2b2d67b4c8
The measurement showed the USER-FACING Opus score is already well-calibrated — the real bug was curate FAILING and dropping to the bad white-box fallback: - curate.py: _salvage_kept() brace-scans + parses each kept object independently, so one bad char or a max-tokens truncation no longer drops the whole shortlist. max_tokens 4000 → 8000 (28 sifted jobs × full report cards overflowed). This fixed the sales case (was failing → fallback). - curate.py: calibration nudge in the prompt — a genuinely strong current-state match is a real low-80s, partial/stretch 60s-70s; don't under-sell strong matches into the 60s (without inflating weak ones). - normalize.py + search.py: _seniority_from_title() infers seniority from the title (conservative) when a board omits it, applied centrally in the sweep → the experience factor + Opus brief aren't blind. Verified (live Opus on the regression cases): tech MAE 9.8→4.5, sales FAILED→MAE 5.3 (kept 13), ops 4.2→3.3 — user-facing scores now MAE 3-5 with zero curate failures. 31 tests 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.