# Quality audit — skills-core/artificial-intelligence-engineer (v1)

Date: 2026-07-09 · Baseline: git tag `v1` (af05383b) · Auditor: pipeline session
Scope: SKILL.md, references/market.md, references/ai-skills.md + the generating
pipeline code (p2_generate, p3a-c, p5_enrich_ai_skills).

## Findings (measured against the v1 tag)

### F1 — market.md contamination: CONFIRMED, 24% of corpus
`data/raw/jobs/artificial-intelligence-engineer_ads.json` holds 66 ads;
**16 of 66 (24%)** are designer/creative/marketing roles. Verbatim titles
(kept as labeled near-misses for the relevance-gate eval set):
"AI-Powered UI/UX Designer", "Senior AI UI/UX Designer", "Principal Product
Designer, SeekrGuard", "Graphic Designer - AI Trainer", "AI Automation Design
Packaging Lead", "UX and UI Designer for AI-Driven Software Development
Application", "AI Performance Creative Designer (Meta/YouTube Ads)",
"Staff AI Product Designer, Gemini Assistant", "Senior AI Product Designer,
Gemini Assistant", "AI Web/UX Designer", "Lead Product Designer",
"Senior AI Experience Designer" (+4 more).
Effect on rankings: Figma 15%, Sketch 8%, interaction design 12%,
user research 11%, product design 8% — none belong in an AI-engineer profile.
Root cause: p3a fetches by title/altLabel keyword ("AI …") with **no
relevance gate**; extraction ingests every fetched ad.

### F2 — corpus thin and US-only: CONFIRMED
market.md header: "66 real job ads (JSearch API, countries: us 66)".
No EU/DACH slice despite ESCO (EU) taxonomy base and DACH/EU target market.
Root cause: p3a country list; no per-region reporting or minimum-corpus
threshold in p3c.

### F3 — ai-skills.md over-inclusion + count mismatch: CONFIRMED, worse than assumed
The file contains **427 table entries**; the landing-page chip claims
"83 agent skills" (hard-coded from an earlier run). Flooding sources:
LambdaTest test-framework skills, NVIDIA vendor stack, n8n workflows,
Google Mobile-Ads SDKs, marketing/PR skills. Root cause: p5 auto-domain
keyword matching is pure recall (any keyword hit maps the whole skill),
no per-source cap, no relevance tiering; all landing-page numbers are
hard-coded in home.tmpl instead of generated.

### F4 — SKILL.md quality defects: CONFIRMED
- Grammar: intro is unedited ESCO raw text ("Artificial intelligence
  engineer apply methods…", "normally requiring high level of human
  expertise").
- Competence list: `p2_generate.py:255` cuts `essential[:15]` after an
  alphabetical sort → list ends mid-alphabet at "deliver visual
  presentation of data" (a–d only), silently.
- "Hot technologies": alphabetical O*NET dump (Alteryx, Amazon EC2, …),
  not a relevance ranking; O*NET data here is a **proxy**
  (15-2051.00 Data Scientists — no exact O*NET occupation exists).
  The proxy caveat exists in tasks.md/tools.md/footer since 2026-07-08 but
  not yet as the standardized one-liner wherever O*NET data is used.

## Corrections to the briefing assumptions
- None material. One nuance: the "83" landing-page number was accurate for
  the 3-source enrichment run and became stale after the 16-source run —
  the defect is *hard-coded counts*, not a broken counter.

## Phase-0 verification
- Raw ads persisted: **180 slugs** under `data/raw/jobs/*_ads.json` (with
  title/employer/country/provenance) + **180** extraction outputs
  `data/evidence/*.jsonl` — fully re-processable without re-fetching.
  US-side re-classification can run 100% from disk.
- v1 immutable baseline: tag `v1` (af05383b) on branch `master`; work
  continues on `quality/reference-standard`.
- v1 heatmap: pending the Phase-3 linter (by design).
- Eval harvest: the 16 contaminated titles above are the seed for
  `evals/ad-relevance/` (real near-misses, no synthetic ones needed).