/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).
Ayobami Adegoke