Live Contracts canvas: Chicago permits × workers_500k × playbook patterns
New devop.live/lakehouse section pairs live public Chicago building
permits with derived staffing contracts, ranked candidates from the
500K worker bench, and meta-index discovered patterns per role+geo.
Makes the Phase 19 boost + Path 2 pattern discovery visible on real
external data, without needing a paying client to demo.
Backend:
- New /intelligence/permit_contracts endpoint
- Fetches 6 recent Chicago permits > $250K from the Socrata API
- Derives proposed fill: 1 worker per $150K of permit value (capped 2-8)
- For each: /vectors/hybrid with use_playbook_memory=true,
playbook_memory_k=25, auto availability>0.5 filter
- For each: /vectors/playbook_memory/patterns with k=25 min_freq=0.3
- Returns permit + proposed contract + top 5 candidates with boosts
and citations + discovered pattern + pattern_matched count
Frontend:
- New "Live Contracts" section on search.html between today's sim
contracts and Market Intelligence
- Per-permit card: cost + work_type + address + proposed role/count
+ pool size + top 3 candidates (with endorsement chip when boost
fires) + memory-derived pattern ("MEMORY (N playbooks): recurring
certifications: OSHA-10 47%, Forklift... · archetype mostly: ...")
Real working demo even without paying clients: shows the system
operating on genuinely external data with our synthetic-data-derived
learning applied.
This commit is contained in:
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@ -1240,6 +1240,107 @@ tr:hover{background:#111827}
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}
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}
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// Intelligence: Chicago permits → assumed staffing contracts with
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// Phase 19-ranked candidates and Path-2 discovered patterns. Each
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// card pairs a REAL permit (live from data.cityofchicago.org) with
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// a PROPOSED fill drawn from our 500K worker bench. Surfaces the
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// meta-index dimension directly: "what past similar fills had in
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// common" for this role + geo.
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if (url.pathname === "/intelligence/permit_contracts" && req.method === "POST") {
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const start = Date.now();
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try {
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const permitUrl = "https://data.cityofchicago.org/resource/ydr8-5enu.json";
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// Recent + substantial permits only — skip tiny ones that
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// don't imply real staffing demand.
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const permits: any[] = await fetch(
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`${permitUrl}?$select=permit_type,work_type,work_description,reported_cost,street_number,street_direction,street_name,community_area,issue_date&`
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+ `$where=reported_cost>250000 AND issue_date>'2025-06-01'`
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+ `&$order=issue_date DESC&$limit=6`
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).then(r => r.json()).catch(() => []);
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const typeToRole: Record<string, string> = {
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"Electrical Work": "Electrician",
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"Masonry Work": "Production Worker",
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"Mechanical Work": "Maintenance Tech",
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"Reroofing": "Production Worker",
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"Plumbing Work": "Maintenance Tech",
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};
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const contracts: any[] = [];
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for (const p of permits) {
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const cost = parseFloat(p.reported_cost || 0);
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// Industry heuristic — one worker per $150K of permit value,
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// capped at 8 per contract for staffing realism.
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const count = Math.min(Math.max(Math.round(cost / 150000), 2), 8);
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const role = typeToRole[p.work_type || ""] || "Production Worker";
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const city = "Chicago";
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const state = "IL";
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// Phase 19 ranked candidates. Soft availability filter
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// auto-applied by /search — this mirrors the real recruiter
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// query path exactly.
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const searchRes = await api("POST", "/vectors/hybrid", {
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index_name: "workers_500k_v1",
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filter_dataset: "workers_500k",
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id_column: "worker_id",
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sql_filter: `role = '${role}' AND state = '${state}' AND city = '${city}' AND CAST(availability AS DOUBLE) > 0.5`,
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question: `${role} for ${p.work_type || "construction"} in ${city}`,
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top_k: 5, generate: false,
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use_playbook_memory: true, playbook_memory_k: 25,
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}).catch(() => ({ sources: [] as any[] }));
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// Path 2 — discovered patterns for this role in this city.
