -Infinity;
for (const poly of mp) {
for (const ring of poly) {
for (const [lon, lat] of ring) {
if (lon < minLon) minLon = lon;
if (lat < minLat) minLat = lat;
if (lon > maxLon) maxLon = lon;
if (lat > maxLat) maxLat = lat;
}
}
}
return { minLon, minLat, maxLon, maxLat };
}
async function ensureLoaded(): Promise<TifFeature[]> {
if (tifIdx) return tifIdx;
if (!existsSync(TIF_GEOJSON)) {
tifIdx = [];
return tifIdx;
}
try {
const raw = JSON.parse(await readFile(TIF_GEOJSON, "utf-8"));
const out: TifFeature[] = [];
for (const f of raw.features || []) {
const geom = f.geometry;
if (!geom) continue;
// Normalize Polygon → MultiPolygon for uniform iteration
let mp: MultiPolygon;
if (geom.type === "MultiPolygon") {
mp = geom.coordinates;
} else if (geom.type === "Polygon") {
mp = [geom.coordinates];
} else {
continue;
}
const props = f.properties || {};
out.push({
name: props.name || "Unknown TIF",
trim_name: props.name_trim,
ref: props.ref,
approval_date: props.approval_d,
expiration: props.expiration,
type: props.type,
comm_area: props.comm_area,
wards: props.wards,
bbox: bboxOfMultiPolygon(mp),
geometry: mp,
});
}
tifIdx = out;
return tifIdx;
} catch (e) {
console.warn("[tif] load failed:", (e as Error).message);
tifIdx = [];
return tifIdx;
}
}
// Ray-casting point-in-polygon (single ring). Returns true if (lon, lat)
// is strictly inside the ring. Edge cases (point exactly on a vertex)
// resolve by half-open interval convention; for our use case (Chicago
// boundary precision is ~1m, sites are point queries) this is fine.
function pointInRing(lon: number, lat: number, ring: Ring): boolean {
let inside = false;
const n = ring.length;
for (let i = 0, j = n - 1; i < n; j = i++) {
const [xi, yi] = ring[i];
const [xj, yj] = ring[j];
const intersect =
yi > lat !== yj > lat &&
lon < ((xj - xi) * (lat - yi)) / (yj - yi + 0) + xi;
if (intersect) inside = !inside;
}
return inside;
}
// Polygon = outer ring + holes. Inside outer AND not inside any hole.
function pointInPolygon(lon: number, lat: number, polygon: Polygon): boolean {
if (polygon.length === 0) return false;
if (!pointInRing(lon, lat, polygon[0])) return false;
for (let i = 1; i < polygon.length; i++) {
if (pointInRing(lon, lat, polygon[i])) return false;
}
return true;
}
export type TifMatch = {
name: string;
ref?: string;
approval_date?: string;
expiration?: string;
comm_area?: string;
wards?: string;
};
export async function findTifDistrict(
longitude: number | string | undefined,
latitude: number | string | undefined,
): Promise<TifMatch | null> {
const lon = typeof longitude === "string" ? parseFloat(longitude) : longitude;
const lat = typeof latitude === "string" ? parseFloat(latitude) : latitude;
if (!lon || !lat || isNaN(lon) || isNaN(lat)) return null;
const idx = await ensureLoaded();
if (idx.length === 0) return null;
for (const f of idx) {
// Bbox reject — cheap O(1) skip for the 99% of districts that
// can't possibly contain the point.
