# QUALITY_BAR — acceptance checklist per occupation package

The bar every package must clear before its depth files count as done.
Reference implementation: `skills/recruitment-consultant/` (gold standard,
reviewed 2026-07-07). The QA harness (`pipeline/qa_sample.py`) checks the
mechanical items on a 2 % sample per batch; the judgment items are for
Claude spot-checks and human review.

## Mechanical (harness-checkable)

- [ ] All four depth files exist: `references/usecases.md`, `references/intake.md`,
      `references/quality.md`, `evals/eval-01..05.md`
- [ ] Section skeletons match the templates (headings greppable:
      `# Use cases —`, `# Intake —`, `# Quality criteria —`, `# Eval 0`)
- [ ] usecases.md has 6-8 `## N.` sections, each with an `Output:` line
- [ ] intake.md has 10-14 numbered questions and a "Do not guess" marker
- [ ] quality.md has 15-25 checkboxes (`- [ ]`)
- [ ] 5 eval files, each with `## Task` and `## Pass criteria`, <= 30 lines
- [ ] One eval contains a marked trap (`TRAP:` in pass criteria)
- [ ] Every market percentage cited in usecases.md exists verbatim in
      market.md (no numbers from memory)
- [ ] No banned filler words (see templates/STYLE.md list), no `TODO`/`TBD`
- [ ] No real PII / employer names from job ads in evals or examples
- [ ] manifest.json updated (sources incl. evidence as-of date), PROVENANCE.md
      regenerated, sanitize gate passes

## Judgment (Claude/human review)

- [ ] Use cases are the *LLM-doable* slice of the occupation (paperwork
      around the craft for manual jobs, never the physical act)
- [ ] Use-case ranking follows leverage (market share x LLM fit), not
      alphabetical or random order
- [ ] Intake questions target this occupation's real failure modes,
      not generic project questions
- [ ] quality.md encodes the occupation's non-negotiables as hard stops
- [ ] Evals are fully specified (agent needs nothing beyond the file) and
      the trap actually tests refusal/flagging behaviour
- [ ] Tone per STYLE.md: terse, evidence-based, no marketing filler

## Extraction quality (per evidence batch, 2 % sample)

- [ ] >= 90 % of sampled records faithful to the ad text (no invented items)
- [ ] Seniority plausible vs. title/requirements
- [ ] No cross-field duplicates (product in tools only)
- [ ] Items normalized: english, singular, lowercase except products/acronyms