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const patternRes = await api("POST", "/vectors/playbook_memory/patterns", {
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query: `${role} in ${city}, ${state}`,
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top_k_playbooks: 25,
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min_trait_frequency: 0.3,
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}).catch(() => ({} as any));
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const sources = (searchRes.sources || []).slice(0, 5).map((s: any) => {
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const name = String(s.chunk_text || "").split("—")[0]?.trim() || s.doc_id;
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return {
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doc_id: s.doc_id,
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name,
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score: s.score,
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playbook_boost: s.playbook_boost || 0,
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playbook_citations: s.playbook_citations || [],
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};
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});
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contracts.push({
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permit: {
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cost,
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work_type: p.work_type || "General construction",
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description: (p.work_description || "").substring(0, 140),
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address: `${p.street_number || ""} ${p.street_direction || ""} ${p.street_name || ""}`.trim(),
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community_area: p.community_area,
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issue_date: (p.issue_date || "").substring(0, 10),
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},
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proposed: {
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role,
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count,
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city, state,
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pool_size: searchRes.sql_matches,
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candidates: sources,
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},
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discovered_pattern: patternRes.discovered_pattern,
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pattern_matched: patternRes.matched_playbooks ?? 0,
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pattern_workers_examined: patternRes.total_workers_examined ?? 0,
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});
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}
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return ok({
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generated_at: new Date().toISOString(),
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count: contracts.length,
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contracts,
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duration_ms: Date.now() - start,
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note: "Live Chicago permits paired with workers_500k-ranked candidates and playbook_memory discovered patterns. The permit is real public data; the proposed fill is derived per industry heuristic (~$150K → 1 worker).",
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});
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} catch (e: any) {
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return err(`permit_contracts: ${e.message}`, 500);
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}
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}
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// Intelligence: Log a search → selection as a learned pattern
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if (url.pathname === "/intelligence/learn" && req.method === "POST") {
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const b = await json();
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@ -112,6 +112,14 @@ body{font-family:'Inter',-apple-system,system-ui,'Segoe UI',sans-serif;backgroun
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<div id="main"><div class="ld">Analyzing contracts and workers...</div></div>
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<div class="section" id="live-contracts-section">
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<div class="section-header">
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<span class="section-title">Live Contracts — Chicago Permits → Proposed Fills</span>
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<span class="section-meta">Real public permit data + our 500K worker bench + past playbook patterns</span>
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</div>
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<div id="live-contracts"><div class="ld">Loading live contracts...</div></div>
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</div>
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<div class="section" id="market-section">
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<div class="section-header">
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<span class="section-title">Market Intelligence</span>
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@ -147,12 +155,84 @@ var P=location.pathname.indexOf('/lakehouse')>=0?'/lakehouse':'';
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var A=location.origin+P;
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var AC=['#1a2744','#1a3a2a','#2a1a3a','#3a2a1a','#1a3a3a','#2a2a1a'];
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var lastQuery='';
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window.addEventListener('load',function(){loadDay();loadMarket();loadLearning()});
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window.addEventListener('load',function(){loadDay();loadLiveContracts();loadMarket();loadLearning()});
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function api(path,body){
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return fetch(A+path,{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify(body)}).then(function(r){return r.json()})
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}
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function loadLiveContracts(){
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// Pair live Chicago permits with our 500K worker bench and the
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// meta-index discovered patterns for each role+geo. This is the
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// "real external data meets synthetic playbook learning" card set.