const b = f.bbox;
if (lon < b.minLon || lon > b.maxLon || lat < b.minLat || lat > b.maxLat) continue;
// Full point-in-polygon for any polygon in this MultiPolygon
for (const poly of f.geometry) {
if (pointInPolygon(lon, lat, poly)) {
return {
name: f.name,
ref: f.ref,
approval_date: f.approval_date,
expiration: f.expiration,
comm_area: f.comm_area,
wards: f.wards,
};
}
}
}
return null;
}
export async function getTifIndexStats(): Promise<{
total: number;
loaded: boolean;
}> {
const idx = await ensureLoaded();
return { total: idx.length, loaded: idx.length > 0 };
}

59
scripts/serve_imagegen.py
View File

@ -29,8 +29,14 @@ CACHE_DIR.mkdir(parents=True, exist_ok=True)
WORKFLOW_PATH = "/opt/ComfyUI/workflows/editorial_hero.json"
def _cache_key(prompt, width, height, steps):
return hashlib.sha256(f"{prompt}|{width}|{height}|{steps}".encode()).hexdigest()[:24]
def _cache_key(prompt, width, height, steps, seed=None):
# Include seed so callers can vary outputs deterministically without
# the proxy collapsing to a single cached image. None == legacy
# (omitted from the key for backward compatibility).
bits = f"{prompt}|{width}|{height}|{steps}"
if seed is not None:
bits += f"|{seed}"
return hashlib.sha256(bits.encode()).hexdigest()[:24]
def _cache_get(key):
fp = CACHE_DIR / f"{key}.webp"
@ -40,8 +46,15 @@ def _cache_put(key, img_bytes):
(CACHE_DIR / f"{key}.webp").write_bytes(img_bytes)
def _comfyui_generate(prompt, width=1024, height=512, steps=8, seed=None):
"""Submit workflow to ComfyUI and wait for result."""
def _comfyui_generate(prompt, width=1024, height=512, steps=8, seed=None,
negative_prompt=None, cfg=None, sampler=None, scheduler=None):
"""Submit workflow to ComfyUI and wait for result.
Optional overrides — when provided, replace the workflow's defaults.
The workflow template at editorial_hero.json was tuned for product
hero shots with a "no humans" negative prompt; portrait callers MUST
pass `negative_prompt` to avoid the model fighting them on faces.
"""
# Load workflow template
with open(WORKFLOW_PATH) as f:
workflow = json.load(f)
@ -51,9 +64,21 @@ def _comfyui_generate(prompt, width=1024, height=512, steps=8, seed=None):
seed = random.randint(0, 2**32)
workflow["3"]["inputs"]["seed"] = seed
workflow["3"]["inputs"]["steps"] = steps
if cfg is not None:
workflow["3"]["inputs"]["cfg"] = cfg
if sampler:
workflow["3"]["inputs"]["sampler_name"] = sampler
if scheduler:
workflow["3"]["inputs"]["scheduler"] = scheduler
workflow["5"]["inputs"]["width"] = width
workflow["5"]["inputs"]["height"] = height
workflow["6"]["inputs"]["text"] = prompt
# Node 7 is the negative-prompt CLIPTextEncode. The default is tuned
# for product hero shots and contains "human, person, face, hand,
# fingers, realistic photo of people" — actively sabotaging any
# portrait render. Always overwrite when negative_prompt is given.
if negative_prompt is not None:
workflow["7"]["inputs"]["text"] = negative_prompt
# Submit to ComfyUI
payload = json.dumps({"prompt": workflow}).encode()
@ -177,9 +202,20 @@ class ImageHandler(BaseHTTPRequestHandler):
height = min(max(int(body.get("height", 720)), 256), 1080)
steps = min(max(int(body.get("steps", 50)), 1), 80)
seed = body.get("seed")
# Portrait-friendly overrides — None means "use workflow default".
# negative_prompt MUST be passed by portrait callers to avoid
# the workflow's "no humans" baked-in negative.
negative_prompt = body.get("negative_prompt")
cfg = body.get("cfg")
sampler = body.get("sampler")
scheduler = body.get("scheduler")
# Cache check
key = _cache_key(prompt, width, height, steps)
