job-scoutlisted

# job-scout — discovery + scoring ## Precedent Sweeps public ATS APIs (Greenhouse, Lever, Ashby, SmartRecruiters), hard-drops out-of-band roles before any LLM call, then scores survivors with a cheap model. Cost scales with matches, not listings. ## Data sources (public ATS APIs — no scraping) Greenhouse, Lever, Ashby, SmartRecruiters each expose public job-listing endpoints. Maintain a watchlist of target companies (see `data/` — create `watchlist.txt`). A full sweep of ~100 companies takes about a minute. ## Pipeline 1. Sweep the watchlist via ATS APIs → raw listings. 2. **Keyword pre-filter BEFORE any LLM call** — cheap. Drop obviously off-target roles. 3. **Hard drops** from `profile/04-job-evaluation.md`: `min_seniority` (drop below Senior), hard-cued experience minimums, location, work-auth, excluded industries. 4. Score survivors against the rubric (mostly penalties). Use a cheap model for scoring, a stronger one only for anything you tailor later — deliberate cost tiering. 5. Sort into **apply first / worth a look / skipped**, one-line reason each. 6. Write a dated digest to `data/digests/YYYY-MM-DD.md` and (if scheduled) commit it. ## Reliability Malformed scoring output retries once, then marks the role `unscored` rather than crashing the whole run. ## Mode Scheduled (daily, cheap, benefits from overnight) via `.github/workflows/daily-scout.yml`, or on demand when the user asks "what's out there". ## Implementation (built and tested) Real code lives in `