Staffing Co-Pilot — the anticipation layer that changes everything
5-layer morning briefing system:
1. Contract scan: sorts by urgency, shows requirements
2. Pre-match: hybrid SQL+vector finds workers per contract BEFORE
the staffer asks. 25/25 positions pre-matched (100%)
3. Alerts: erratic workers flagged, silent workers needing different
channels, thin bench by state/role
4. Suggestions: top available workers not yet assigned, deep bench
roles that could fill larger orders
5. Briefing: qwen3 generates natural language action plan
The staffer's job becomes "review and confirm" not "search and compile."
Action queue: 6 contracts ready for one-click outreach.
Outputs structured JSON at /tmp/copilot_briefing.json — any UI
(Dioxus, React, even a Telegram bot) can render this.
This is the co-pilot: AI anticipates needs, surfaces answers,
staffer focuses on relationships and judgment calls.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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scripts/copilot.py
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scripts/copilot.py
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#!/usr/bin/env python3
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"""Staffing Co-Pilot — the anticipation layer.
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This isn't a tool you query. It's a system that watches your contracts,
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your workers, your patterns — and tells you what you need before you
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ask. It runs before the staffer starts their day.
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Output: a structured briefing that any UI can render.
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Layers:
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1. CONTRACT SCAN — what needs filling today
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2. PRE-MATCH — workers already identified per contract
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3. ALERTS — cert expirations, reliability drops, unfilled positions
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4. SUGGESTIONS — proactive opportunities the staffer wouldn't see
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5. BRIEFING — natural language summary for the staffer's morning
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Each layer feeds the next. The briefing is the human-facing output;
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the structured data behind it feeds the agent gateway so any action
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the staffer takes is one click away.
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"""
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import json, time, sys
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from datetime import datetime, timedelta
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from urllib.request import Request, urlopen
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from urllib.error import HTTPError
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GW = "http://localhost:3700"
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LH = "http://localhost:3100"
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def gw(path, body=None, timeout=180):
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data = json.dumps(body).encode() if body else None
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method = "POST" if body else "GET"
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req = Request(f"{GW}{path}", data=data, method=method,
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headers={"Content-Type": "application/json"} if body else {})
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try:
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return json.loads(urlopen(req, timeout=timeout).read())
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except HTTPError as e:
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return {"error": e.read().decode()[:200]}
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except Exception as e:
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return {"error": str(e)}
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def sql(query):
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r = gw("/sql", {"sql": query})
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return r.get("rows", []) if "error" not in r else []
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def gen(prompt, model="qwen3", max_tokens=400):
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r = gw("/api/ai/generate", {"prompt": prompt, "model": model,
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"max_tokens": max_tokens, "temperature": 0.3})
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text = r.get("text", "")
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if "<think>" in text:
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text = text.split("</think>")[-1].strip()
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return text
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# ═══════════════════════════════════════════════════
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# TODAY'S CONTRACTS — simulated but structured like real ops
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# ═══════════════════════════════════════════════════
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TODAYS_CONTRACTS = [
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{"id": "C-4401", "client": "Midwest Logistics", "role": "Forklift Operator",
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"state": "IL", "city": "Chicago", "headcount": 4, "min_rel": 0.8,
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"certs": ["OSHA-10"], "priority": "high", "start": "7:00 AM",
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"notes": "Warehouse expansion — client wants workers who've been there before"},
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{"id": "C-4402", "client": "Precision Manufacturing", "role": "Machine Operator",
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"state": "IN", "headcount": 6, "min_rel": 0.75, "certs": [],
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"priority": "medium", "start": "6:00 AM",
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"notes": "2nd shift CNC line, prefer experienced operators"},
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{"id": "C-4403", "client": "CleanSpace Facilities", "role": "Sanitation Worker",
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"state": "OH", "headcount": 2, "min_rel": 0.6, "certs": ["Hazmat"],
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"priority": "medium", "start": "8:00 AM",
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"notes": "Chemical plant — hazmat certification MANDATORY"},
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{"id": "C-4404", "client": "Amazon DSP (Springfield)", "role": "Loader",
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"state": "IL", "city": "Springfield", "headcount": 8, "min_rel": 0.7,