# Cache check — seed + negative + cfg are part of the key so per-
# worker / per-config requests don't collapse to one cached image.
key = _cache_key(
f"{prompt}||neg={negative_prompt or ''}||cfg={cfg or ''}",
width, height, steps, seed,
)
cached = _cache_get(key)
if cached:
self._json(200, {"image": cached, "format": "webp", "width": width, "height": height,
@ -192,7 +228,11 @@ class ImageHandler(BaseHTTPRequestHandler):
try:
comfy_check = urllib.request.urlopen(f"{COMFYUI_URL}/system_stats", timeout=3)
if comfy_check.status == 200:
img_bytes, seed = _comfyui_generate(prompt, width, height, steps, seed)
img_bytes, seed = _comfyui_generate(
prompt, width, height, steps, seed,
negative_prompt=negative_prompt, cfg=cfg,
sampler=sampler, scheduler=scheduler,
)
backend = "comfyui"
except:
pass
@ -210,6 +250,11 @@ class ImageHandler(BaseHTTPRequestHandler):
elapsed_ms = int((time.time() - t0) * 1000)
img_b64 = base64.b64encode(img_bytes).decode()
# Recompute key with the actual seed used (when caller passed
# None, _comfyui_generate picks a random one and we want the
# cache to reflect that so re-requests with the same returned
# seed hit the disk).
key = _cache_key(prompt, width, height, steps, seed)
_cache_put(key, img_bytes)
self._json(200, {

225
scripts/staffing/fetch_face_pool.py Normal file
View File

@ -0,0 +1,225 @@
#!/usr/bin/env python3
"""
fetch_face_pool.py — pull N synthetic headshots from
https://thispersondoesnotexist.com/, write to data/headshots/face_NNNN.jpg,
optionally tag each with gender via deepface, emit a JSONL manifest.
Each fetch is a fresh StyleGAN face — no real people. Deterministic per
worker mapping happens at serve time (mcp-server hashes the worker key
into the pool); this script just builds the pool.
Usage:
python3 scripts/staffing/fetch_face_pool.py --count 300 --concurrency 3
python3 scripts/staffing/fetch_face_pool.py --count 50 --no-gender
Re-running is idempotent: existing face_NNNN.jpg files are skipped, and
the manifest is rewritten from disk state.
"""
from __future__ import annotations
import argparse
import hashlib
import json
import os
import sys
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
import urllib.request
import urllib.error
URL = "https://thispersondoesnotexist.com/"
UA = "Lakehouse/1.0 (face-pool fetch · synthetic-only · no real-person tracking)"
def fetch_one(idx: int, out_dir: str) -> tuple[int, str, bool, str | None]:
"""Returns (idx, basename, cached, error)."""
fname = f"face_{idx:04d}.jpg"
full = os.path.join(out_dir, fname)
if os.path.exists(full) and os.path.getsize(full) > 1024:
return idx, fname, True, None
try:
req = urllib.request.Request(URL, headers={"User-Agent": UA})
with urllib.request.urlopen(req, timeout=20) as resp:
blob = resp.read()
if len(blob) < 1024:
return idx, fname, False, f"response too small ({len(blob)} bytes)"
with open(full, "wb") as f:
f.write(blob)
return idx, fname, False, None
except urllib.error.URLError as e:
return idx, fname, False, f"urlerror: {e}"
except Exception as e:
return idx, fname, False, f"{type(e).__name__}: {e}"
def maybe_tag_gender(records: list[dict], out_dir: str) -> dict[str, int]:
"""If deepface is installed, label records that don't already have a
gender. Returns a count summary; mutates records in place.
Preservation contract: never overwrites prior `gender` (or any other
tag — race/age/excluded — set by tag_face_pool.py). On deepface
import failure, leaves existing tags alone instead of resetting them
to None. The previous behavior wiped 952 hand-classified rows when
fetch_face_pool was re-run from a Python without deepface installed."""
try:
from deepface import DeepFace # type: ignore
except Exception as e:
print(f" (deepface unavailable: {e}) — leaving existing tags untouched")
for r in records:
r.setdefault("gender", None)
already = sum(1 for r in records if r.get("gender") in ("man", "woman"))
return {"preserved_tagged": already, "untagged": len(records) - already}
todo = [r for r in records if r.get("gender") not in ("man", "woman")]
if not todo:
print(" every record already has gender — nothing to tag.")
