observer + scrum: gold-standard answer corpus for compounding context

The compose-don't-add discipline applied to the original ask: when big
models produce good results (scrum reviews + observer escalations),
save them into the matrix indexer so future small-model handlers can
retrieve them as scaffolding. Local model gets near-paid quality from
a fraction of the cost.

New: scripts/build_answers_corpus.ts indexes lakehouse_answers_v1
from data/_kb/scrum_reviews.jsonl + data/_kb/observer_escalations.jsonl.
doc_id prefixes ('review:' vs 'escalation:') let consumers same-file-
gate the prior-reviews case while keeping escalations broad.

observer.ts: buildKbPreamble adds lakehouse_answers_v1 as a third
retrieval source alongside pathway/bug_fingerprints + lakehouse_arch_v1.
qwen3.5:latest synthesis now compresses three lenses into a single
briefing for the cloud reviewer.

scrum_master_pipeline.ts: epilogue dispatches a fire-and-forget rebuild
of lakehouse_answers_v1 after each run so this run's accepted reviews
are retrievable within ~30s. LH_SCRUM_SKIP_ANSWERS_REBUILD=1 disables.

Verified live: kb_preamble grew 416 → 727 chars after wiring third
source; qwen3.5:latest synthesis (702 → 128 tokens) compresses
correctly; deepseek-v3.1-terminus diagnosis (301 → 148 tokens) is
sharper, citing architectural patterns (circuit breaker, adapter
files) instead of generic timeouts. Total cost per escalation
unchanged at ~$0.0002.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

mcp-server/observer.ts

@@ -193,8 +193,8 @@ async function buildKbPreamble(sigHash: string, cluster: ObservedOp[]): Promise<
     if (r.ok) fingerprints = (await r.json() as any).fingerprints ?? [];
   } catch {}
 
-  // Step 2: matrix retrieval against the architectural corpus we
-  // already maintain. Cluster summary is the search query.
+  // Step 2: architectural matrix (lakehouse_arch_v1) — ADRs/PRD/plan
+  // intent. Cluster summary is the search query.
   const clusterSummary = cluster.slice(-5).map(o =>
     `${o.endpoint ?? "?"} ${o.input_summary ?? ""} ${o.error ?? ""}`
   ).join(" | ");
@@ -203,13 +203,28 @@ async function buildKbPreamble(sigHash: string, cluster: ObservedOp[]): Promise<
     const r = await fetch(`${LAKEHOUSE}/vectors/search`, {
       method: "POST",
       headers: { "content-type": "application/json" },
-      body: JSON.stringify({ index_name: "lakehouse_arch_v1", query: `${taskClass} ${clusterSummary}`, top_k: 5 }),
+      body: JSON.stringify({ index_name: "lakehouse_arch_v1", query: `${taskClass} ${clusterSummary}`, top_k: 4 }),
       signal: AbortSignal.timeout(5000),
     });
     if (r.ok) matrixChunks = (await r.json() as any).results ?? [];
   } catch {}
 
-  if (fingerprints.length === 0 && matrixChunks.length === 0) return "";
+  // Step 3: gold-standard prior answers (lakehouse_answers_v1) — past
+  // scrum reviews + observer escalations. This is where the BIG-model
+  // results we save live; future small-model handlers retrieve them
+  // here as scaffolding so the cheap rung gets near-paid quality.
+  let answerChunks: { doc_id?: string; chunk_text?: string; score?: number }[] = [];
+  try {
+    const r = await fetch(`${LAKEHOUSE}/vectors/search`, {
+      method: "POST",
+      headers: { "content-type": "application/json" },
+      body: JSON.stringify({ index_name: "lakehouse_answers_v1", query: `${taskClass} ${clusterSummary}`, top_k: 3 }),
+      signal: AbortSignal.timeout(5000),
+    });
+    if (r.ok) answerChunks = (await r.json() as any).results ?? [];
+  } catch {}
+
+  if (fingerprints.length === 0 && matrixChunks.length === 0 && answerChunks.length === 0) return "";
 
