13faed0a06
Boards (signal-weighted, ~150/sweep): - Add TimesJobs (shahidirfan) — richest board: skills+salary+experience+offsite. Build/normalize + register; legacy city alias (New Delhi→Delhi, Bengaluru→Bangalore, Gurugram→Gurgaon) — verified live it returns 0 for "New Delhi", 35 for "Delhi". - Swap LinkedIn harvestapi → curious_coder (structured industry/jobFunction/applicants); harvestapi kept registered as linkedin_v1 fallback. - Drop Indeed from enabled (0% skills, redundant). Budgets: Foundit 40, TimesJobs 35, Naukri 30, LinkedIn 30, WorkIndia 15. Pool — cursorless freshness + safety: - REMOVE pagination/cursors: curious_coder only honors start=0 (start>0 → empty), proven; the rest are date feeds. Freshness now = boards' date-sort + the per-user seen-net + pool_save id-dedup. - Exhaustion guard: a refill adding < POOL_MIN_NEW_PER_REFILL new jobs flags the (user,query) exhausted → the gate relaxes the floor instead of block-fetching dupes (makes the 100/90 floor safe on niche queries). - Storage cap: pool_save trims beyond POOL_MAX_PER_QUERY freshest (kills DB swell). 72h TTL verified. - Thresholds 100/90 (deep pool, pay-for-volume). Scoring / cards: - Evidence-based fallback prose: when Haiku's cards stage gives nothing, the card uses Opus's REAL dimension notes (not a generic "Strong on X") + coverage logging + generic salvage parser. - "Skills & requirements" → "Skills fit" (consistent dimension labels). Bug fixes: - mark_seen dedups ids (was CardinalityViolationError on a duplicate id in one batch). Tests: deep stack contracts + committed board-sample fixture (no cache-pollution flakiness) + offline e2e + opt-in live e2e. 64 pass.
matchmaking-v2 (Scout)
Fresh, on-demand matchmaking service replacing the dead nightly aggregator. Same agent-mesh mold as the other GrowQR services (FastAPI · a2a card · /a2a/tasks · orchestrator-routed). Not a drop-in — it keeps the existing 4 skills working and adds new actions from scratch.
Layout
app/
main.py FastAPI app (card + /a2a/tasks + /api/v1/health), lifespan worker
config.py lean settings (no corpus DB, no scrape schedule)
a2a/ card.py (discovery), auth.py (bearer), tasks.py (orchestrator entry)
agent/session.py Session: on_session_start / on_user_action dispatch ← the brain
adk/worker.py Redis-Streams worker (graceful no-op without Redis)
api/v1/health.py /api/v1/health
engine/
board_adapters/ ScoutPrefs → Apify actor inputs (Naukri-first, verified maps)
... the §3 cascade (normalize→filter→utility→fusion→rerank) lands here
contracts/ Pydantic contracts (user_context, transport, …) — added per slice
research/ docs/ (ENGINE_DESIGN, SIGNAL_AUDIT_V2, ENGINE_INPUTS, …) + poc/ (auto-apply)
Contract (how it connects)
Frontend useAgentSession (page "job-matching") → orchestrator (routes by card name
= matchmaking-service) → POST /a2a/tasks {action, params, user_context} → Session pushes
agent_data{action,data} → orchestrator → frontend latestData[action].
Skills: existing get_feed · sync_preferences · record_feedback · get_opportunity_detail;
new run_search · tailor_resume · submit_application · get_apply_proof (stubbed). Each new action
needs: card skill (here) + orchestrator action-map entry + frontend sendAction wiring.
Run (local)
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8006
# card: GET http://localhost:8006/.well-known/agent-card.json
# health: GET http://localhost:8006/api/v1/health
# action: POST http://localhost:8006/a2a/tasks (Bearer dev-a2a-key)
Build order (full-stack slices)
- ✅ scaffold (this) — bootable skeleton, contract wired, handlers stubbed.
- on-demand
run_search— board_adapters → Apify → engine cascade → ranked feed (+ frontend wire). - feedback labels +
record_feedback. 3.tailor_resume. 4.submit_application+get_apply_proof. - cut over from old
:8006, decommission corpus.