return {"preserved_tagged": len(records)}
print(f" tagging gender via deepface ({len(todo)} of {len(records)} records, CPU; ~0.5-1s per face)…")
counts: dict[str, int] = {}
for i, r in enumerate(todo):
full = os.path.join(out_dir, r["file"])
try:
ana = DeepFace.analyze(
img_path=full,
actions=["gender"],
enforce_detection=False,
silent=True,
)
if isinstance(ana, list):
ana = ana[0] if ana else {}
g_raw = (ana.get("dominant_gender") or "").lower().strip()
r["gender"] = (
"man" if g_raw.startswith("man") else
"woman" if g_raw.startswith("woman") else
None
)
except Exception as e:
r["gender"] = None
r["gender_error"] = f"{type(e).__name__}: {e}"
counts[r["gender"] or "unknown"] = counts.get(r["gender"] or "unknown", 0) + 1
if (i + 1) % 25 == 0:
print(f" [{i+1}/{len(todo)}] {counts}")
return counts
def main():
p = argparse.ArgumentParser()
p.add_argument("--count", type=int, default=300, help="how many faces to maintain in pool")
p.add_argument(
"--out",
default=os.path.join(os.path.dirname(__file__), "..", "..", "data", "headshots"),
)
p.add_argument("--concurrency", type=int, default=3, help="parallel fetches (be polite)")
p.add_argument("--no-gender", action="store_true", help="skip deepface gender tagging")
p.add_argument("--shrink", action="store_true",
help="allow --count to drop manifest entries with id >= count. Default: preserve them.")
args = p.parse_args()
out = os.path.realpath(args.out)
os.makedirs(out, exist_ok=True)
# Load any existing manifest into a by-id dict so prior tags
# (gender / race / age / excluded) survive the rewrite. Also
# naturally dedupes — if the file accidentally has duplicate
# lines for the same id (this is how we ended up with a 2497-
# row manifest backing a 1000-face pool), the last one wins.
manifest = os.path.join(out, "manifest.jsonl")
existing: dict[int, dict] = {}
if os.path.exists(manifest):
dup_count = 0
with open(manifest) as f:
for line in f:
line = line.strip()
if not line:
continue
try:
row = json.loads(line)
except json.JSONDecodeError:
continue
rid = row.get("id")
if not isinstance(rid, int):
continue
if rid in existing:
dup_count += 1
existing[rid] = row
print(f"Loaded existing manifest: {len(existing)} unique ids ({dup_count} duplicate lines collapsed)")
max_existing = max(existing.keys()) if existing else -1
if max_existing >= args.count and not args.shrink:
print(
f"\nERROR: --count={args.count} would drop {sum(1 for k in existing if k >= args.count)} "
f"manifest entries (max existing id = {max_existing}). Pass --shrink to allow.\n",
file=sys.stderr,
)
sys.exit(2)
print(f"Fetching {args.count} faces → {out}")
print(f"Source: {URL} (synthetic StyleGAN — no real people)")
results: list[dict] = [None] * args.count # type: ignore
t0 = time.time()
with ThreadPoolExecutor(max_workers=max(1, args.concurrency)) as ex:
futs = {ex.submit(fetch_one, i, out): i for i in range(args.count)}
for done, fut in enumerate(as_completed(futs), 1):
idx, fname, cached, err = fut.result()
# Start from prior manifest row (preserves gender/race/age/excluded)
# and overlay only the fields fetch_one is responsible for.
base = dict(existing.get(idx, {}))
base.update({
"id": idx,
"file": fname,
"cached": cached,
"error": err,
})
results[idx] = base
if done % 25 == 0 or done == args.count:
ok = sum(1 for r in results if r and not r.get("error"))
print(f" [{done}/{args.count}] {ok} ok ({time.time()-t0:.1f}s)")
# Drop slots that errored or are still None (shouldn't happen)
records = [r for r in results if r and not r.get("error")]
print(f"\nPool ready: {len(records)} faces, {sum(1 for r in records if r['cached'])} from cache")
preserved_tags = sum(1 for r in records if r.get("gender") in ("man", "woman"))
if preserved_tags:
print(f"Preserved {preserved_tags} prior gender tags (and any race/age/excluded fields).")
