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Ayobami Adegoke
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/scrape finds and dedupes postings; /apply evaluates one at a time in depth. Nothing connects the two ends: after a scrape returns 20 jobs, the user eyeballs a table to decide where to spend /apply effort. /rank is the bridge: batch-score every new posting against the fit framework and return a ranked shortlist. How it works: - Selects jobs with status "new" from job_scraper/seen_jobs.json (--all re-ranks everything unapplied; a focus argument filters), excluding anything already in job_search_tracker.csv - Dispatches parallel general-purpose agents (~5 jobs each) that WebFetch each posting and score the five dimensions from 04-job-evaluation.md. The rubric (skill match areas, career goals, deal-breakers) is passed inline per the same token-efficiency rules /apply uses; agents score only from actually fetched content and mark dead postings expired, never guessing from a title - Triage depth by design: posting text vs. profile only - no company research, no salary lookups. /apply's Step 1 evaluation stays authoritative and always re-runs on handoff - Aggregates with the framework's 30/25/15/30 weighting and verdict bands; location deal-breakers veto regardless of score; deadlines within 7 days get urgency flags and win ties - Updates seen_jobs.json additively (status "ranked"/"expired" plus rank_score/rank_verdict/rank_date) so /scrape dedup keeps working; the tracker is read-only. Re-running is idempotent Integration: job-scraper SKILL.md documents the new status values and suggests /rank after large scrape batches; README (commands list, file tree, quick-start step 4).