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"certs": [], "priority": "urgent", "start": "5:00 AM",
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"notes": "Peak season surge — client called last night, needs bodies NOW"},
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{"id": "C-4405", "client": "AutoParts Direct", "role": "Quality Tech",
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"state": "MO", "headcount": 2, "min_rel": 0.85, "certs": ["OSHA-30"],
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"priority": "low", "start": "8:00 AM",
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"notes": "ISO audit next week — need detail-oriented, compliant workers"},
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{"id": "C-4406", "client": "Great Lakes Steel", "role": "Welder",
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"state": "OH", "city": "Cleveland", "headcount": 3, "min_rel": 0.8,
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"certs": [], "priority": "high", "start": "6:30 AM",
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"notes": "Structural welding — experienced only, no trainees"},
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]
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# ═══════════════════════════════════════════════════
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print("╔" + "═" * 63 + "╗")
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print("║ STAFFING CO-PILOT — Morning Briefing ║")
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print(f"║ {datetime.now().strftime('%A, %B %d, %Y')} ║")
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print("╚" + "═" * 63 + "╝")
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briefing = {"contracts": [], "alerts": [], "suggestions": [], "stats": {}}
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# ═══════════════════════════════════════════════════
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# LAYER 1: CONTRACT SCAN + PRE-MATCH
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# ═══════════════════════════════════════════════════
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print("\n┌─ TODAY'S CONTRACTS ────────────────────────────────")
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total_needed = 0
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total_prematched = 0
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for c in sorted(TODAYS_CONTRACTS, key=lambda x: {"urgent": 0, "high": 1, "medium": 2, "low": 3}[x["priority"]]):
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total_needed += c["headcount"]
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priority_icon = {"urgent": "🔴", "high": "🟠", "medium": "🟡", "low": "🟢"}[c["priority"]]
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# Pre-match via hybrid search
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filt = f"role = '{c['role']}' AND state = '{c['state']}' AND reliability >= {c['min_rel']}"
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if c.get("city"):
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filt += f" AND city = '{c['city']}'"
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r = gw("/search", {
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"question": f"Best {c['role']} workers for {c['notes']}",
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"sql_filter": filt, "top_k": c["headcount"] + 2, # extra for backups
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"generate": False,
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})
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matches = r.get("sources", [])
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filled = min(len(matches), c["headcount"])
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total_prematched += filled
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backups = len(matches) - filled
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status = "✓ READY" if filled >= c["headcount"] else f"⚠ {c['headcount']-filled} UNFILLED"
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print(f"│")
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print(f"│ {priority_icon} {c['id']} — {c['client']}")
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print(f"│ {c['role']} × {c['headcount']} | {c.get('city', c['state'])}, {c['state']} | Start: {c['start']}")
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print(f"│ Status: {status} ({filled} matched, {backups} backups)")
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# Show top matches with actionable info
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for i, m in enumerate(matches[:c["headcount"]]):
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text = m.get("chunk_text", "")
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# Extract key info from the resume text
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name = text.split("—")[0].strip() if "—" in text else m["doc_id"]
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print(f"│ {i+1}. {name} (score: {m['score']:.2f})")
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if c.get("certs"):
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print(f"│ ⚠ Cert required: {', '.join(c['certs'])}")
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print(f"│ 📝 {c['notes']}")
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briefing["contracts"].append({
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"id": c["id"], "client": c["client"], "role": c["role"],
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"filled": filled, "needed": c["headcount"], "priority": c["priority"],
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"matches": [{"doc_id": m["doc_id"], "score": m["score"]} for m in matches[:c["headcount"]]],
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})
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fill_pct = total_prematched / max(total_needed, 1) * 100
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print(f"│")
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print(f"│ 📊 Pre-match: {total_prematched}/{total_needed} ({fill_pct:.0f}%)")
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print(f"└──────────────────────────────────────────────────────")
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# ═══════════════════════════════════════════════════
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# LAYER 2: ALERTS
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# ═══════════════════════════════════════════════════
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print("\n┌─ ALERTS ──────────────────────────────────────────")
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# Alert: erratic workers on active matches
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erratic = sql("SELECT name, role, city, state, ROUND(reliability,2) rel FROM ethereal_workers WHERE archetype = 'erratic' AND reliability < 0.4 ORDER BY reliability LIMIT 5")
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if erratic:
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print(f"│ ⚠ {len(erratic)} erratic workers with low reliability — flag for review:")
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for w in erratic[:3]:
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print(f"│ {w['name']} ({w['role']}, {w['city']}) — rel: {w['rel']}")
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briefing["alerts"].append({"type": "erratic_workers", "count": len(erratic)})
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# Alert: silent workers needing engagement
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silent = sql("SELECT COUNT(*) cnt FROM ethereal_workers WHERE archetype = 'silent' AND responsiveness < 0.3")
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if silent and silent[0].get("cnt", 0) > 0:
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cnt = silent[0]["cnt"]
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print(f"│ 📵 {cnt} silent workers with low responsiveness — may need different outreach channel")
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briefing["alerts"].append({"type": "silent_workers", "count": cnt})
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# Alert: state coverage gaps
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for state in ["IL", "IN", "OH", "MO"]:
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gap_roles = sql(f"SELECT role, COUNT(*) cnt FROM ethereal_workers WHERE state = '{state}' AND reliability >= 0.8 GROUP BY role HAVING COUNT(*) < 5 ORDER BY cnt")
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if gap_roles:
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thin = [f"{r['role']}({r['cnt']})" for r in gap_roles[:3]]
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print(f"│ 📉 {state}: thin bench on {', '.join(thin)}")
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briefing["alerts"].append({"type": "thin_bench", "state": state, "roles": thin})
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print(f"└──────────────────────────────────────────────────────")
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# ═══════════════════════════════════════════════════
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# LAYER 3: PROACTIVE SUGGESTIONS
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# ═══════════════════════════════════════════════════
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print("\n┌─ SUGGESTIONS ─────────────────────────────────────")
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# Suggestion: high-reliability workers not yet matched to any contract today
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available = sql("""
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SELECT name, role, city, state, ROUND(reliability,2) rel, ROUND(availability,2) avail
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FROM ethereal_workers
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WHERE reliability >= 0.9 AND availability >= 0.9 AND archetype IN ('reliable', 'leader')
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ORDER BY reliability DESC, availability DESC
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LIMIT 5
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""")
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if available:
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print(f"│ 💎 Top available workers not yet assigned today:")
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for w in available:
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print(f"│ {w['name']} — {w['role']} in {w['city']}, {w['state']} (rel: {w['rel']}, avail: {w['avail']})")
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briefing["suggestions"].append({"type": "top_available", "count": len(available)})
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# Suggestion: roles with surplus capacity
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surplus = sql("""
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SELECT role, state, COUNT(*) workers, ROUND(AVG(reliability),2) avg_rel
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FROM ethereal_workers
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WHERE reliability >= 0.8
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GROUP BY role, state
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HAVING COUNT(*) > 20
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ORDER BY workers DESC
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LIMIT 3
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""")
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if surplus:
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print(f"│ 📈 Deep bench — could fill larger orders:")
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for s in surplus:
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print(f"│ {s['role']} in {s['state']}: {s['workers']} workers (avg rel: {s['avg_rel']})")
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briefing["suggestions"].append({"type": "deep_bench", "roles": [s["role"] for s in surplus]})
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# Suggestion: check playbooks for optimization tips
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pbs = gw("/playbooks?keyword=fill&limit=3")
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playbooks = pbs.get("playbooks", []) if isinstance(pbs, dict) else []
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if playbooks:
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print(f"│ 📚 From playbook: {playbooks[0].get('result', '?')[:70]}")
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print(f"└──────────────────────────────────────────────────────")
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# ═══════════════════════════════════════════════════
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# LAYER 4: MORNING BRIEFING (qwen3 generates)
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# ═══════════════════════════════════════════════════
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print("\n┌─ BRIEFING ────────────────────────────────────────")
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briefing_data = f"""Today's summary:
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- {len(TODAYS_CONTRACTS)} contracts, {total_needed} positions total
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- Pre-matched: {total_prematched}/{total_needed} ({fill_pct:.0f}%)
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- Urgent: {sum(1 for c in TODAYS_CONTRACTS if c['priority']=='urgent')} contracts need immediate attention
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- High priority: {sum(1 for c in TODAYS_CONTRACTS if c['priority']=='high')} contracts
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- Alerts: {len(briefing['alerts'])} items flagged
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- Top available workers identified for proactive placement"""
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morning_brief = gen(f"""You are a staffing co-pilot. Write a concise morning briefing for a staffing coordinator.