if not args.no_gender and records:
print("\nGender-tagging pass:")
summary = maybe_tag_gender(records, out)
print(f" distribution: {summary}")
else:
for r in records:
r.setdefault("gender", None)
# If --shrink was NOT used and somehow id >= count rows are still in
# `existing` (which can only happen if the early gate was bypassed),
# carry them forward so we don't quietly drop them.
if not args.shrink:
for rid, row in existing.items():
if rid >= args.count and rid not in {r["id"] for r in records}:
records.append(row)
records.sort(key=lambda r: r.get("id", 0))
# Strip transient flags before persisting
for r in records:
r.pop("cached", None)
r.pop("error", None)
# Atomic write — if a re-run is interrupted, manifest stays intact.
tmp = manifest + ".tmp"
with open(tmp, "w") as f:
for r in records:
f.write(json.dumps(r) + "\n")
os.replace(tmp, manifest)
print(f"\nManifest: {manifest} ({len(records)} entries)")
# Quick checksum manifest for downstream debugging
h = hashlib.sha256()
for r in records:
h.update(r["file"].encode())
h.update(b"|")
h.update((r.get("gender") or "?").encode())
print(f"Pool fingerprint (sha256): {h.hexdigest()[:16]}")
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""
render_role_pool.py — pre-render a role-aware face pool by hitting
serve_imagegen.py (localhost:3600/generate) with prompts pulled from
the bun server's /headshots/_scenes endpoint (single source of truth
for SCENES + SCENES_VERSION).
Layout:
data/headshots_role_pool/
{band}/
{gender}_{race}/
face_00.webp
face_01.webp
...
manifest.jsonl
Each entry in manifest.jsonl:
{"band": "warehouse", "gender": "man", "race": "caucasian",
"file": "warehouse/man_caucasian/face_03.webp",
"seed": 184729338, "scenes_version": "v1"}
Idempotent: a file at the target path is skipped. Re-run with --force
to regenerate. SCENES_VERSION is captured per render so the server's
pool route can refuse stale renders if the version drifts.
"""
from __future__ import annotations
import argparse
import base64
import json
import os
import sys
import time
import urllib.request
import urllib.error
DEFAULT_BANDS = ["warehouse", "production", "trades", "driver", "lead"]
DEFAULT_GENDERS = ["man", "woman"]
DEFAULT_RACES = ["caucasian", "east_asian", "south_asian", "middle_eastern", "black", "hispanic"]
def race_text(r: str) -> str:
return {
"caucasian": "",
"east_asian": "East Asian",
"south_asian": "South Asian",
"middle_eastern": "Middle Eastern",
"black": "Black",
"hispanic": "Hispanic",
}.get(r, "")
def fetch_scenes(mcp_url: str) -> tuple[str, dict]:
"""Pull canonical SCENES from the bun server. Single source of truth."""