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api('/intelligence/permit_contracts',{}).then(function(r){
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var el=document.getElementById('live-contracts');el.textContent='';
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if(!r||!r.contracts||r.contracts.length===0){
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el.textContent='No permits returned.';return;
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}
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r.contracts.forEach(function(c){
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var p=c.permit||{}, prop=c.proposed||{};
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var card=document.createElement('div');card.className='insight info';
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card.style.cssText='background:#0d1117;border:1px solid #171d27;border-radius:10px;padding:16px;margin-bottom:10px;border-left:3px solid #388bfd';
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// Header — permit
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var hdr=document.createElement('div');hdr.style.cssText='display:flex;justify-content:space-between;margin-bottom:8px;gap:12px';
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var left=document.createElement('div');
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var title=document.createElement('div');title.style.cssText='font-weight:600;color:#e6edf3;font-size:14px';
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title.textContent='$'+(p.cost||0).toLocaleString()+' · '+(p.work_type||'');
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var addr=document.createElement('div');addr.style.cssText='color:#8b949e;font-size:12px;margin-top:2px';
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addr.textContent=(p.address||'')+' · Chicago, IL · '+(p.issue_date||'');
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left.appendChild(title);left.appendChild(addr);
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var right=document.createElement('div');right.style.cssText='color:#58a6ff;font-size:12px;font-weight:600;text-align:right;white-space:nowrap';
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right.textContent=prop.count+'× '+prop.role;
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var sub=document.createElement('div');sub.style.cssText='color:#545d68;font-size:10px;text-align:right';
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sub.textContent='pool: '+(prop.pool_size||'?').toLocaleString()+' available';
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right.appendChild(sub);
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hdr.appendChild(left);hdr.appendChild(right);card.appendChild(hdr);
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// Description
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if(p.description){
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var desc=document.createElement('div');desc.style.cssText='color:#94a3b8;font-size:11px;margin-bottom:10px;line-height:1.5';
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desc.textContent=p.description;card.appendChild(desc);
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}
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// Pattern (meta-index) chip
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if(c.discovered_pattern){
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var pat=document.createElement('div');pat.style.cssText='background:#0d2818;border:1px solid #2ea04360;border-radius:6px;padding:8px 12px;margin-bottom:10px;font-size:11px;color:#86efac;line-height:1.5';
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var plabel=document.createElement('span');plabel.style.cssText='color:#3fb950;font-weight:600;margin-right:6px';
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plabel.textContent='MEMORY ('+c.pattern_matched+' playbooks):';
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pat.appendChild(plabel);
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pat.appendChild(document.createTextNode(' '+c.discovered_pattern));
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card.appendChild(pat);
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}
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// Candidates
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var cands=prop.candidates||[];
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cands.slice(0,3).forEach(function(cand,i){
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var row=document.createElement('div');row.style.cssText='display:flex;align-items:center;gap:10px;padding:6px 10px;background:#161b22;border-radius:6px;margin-bottom:4px;font-size:12px';
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var av=document.createElement('div');av.style.cssText='width:28px;height:28px;border-radius:6px;display:flex;align-items:center;justify-content:center;font-weight:600;font-size:10px;color:#e6edf3;background:'+AC[i%AC.length];
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av.textContent=(cand.name||'?').split(' ').map(function(n){return (n[0]||'').toUpperCase()}).join('').substring(0,2);
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var info=document.createElement('div');info.style.cssText='flex:1;min-width:0';
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var nm=document.createElement('div');nm.style.cssText='color:#e6edf3;font-weight:500';nm.textContent=cand.name||cand.doc_id;
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if((cand.playbook_boost||0)>0){
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var chip=document.createElement('span');chip.style.cssText='margin-left:8px;padding:2px 7px;border-radius:9px;font-size:9px;font-weight:600;background:#0d2818;border:1px solid #2ea043;color:#3fb950;vertical-align:middle';
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chip.textContent='Endorsed · '+(cand.playbook_citations||[]).length+' playbook'+((cand.playbook_citations||[]).length===1?'':'s');
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nm.appendChild(chip);
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}
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var sub2=document.createElement('div');sub2.style.cssText='color:#545d68;font-size:10px';
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sub2.textContent=cand.doc_id+' · score '+(cand.score||0).toFixed(3);
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info.appendChild(nm);info.appendChild(sub2);
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row.appendChild(av);row.appendChild(info);
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card.appendChild(row);
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});
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if(cands.length>3){
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var more=document.createElement('div');more.style.cssText='font-size:10px;color:#58a6ff;padding:4px 10px;margin-top:2px';
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more.textContent='+ '+(cands.length-3)+' more candidates available';
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card.appendChild(more);
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}
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el.appendChild(card);
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});
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}).catch(function(e){
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document.getElementById('live-contracts').textContent='Error loading: '+e.message;
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});
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}
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function loadDay(){
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// Step 1: run simulation + get real worker count + populate dropdowns from actual data
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Promise.all([
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