feat(gen): SKILL.md quality hardening + reviewed overlay layer
- intro: cached gemma3 rewrite to one clean paragraph (raw ESCO stays the provenance reference); overlays/<slug>/intro.md wins when present - competences: full essential list, the silent essential[:15] alphabetical cut is gone - hot technologies: derived by p3c from post-gate market ranking (omitted below 30 gated ads), no longer the alphabetical O*NET dump - standardized O*NET proxy caveat for non-exact crosswalk matches - overlays/<slug>/workflow.md preserves curated core workflows across regeneration (flagship overlay included) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PDKeXvpT6tENSvyQGLV1Uq
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skillfactor-pipeline
7
overlays/artificial-intelligence-engineer/intro.md
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7
overlays/artificial-intelligence-engineer/intro.md
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Artificial intelligence engineers apply AI methods from engineering, robotics
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and computer science to design programs that simulate intelligence — including
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reasoning models, cognitive and knowledge-based systems, problem solving and
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decision making. They also integrate structured knowledge into computer
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systems (ontologies, knowledge bases) to solve complex problems that normally
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require a high level of human expertise, and they carry models from prototype
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to monitored production systems.
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16
overlays/artificial-intelligence-engineer/workflow.md
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16
overlays/artificial-intelligence-engineer/workflow.md
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1. Frame the problem: target metric, baseline, cost of errors, and whether
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AI is warranted at all (see references/intake.md — mandatory questions).
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2. Assess and prepare the data: sources, quality, labels, legal basis,
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leakage risks; build the evaluation set before the model.
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3. Choose the solution tier: rules/heuristic baseline → prompting an
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existing model → RAG over private data → fine-tuning → custom training.
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4. Build the pipeline: versioned data, features/prompts, training or
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orchestration code, reproducible experiments.
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5. Evaluate offline against the baseline — overall and per segment; for
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generative systems run the golden-example eval suite.
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6. Ship safely: shadow mode → canary → ramp-up, with rollback and
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guardrails (input validation, output schemas, human fallback).
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7. Monitor in production: drift, quality proxies, cost, latency; alert
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thresholds with owners.
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8. Iterate and govern: retraining triggers, model cards, bias reviews,
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compliance (e.g. EU AI Act risk class).
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@@ -19,6 +19,70 @@ sys.path.insert(0, os.path.dirname(__file__))
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from db import connect
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SKILLS_DIR = os.path.join(os.path.dirname(__file__), "..", "skills")
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OVERLAYS_DIR = os.path.join(os.path.dirname(__file__), "..", "overlays")
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REWRITE_CACHE = os.path.join(os.path.dirname(__file__), "..", "data", "rewrites")
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def clean_intro(slug, raw_text):
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"""Phase 2d: one grammatically clean English paragraph instead of raw
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ESCO text. gemma3-rewritten once, cached in data/rewrites/<slug>.intro.txt
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(re-runs cost nothing); falls back to the raw text when Ollama is
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unreachable. The raw ESCO original stays reachable via the ESCO URI in
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the sources footer."""
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if not raw_text:
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return raw_text
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os.makedirs(REWRITE_CACHE, exist_ok=True)
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cache = os.path.join(REWRITE_CACHE, f"{slug}.intro.txt")
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if os.path.exists(cache):
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cached = open(cache, encoding="utf-8").read().strip()
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if cached:
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return cached
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try:
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import requests
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from dotenv import load_dotenv
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load_dotenv(os.path.join(os.path.dirname(__file__), "..", ".env"))
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url = os.environ.get("OLLAMA_URL", "").rstrip("/")
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model = os.environ.get("OLLAMA_MODEL", "")
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if not url or not model:
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return raw_text
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prompt = (
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"Rewrite the following occupation description as ONE grammatically "
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"correct, clear English paragraph. Fix subject-verb agreement and "
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"awkward phrasing. Preserve every fact; add nothing; remove "
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"nothing substantive. Output only the paragraph.\n\n" + raw_text[:1500])
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r = requests.post(f"{url}/api/generate", json={
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"model": model, "prompt": prompt, "stream": False,
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"options": {"temperature": 0, "num_ctx": 2048, "num_predict": 400},
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}, timeout=120)
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r.raise_for_status()
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text = (r.json().get("response") or "").strip()
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# sanity: rewrite must stay a single plausible paragraph
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if 80 <= len(text) <= max(400, int(len(raw_text) * 1.6)) and "\n\n" not in text:
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open(cache, "w", encoding="utf-8", newline="\n").write(text + "\n")
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return text
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except Exception:
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pass
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return raw_text
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def apply_overlays(slug, text):
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"""Reviewed manual curation that must SURVIVE regeneration (phase 2d).