req = urllib.request.Request(f"{mcp_url}/headshots/_scenes")
with urllib.request.urlopen(req, timeout=10) as resp:
data = json.loads(resp.read())
return data["version"], data["scenes"]
def render(comfy_url: str, prompt: str, seed: int, steps: int, timeout: int, dim: int) -> bytes | None:
payload = json.dumps({
"prompt": prompt,
"width": dim,
"height": dim,
"steps": steps,
"seed": seed,
}).encode()
req = urllib.request.Request(
f"{comfy_url}/generate",
data=payload,
headers={"Content-Type": "application/json"},
)
try:
with urllib.request.urlopen(req, timeout=timeout) as resp:
data = json.loads(resp.read())
except urllib.error.HTTPError as e:
print(f" HTTP {e.code} from comfy: {e.read()[:200]}", file=sys.stderr)
return None
except Exception as e:
print(f" comfy error: {type(e).__name__}: {e}", file=sys.stderr)
return None
img_b64 = data.get("image")
if not img_b64:
print(f" comfy response missing 'image' field: {list(data.keys())}", file=sys.stderr)
return None
return base64.b64decode(img_b64)
def main():
p = argparse.ArgumentParser()
p.add_argument("--out", default=os.path.join(os.path.dirname(__file__), "..", "..", "data", "headshots_role_pool"))
p.add_argument("--per-bucket", type=int, default=10, help="how many faces per (band × gender × race)")
p.add_argument("--mcp", default="http://localhost:3700")
p.add_argument("--comfy", default="http://localhost:3600")
p.add_argument("--steps", type=int, default=8)
p.add_argument("--bands", nargs="*", default=DEFAULT_BANDS)
p.add_argument("--genders", nargs="*", default=DEFAULT_GENDERS)
p.add_argument("--races", nargs="*", default=DEFAULT_RACES)
p.add_argument("--force", action="store_true", help="regenerate existing files")
p.add_argument("--age", type=int, default=32)
p.add_argument("--timeout", type=int, default=120, help="per-render timeout (1024² takes ~5s on A4000)")
p.add_argument("--dim", type=int, default=1024, help="square render dimension (v2 default 1024, v1 was 512)")
args = p.parse_args()
out_root = os.path.realpath(args.out)
os.makedirs(out_root, exist_ok=True)
print(f"Fetching canonical SCENES from {args.mcp}/headshots/_scenes…")
try:
version, scenes = fetch_scenes(args.mcp)
except Exception as e:
print(f"FATAL: could not fetch scenes ({e}). Is the mcp-server up?", file=sys.stderr)
sys.exit(1)
print(f" SCENES_VERSION={version}, {len(scenes)} bands available: {list(scenes.keys())}")
# v2+ files live at {out}/{version}/{band}/{g}_{r}/face_NN.webp.
# v1 lived at {out}/{band}/... — keep that layout intact for
# rollback; the server route reads both and prefers current.
out = out_root if version == "v1" else os.path.join(out_root, version)
os.makedirs(out, exist_ok=True)
print(f" writing to: {out}")
print(f" render dim: {args.dim}×{args.dim}")
# Reject any --bands not in the server's SCENES
unknown = [b for b in args.bands if b not in scenes]
if unknown:
print(f"FATAL: unknown bands {unknown}. Server has: {list(scenes.keys())}", file=sys.stderr)
sys.exit(1)
manifest_rows = []
todo = [
(band, g, r, n)
for band in args.bands
for g in args.genders
for r in args.races
for n in range(args.per_bucket)
]
print(f"\nPlanning: {len(todo)} renders ({len(args.bands)} bands × {len(args.genders)} genders × {len(args.races)} races × {args.per_bucket} faces).")
print(f"Estimated GPU time at 1.5s/render = {len(todo) * 1.5 / 60:.1f} min.\n")
t0 = time.time()
rendered = 0
skipped = 0
failed = 0
for i, (band, g, r, n) in enumerate(todo):
bucket_dir = os.path.join(out, band, f"{g}_{r}")
os.makedirs(bucket_dir, exist_ok=True)
fname = f"face_{n:02d}.webp"
full = os.path.join(bucket_dir, fname)
rel = os.path.relpath(full, out)
if os.path.exists(full) and os.path.getsize(full) > 1024 and not args.force:
skipped += 1
manifest_rows.append({
"band": band, "gender": g, "race": r, "file": rel,
"seed": None, "scenes_version": version, "cached": True,
})
continue
scene_def = scenes[band]
scene_clause = scene_def["scene"]
race_clause = race_text(r)
gender_clause = g # "man" / "woman"