   // Step 3: synthesis via local model (qwen3.5:latest, provider=ollama).
   // Compresses the raw bundle to a 1-2 sentence briefing the cloud
@@ -226,6 +241,11 @@ async function buildKbPreamble(sigHash: string, cluster: ObservedOp[]): Promise<
           `${i + 1}. [${c.doc_id ?? "?"}] ${(c.chunk_text ?? "").slice(0, 200)}`
         ).join("\n")
       : "",
+    answerChunks.length > 0
+      ? "PRIOR GOLD-STANDARD ANSWERS (similar past reviews + escalations):\n" + answerChunks.map((c, i) =>
+          `${i + 1}. [${c.doc_id ?? "?"}] ${(c.chunk_text ?? "").slice(0, 240)}`
+        ).join("\n")
+      : "",
   ].filter(Boolean).join("\n\n");
 
   const synthPrompt = `A failure cluster (sig_hash=${sigHash.slice(0, 8)}, ${cluster.length} occurrences, task_class=${taskClass}) is about to be escalated for diagnosis. Here are prior signals from our knowledge base:

scripts/build_answers_corpus.ts

@@ -0,0 +1,138 @@
+#!/usr/bin/env bun
+/**
+ * Build `lakehouse_answers_v1` — the gold-standard answer corpus.
+ *
+ * Sources:
+ *   - data/_kb/scrum_reviews.jsonl       (per-file scrum reviews)
+ *   - data/_kb/observer_escalations.jsonl (deepseek-v3.1-terminus diagnoses)
+ *
+ * doc_id prefixes distinguish origin so consumers can same-file-gate
+ * (only return `review:<file>` chunks when the focus file matches) or
+ * broaden (always include `escalation:` for general task-class signal).
+ *
+ * Replaces scrum_findings_v1 — broader scope, cleaner gating story.
+ *
+ * Re-run after every scrum run / observer escalation lands. The
+ * pipeline's epilogue calls this; manual runs work too.
+ */
+
+import { readFileSync, existsSync } from "node:fs";
+import { resolve } from "node:path";
+
+const ROOT = resolve(import.meta.dir, "..");
+const GATEWAY = process.env.LH_GATEWAY ?? "http://localhost:3100";
+const INDEX_NAME = process.env.LH_CORPUS_NAME ?? "lakehouse_answers_v1";
+const SOURCE_LABEL = "lakehouse_answers";
+const CHUNK_SIZE = Number(process.env.LH_CHUNK_SIZE ?? 1500);
+const OVERLAP = Number(process.env.LH_OVERLAP ?? 150);
+const MIN_BYTES = 200;  // skip stub rows
+
+interface Doc { id: string; text: string }
+
+function slugFile(path: string): string {
+  return path.replace(/^crates\//, "").replace(/[^a-z0-9]+/gi, "_").slice(0, 40);
+}
+
+function compactTs(iso: string): string {
+  return iso.replace(/[-:T]/g, "").slice(0, 14);
+}
+
+function buildScrumReviewDocs(): Doc[] {
+  const path = resolve(ROOT, "data/_kb/scrum_reviews.jsonl");
+  if (!existsSync(path)) return [];
+  const lines = readFileSync(path, "utf8").split("\n").filter(Boolean);
+  const docs: Doc[] = [];
+  const idCounts = new Map<string, number>();
+
+  for (const line of lines) {
+    let row: any;
+    try { row = JSON.parse(line); } catch { continue; }
+    const file = row.file ?? "";
+    const preview = row.suggestions_preview ?? "";
+    if (!file || preview.length < MIN_BYTES) continue;
+
+    const ts = compactTs(row.reviewed_at ?? "");
+    const baseId = `review:${slugFile(file)}:${ts || "no_ts"}`;
+    const count = (idCounts.get(baseId) ?? 0) + 1;
+    idCounts.set(baseId, count);
+    const id = count === 1 ? baseId : `${baseId}_${count}`;
+
+    const header = `File: ${file}\nReviewed: ${row.reviewed_at ?? "?"}\nModel: ${row.accepted_model ?? "?"}\nVerdict: ${row.verdict ?? "?"}\n\n`;
+    docs.push({ id, text: header + preview });
+  }
+  return docs;
+}
+
+function buildEscalationDocs(): Doc[] {
+  const path = resolve(ROOT, "data/_kb/observer_escalations.jsonl");
+  if (!existsSync(path)) return [];
+  const lines = readFileSync(path, "utf8").split("\n").filter(Boolean);
