| name | gary-job-search |
| description | GARY's core job-search engine — source offers across boards, validate fit by reading the real JD, tailor an ATS CV per role, and prepare human-quality applications, stopping before submit. Use whenever the user wants to find jobs, scan boards, evaluate fit, tailor a CV, or continue the hunt. A GARY skill created by Julián Nicholls (@jnichollsc). |
| metadata | {"author":"Julián Nicholls (@jnichollsc)","origin":"GARY — ported & generalized from proyecto26/career-ops"} |
GARY job-search engine (by @jnichollsc)
The full, user-agnostic methodology lives in docs/operating-rules.md — load it. The per-board execution runbook (exact steps, URLs/APIs, apply mechanisms, Himalayas ≤2-bot/429 rule, ask-on-untracked→continue) lives in docs/career-ops-map.md — load it too. This skill is the entry point; it does NOT restate the rules.
Candidate context = NotebookLM RAG (not a static file)
The user's context is a NotebookLM notebook built from their CV + the questions answered over time (ask-on-unmapped). Query the notebook (via the notebooklm-ai-plugin skill) for any candidate fact — stack, YOE, gaps, comp, language, eligibility, EEO, screening answers. When a fact is missing: ask the user, then store the answer back into the notebook so the RAG converges and it's never re-asked. Never fabricate.
Flow (see operating-rules.md for detail)
- ATS provider scan (0 tokens, ISOLATED) — "buscar ofertas" ALSO triggers
node engines/scan.mjs → data/pipeline.md: the mature 16-provider zero-token scanner (Ashby/Greenhouse/Lever/Workday/…), reading portals.yml (derived agnostically from an ATS-only per-session NotebookLM clone, decoupled from the connections' gary-context.md). Runs ALONGSIDE the connections below — never replaces or touches them. Its jobs are recorded under the ATS column in data/metrics.md. Full rule: operating-rules.md §4.
- Source per board (LinkedIn/Gmail/GetOnBoard/Himalayas/…) — paginate fully, read each JD, validate by description (not title).
- Triage with the two-way-fit gate (modality/language/profile/YOE/one-per-company/closed).
- Evaluate A–G + tailor the CV to the mapped role variant.
- Apply-prep — fill to the Submit step; the final click + captcha stay the user's.
- Merge survivors into
data/offers-master.md (the one canonical map).
Onboarding (CV → roles → per-offer CV)
User delivers a base CV → build the NotebookLM context + map role families (variants) → derive the scanner filter → tailor per offer from the role variant. Log into each mapped site in the automation browser once.