# Match the bun server's prompt builder exactly. If you tweak
# one, tweak the other (or factor a /prompt-builder endpoint).
# The {role} slot is intentionally a band-typical title here
# — the pre-rendered face is shared across roles in the same
# band, so we use the band's archetypal role. Specific roles
# still hit the on-demand /headshots/generate/:key path with
# their actual title.
archetype_role = {
"warehouse": "warehouse worker",
"production": "production worker",
"trades": "skilled tradesperson",
"driver": "delivery driver",
"lead": "shift supervisor",
}.get(band, "warehouse worker")
prompt = (
f"professional headshot portrait of a {args.age}-year-old "
f"{race_clause} {gender_clause} {archetype_role}, {scene_clause}, "
f"neutral confident expression, sharp focus, photorealistic"
)
# Deterministic seed per slot — same (band, g, r, n) always
# gets the same face. Mixing scenes_version means a SCENES
# tweak shifts every face slightly; that's the right behavior
# (it's how cache invalidation propagates to the pool too).
seed_str = f"{band}|{g}|{r}|{n}|{version}"
seed_h = 5381
for ch in seed_str:
seed_h = ((seed_h << 5) + seed_h + ord(ch)) & 0x7fffffff
seed = seed_h
bytes_ = render(args.comfy, prompt, seed, args.steps, args.timeout, args.dim)
if bytes_ is None:
failed += 1
continue
with open(full, "wb") as f:
f.write(bytes_)
rendered += 1
manifest_rows.append({
"band": band, "gender": g, "race": r, "file": rel,
"seed": seed, "scenes_version": version, "cached": False,
})
if (i + 1) % 10 == 0 or (i + 1) == len(todo):
elapsed = time.time() - t0
done = i + 1
rate = done / elapsed if elapsed > 0 else 0
eta = (len(todo) - done) / rate if rate > 0 else 0
print(f" [{done}/{len(todo)}] rendered={rendered} skipped={skipped} failed={failed} "
f"rate={rate:.2f}/s eta={eta:.0f}s")
# Atomic manifest write
manifest_path = os.path.join(out, "manifest.jsonl")
tmp = manifest_path + ".tmp"
with open(tmp, "w") as f:
for row in manifest_rows:
f.write(json.dumps(row) + "\n")
os.replace(tmp, manifest_path)
print(f"\nDone. {rendered} new, {skipped} cached, {failed} failed in {time.time()-t0:.1f}s")
print(f"Manifest: {manifest_path} ({len(manifest_rows)} entries)")
print(f"\nNext: poke {args.mcp}/headshots/__reload to pick up the new pool.")
if __name__ == "__main__":
main()

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#!/usr/bin/env python3
"""
tag_face_pool.py — run deepface gender + race classification over the
synthetic face pool produced by fetch_face_pool.py and rewrite
manifest.jsonl with `gender` (man / woman) and `race` (asian / black /
hispanic / indian / middle_eastern / white) tags.
Run with the venv that has deepface installed:
/home/profit/.local/share/deepface-venv/bin/python \
scripts/staffing/tag_face_pool.py
Idempotent: rows that already have BOTH gender and race tagged are
skipped. Pass --force to re-tag everything.
Mapping deepface buckets → /headshots/ ?e= values:
asian → split by manual region (deepface doesn't differentiate
East / South Asian; we lump as 'east_asian' since the
StyleGAN training set leans East Asian)
indian → south_asian
middle eastern → middle_eastern
black → black
hispanic → hispanic
white → caucasian
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
DEEPFACE_RACE_TO_HINT = {
"asian": "east_asian",
"indian": "south_asian",
"middle eastern": "middle_eastern",
"black": "black",
"latino hispanic": "hispanic",
"hispanic": "hispanic",
"white": "caucasian",
}
def main():
p = argparse.ArgumentParser()
p.add_argument(
"--out",
default=os.path.join(os.path.dirname(__file__), "..", "..", "data", "headshots"),
)
p.add_argument("--force", action="store_true", help="re-tag rows that already have gender+race")
p.add_argument("--limit", type=int, default=0, help="cap how many faces to process this run (0 = all)")
p.add_argument("--min-age", type=int, default=22, help="exclude faces estimated below this age (kids/teens). Staffing context = legal-age workers only.")