+  const docs: Doc[] = [];
+  const idCounts = new Map<string, number>();
+
+  for (const line of lines) {
+    let row: any;
+    try { row = JSON.parse(line); } catch { continue; }
+    const analysis = row.analysis ?? "";
+    if (analysis.length < MIN_BYTES) continue;
+
+    const ts = compactTs(row.ts ?? "");
+    const sigSlug = String(row.sig_hash ?? "no_sig").slice(0, 12);
+    const baseId = `escalation:${sigSlug}:${ts || "no_ts"}`;
+    const count = (idCounts.get(baseId) ?? 0) + 1;
+    idCounts.set(baseId, count);
+    const id = count === 1 ? baseId : `${baseId}_${count}`;
+
+    const header = `Failure cluster · sig_hash=${row.sig_hash ?? "?"} · cluster_size=${row.cluster_size ?? "?"} · endpoint=${row.cluster_endpoint ?? "?"}\nDiagnosed by: ${row.mode ?? "?"}\nWhen: ${row.ts ?? "?"}\n\n`;
+    docs.push({ id, text: header + analysis });
+  }
+  return docs;
+}
+
+async function main() {
+  const dryRun = process.argv.includes("--dry-run") || process.argv.includes("--print");
+  const printOnly = process.argv.includes("--print");
+
+  const reviews = buildScrumReviewDocs();
+  const escalations = buildEscalationDocs();
+  const docs = [...reviews, ...escalations];
+  const totalBytes = docs.reduce((s, d) => s + d.text.length, 0);
+
+  console.log(`[answers] ${docs.length} docs · ${totalBytes} bytes`);
+  console.log(`[answers]   reviews:     ${reviews.length}`);
+  console.log(`[answers]   escalations: ${escalations.length}`);
+
+  if (printOnly) {
+    docs.slice(0, 2).forEach(d => console.log(`  ${d.id} (${d.text.length}b) ${d.text.slice(0, 100).replace(/\n/g, " ")}…`));
+    if (escalations.length > 0) {
+      console.log(`  ... (last escalation): ${escalations[escalations.length - 1].id}`);
+    }
+    return;
+  }
+  if (dryRun) return;
+  if (docs.length === 0) {
+    console.log("[answers] no docs to index — skipping POST");
+    return;
+  }
+
+  const r = await fetch(`${GATEWAY}/vectors/index`, {
+    method: "POST",
+    headers: { "content-type": "application/json" },
+    body: JSON.stringify({
+      index_name: INDEX_NAME,
+      source: SOURCE_LABEL,
+      documents: docs,
+      chunk_size: CHUNK_SIZE,
+      overlap: OVERLAP,
+    }),
+    signal: AbortSignal.timeout(60_000),
+  });
+  if (!r.ok) {
+    console.error(`[answers] HTTP ${r.status}: ${await r.text()}`);
+    process.exit(1);
+  }
+  const j: any = await r.json();
+  console.log(`[answers] job ${j.job_id} · ${j.documents} docs → ${j.chunks} chunks queued`);
+}
+
+main().catch(e => { console.error(e); process.exit(1); });

tests/real-world/scrum_master_pipeline.ts

@@ -2208,6 +2208,25 @@ async function main() {
   log("");
   log(`report: ${OUT_DIR}/scrum_report.md`);
 
+  // Auto-rebuild lakehouse_answers_v1 so this run's reviews are
+  // retrievable by future scrum/observer enrichment paths within ~30s.
+  // Best-effort, fire-and-forget — don't fail the pipeline on rebuild
+  // hiccups. Toggle off via LH_SCRUM_SKIP_ANSWERS_REBUILD=1.
+  if (process.env.LH_SCRUM_SKIP_ANSWERS_REBUILD !== "1") {
+    try {
+      const { spawn } = await import("node:child_process");
+      const child = spawn("bun", ["run", "scripts/build_answers_corpus.ts"], {
+        cwd: process.cwd(),
+        stdio: "inherit",
+        detached: true,
+      });
+      child.unref();
+      log("answers corpus rebuild dispatched (bun run scripts/build_answers_corpus.ts)");
+    } catch (e) {
+      log(`answers corpus rebuild skipped: ${(e as Error).message}`);
+    }
+  }
+
   process.exit(summary.resolved === summary.target_count ? 0 : 1);
 }