Run skill-scout

Launch the skill-scout loop over the unscanned conference queue. Full design + pipeline: skill-scout/README.md. Invoking this skill is the explicit opt-in to run the Workflow.

Steps

1. Today's date. Use the real current date as YYYY-MM-DD (it stamps the CSVs — never hardcode).

2. Check the queue.

   python3 skill-scout/harness/queue.py | python3 -c "import json,sys; d=json.load(sys.stdin); print(len(d['confs']),'unscanned'); [print(' ',c['slug']) for c in d['confs'][:30]]"
   

   If 0 unscanned: the queue is empty — tell the user to append fresh JVM/Kotlin conferences (2024–26) to skill-scout/db/conferences.csv, or stop. Do not fabricate conferences.

3. Launch the Workflow (one batch, self-committing):

   Workflow({
     scriptPath: "skill-scout/harness/overnight.workflow.js",
     args: { limit: 25, today: "<today>", autoCommit: true }
   })
   

   • limit: 25 covers a full batch (the tested size); pass a smaller limit for a quick run.
   • autoCommit: true makes ONE scoped commit at the end (only skill-scout/db, candidates.md, review.html, rules/*.md; aborts if anything else is staged). Omit it if the user wants to review review.html before committing.
   • It runs in the background; a task-notification fires on completion.

4. On completion, report the delta from the result JSON: found / needs_review / bundles, the per-conf validation (must be PASS), and browserNeeded (confs whose roster needed the agent-browser fallback and may warrant a re-run). Then point the user at skill-scout/review.html — the human reviews it and promotes rows into skills/*.yaml (see top-level CONTRIBUTING.md).

Options (pass in args when asked)

Want	Arg
Higher-rigor adversarial recheck	recheckModel: "opus" (slower/pricier; delete stale harness/recheck_*.json first — the cache is model-blind)
Dry run (no CSV writes)	dryApply: true
Specific conference(s) only	slugs: ["<slug>", …]
Re-judge from cached scans (no re-scan)	evalOnly: [{slug,name,url}, …]

Notes

• One conference is the unit of work; the db/*.csv files are the state. A relaunch resumes cheaply from the per-conf caches in harness/ (gitignored), so an interrupted run is safe to re-launch.
• A full-queue run is long (serial GitHub scan, ~15 min/conf) — it's meant to run AFK. Monitor with /workflows; serial phases self-recover from GitHub rate-limiting.