args = p.parse_args()
out = os.path.realpath(args.out)
manifest_path = os.path.join(out, "manifest.jsonl")
if not os.path.exists(manifest_path):
print(f"manifest not found: {manifest_path}", file=sys.stderr)
sys.exit(1)
print(f"loading deepface (cold start ~10-15s for first model build)…")
from deepface import DeepFace # type: ignore
rows = []
with open(manifest_path) as f:
for line in f:
line = line.strip()
if not line:
continue
rows.append(json.loads(line))
print(f"manifest: {len(rows)} rows")
todo = [
r for r in rows
if args.force or r.get("gender") is None or r.get("race") is None or r.get("age") is None
]
if args.limit > 0:
todo = todo[: args.limit]
print(f"to tag: {len(todo)} faces")
if not todo:
print("nothing to do.")
return
counts_g = {}
counts_r = {}
failed = 0
t0 = time.time()
for i, r in enumerate(todo):
full = os.path.join(out, r["file"])
try:
ana = DeepFace.analyze(
img_path=full,
actions=["gender", "race", "age"],
enforce_detection=False,
silent=True,
)
if isinstance(ana, list):
ana = ana[0] if ana else {}
g_raw = (ana.get("dominant_gender") or "").lower().strip()
r["gender"] = (
"man" if g_raw.startswith("man") else
"woman" if g_raw.startswith("woman") else
None
)
r_raw = (ana.get("dominant_race") or "").lower().strip()
r["race"] = DEEPFACE_RACE_TO_HINT.get(r_raw, None)
if r["race"] is None and r_raw:
r["race_raw"] = r_raw
# Age estimation — exclude minors / teens. Staffing context
# uses adult workers only. Threshold is 22 by default
# (legal + a buffer because age estimation is noisy).
try:
age = int(round(float(ana.get("age") or 0)))
except Exception:
age = 0
r["age"] = age
if age and age < args.min_age:
r["excluded"] = "minor"
else:
r.pop("excluded", None)
counts_g[r["gender"] or "unknown"] = counts_g.get(r["gender"] or "unknown", 0) + 1
counts_r[r["race"] or r_raw or "unknown"] = counts_r.get(r["race"] or r_raw or "unknown", 0) + 1
except Exception as e:
r["tag_error"] = f"{type(e).__name__}: {e}"
failed += 1
if (i + 1) % 25 == 0 or (i + 1) == len(todo):
elapsed = time.time() - t0
rate = (i + 1) / elapsed if elapsed > 0 else 0
eta = (len(todo) - i - 1) / rate if rate > 0 else 0
print(f" [{i+1}/{len(todo)}] rate={rate:.1f}/s eta={eta:.0f}s failed={failed}")
print(f" gender: {counts_g}")
print(f" race : {counts_r}")
# Write updated manifest atomically
tmp = manifest_path + ".tmp"
with open(tmp, "w") as f:
for r in rows:
f.write(json.dumps(r) + "\n")
os.replace(tmp, manifest_path)
final_g = {}
final_r = {}
excluded = 0
age_hist = {"<18": 0, "18-22": 0, "22-30": 0, "30-40": 0, "40-50": 0, "50-60": 0, "60+": 0, "unknown": 0}
for r in rows:
if r.get("excluded"):
excluded += 1
continue
final_g[r.get("gender") or "untagged"] = final_g.get(r.get("gender") or "untagged", 0) + 1
final_r[r.get("race") or "untagged"] = final_r.get(r.get("race") or "untagged", 0) + 1
a = r.get("age") or 0
if a == 0: age_hist["unknown"] += 1
elif a < 18: age_hist["<18"] += 1
elif a < 22: age_hist["18-22"] += 1
elif a < 30: age_hist["22-30"] += 1
elif a < 40: age_hist["30-40"] += 1
elif a < 50: age_hist["40-50"] += 1
elif a < 60: age_hist["50-60"] += 1
else: age_hist["60+"] += 1
print(f"\nDone. {len(rows)} rows, {excluded} excluded as <{args.min_age}, {failed} tag errors, {time.time()-t0:.1f}s")
print(f" final gender: {final_g}")
print(f" final race : {final_r}")
print(f" age dist : {age_hist}")
print(f"\nNext: poke /headshots/__reload to refresh the in-memory pool.")
if __name__ == "__main__":
main()