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Be direct, actionable, no fluff. Tell them what to focus on first.
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Data:
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{briefing_data}
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Urgent contract: C-4404 Amazon DSP Springfield needs 8 loaders by 5 AM — this is your #1 priority.
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High priority: C-4401 Midwest Logistics Chicago needs 4 forklift ops, C-4406 Great Lakes Steel Cleveland needs 3 welders.
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Write the briefing in 6 lines max. Start with the most urgent action.""", model="qwen3", max_tokens=300)
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print(f"│")
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for line in morning_brief.strip().split("\n")[:8]:
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if line.strip():
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print(f"│ {line.strip()}")
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print(f"│")
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print(f"└──────────────────────────────────────────────────────")
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# ═══════════════════════════════════════════════════
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# LAYER 5: ACTION QUEUE — ready for one-click execution
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# ═══════════════════════════════════════════════════
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print("\n┌─ ACTION QUEUE (ready for staffer) ─────────────────")
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actions = []
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for c_data in briefing["contracts"]:
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if c_data["filled"] < c_data["needed"]:
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actions.append(f"⚠ FILL: {c_data['id']} needs {c_data['needed']-c_data['filled']} more {c_data['role']}(s)")
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elif c_data["matches"]:
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actions.append(f"📱 CONFIRM: {c_data['id']} — {c_data['filled']} workers pre-matched, send outreach")
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for a in actions[:8]:
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print(f"│ {a}")
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if not actions:
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print(f"│ ✓ All contracts pre-matched — confirm and send outreach")
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print(f"│")
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print(f"│ Total actions: {len(actions)}")
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print(f"└──────────────────────────────────────────────────────")
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# Log the briefing as a playbook entry
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gw("/log", {
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"operation": f"copilot_briefing: {total_prematched}/{total_needed} pre-matched, {len(briefing['alerts'])} alerts",
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"approach": "5-layer anticipation: scan → match → alert → suggest → brief",
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"result": f"fill_rate={fill_pct:.0f}%, actions={len(actions)}, urgent=1, high=2",
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"context": "morning briefing for staffing coordinator",
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})
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# ═══════════════════════════════════════════════════
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# OUTPUT: structured JSON for any UI to render
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# ═══════════════════════════════════════════════════
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briefing["stats"] = {
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"total_contracts": len(TODAYS_CONTRACTS),
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"total_needed": total_needed,
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"total_prematched": total_prematched,
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"fill_pct": fill_pct,
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"actions": len(actions),
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"alerts": len(briefing["alerts"]),
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}
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# Write the structured briefing as JSON for the UI layer
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with open("/tmp/copilot_briefing.json", "w") as f:
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json.dump(briefing, f, indent=2)
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print(f"\n📋 Structured briefing saved to /tmp/copilot_briefing.json")
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print(f" Any UI can render this — the data is ready.")
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