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overlays/<slug>/intro.md -> replaces the intro paragraph
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overlays/<slug>/workflow.md -> replaces the '## Core workflow' body"""
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odir = os.path.join(OVERLAYS_DIR, slug)
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if not os.path.isdir(odir):
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return text
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intro_p = os.path.join(odir, "intro.md")
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if os.path.isfile(intro_p):
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intro = open(intro_p, encoding="utf-8").read().strip()
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text = re.sub(r"(?ms)(^---\n.*?\n---\n\n# [^\n]+\n\n).*?(?=\n\n## )",
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lambda m: m.group(1) + intro, text, count=1)
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wf_p = os.path.join(odir, "workflow.md")
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if os.path.isfile(wf_p):
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wf = open(wf_p, encoding="utf-8").read().strip()
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text = re.sub(r"(?ms)(^## Core workflow\n\n).*?(?=\n\n## )",
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lambda m: m.group(1) + wf, text, count=1)
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return text
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TARGET_COUNT = 100
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# ISCO-08 prefixes for knowledge work (management, business, IT, HR, finance,
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@@ -199,7 +263,6 @@ def gen_package(cn, r):
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[t["task"] for t in tasks][:8]
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essential = [s for s in skills if s["relation_type"] == "essential"]
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optional = [s for s in skills if s["relation_type"] != "essential"]
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hot = [s for s in software if s["hot_technology"] == "Y"]
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trigger_examples = "; ".join(core_tasks[:3]) if core_tasks else \
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f"typical {label} responsibilities"
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@@ -210,6 +273,9 @@ def gen_package(cn, r):
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)
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# --- SKILL.md (< 300 lines, progressive disclosure into references/) ---
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# Phase 2d: intro = grammatically clean rewrite (cached, gemma3);
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# the raw ESCO text stays reachable via the provenance link in the footer.
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intro = clean_intro(slug, (r["description"] or r["definition"] or "").strip())
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lines = [
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"---",
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f"name: {slug}",
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@@ -218,7 +284,7 @@ def gen_package(cn, r):
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"",
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f"# {label.title()}",
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"",
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(r["description"] or r["definition"] or "").strip(),
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intro,
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"",
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"## Core workflow",
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"",
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@@ -236,6 +302,7 @@ def gen_package(cn, r):
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]
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# enrichment files (phase 3) survive regeneration — link them when present
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enrichment = [
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("references/ai-skills.md", "matched external AI agent skills (tiered, per-source attribution)"),
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("references/market.md", "FULL market-evidence report from real job ads (rankings, seniority, titles)"),
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("references/usecases.md", "ranked work packages the agent can take over"),
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("references/intake.md", "mandatory questions before starting any task"),
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@@ -252,25 +319,25 @@ def gen_package(cn, r):
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"## Key competences (essential)",
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"",
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]
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for s in essential[:15]:
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# Phase 2d: FULL essential list — never a silently cut alphabetical prefix.
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for s in essential:
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lines.append(f"- {s['preferred_label']}")
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if hot:
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lines += ["", "## Hot technologies", ""]
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seen = set()
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for s in hot:
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if s["example"] not in seen:
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lines.append(f"- {s['example']}")
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seen.add(s["example"])
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if len(seen) >= 10:
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break
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# "Hot technologies" intentionally NOT emitted here: it is derived from
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# post-gate market evidence by p3c (omitted when the corpus is too thin),
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# never from the alphabetical O*NET dump.
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lines += [
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"",
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"---",
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f"*Sources: ESCO v1.2.1 ({r['concept_uri']}), O*NET 30.3 ({r['onet_id']}). "
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"See manifest.json for licensing/attribution.*",
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"",
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]
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write(os.path.join(base, "SKILL.md"), "\n".join(lines))
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# Phase 2d: standardized proxy caveat wherever O*NET data is not an exact match
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if r.get("onet_id") and (r.get("match_type") or "").lower() not in ("exact", ""):
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lines.append(f"*O*NET nearest match: {r['onet_id']} {r.get('onet_title', '')} "
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"(proxy — no exact O*NET occupation exists).*")
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lines.append("")
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write(os.path.join(base, "SKILL.md"),
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apply_overlays(slug, "\n".join(lines)))
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# --- references/profile.md ---
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prof = [
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@@ -189,6 +189,39 @@ def aggregate_for_occupation(cn, slug, skills_dir, max_retries=3):
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by_file.setdefault(SECTION_FOR[etype], {}).setdefault(etype, []).append(
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(entity, pct))
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# Phase 2d: "Hot technologies" in SKILL.md — from the post-gate
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# market ranking, never the alphabetical O*NET dump. Omitted when
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# the corpus is too thin for a ranking.
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HOT_MARKER = "<!-- hot-tech -->"
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skill_path = os.path.join(skills_dir, slug, "SKILL.md")
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if os.path.exists(skill_path):
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stext = open(skill_path, encoding="utf-8").read()
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stext = re.sub(rf"\n?{HOT_MARKER}.*?{HOT_MARKER}\n?", "",
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stext, flags=re.S)
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if total >= 30:
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hot_rows = cur.execute("""
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SELECT TOP 10 ee.entity, COUNT(DISTINCT ee.job_id) AS jobs
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FROM evidence_entity ee
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JOIN evidence_job ej ON ej.job_id=ee.job_id
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WHERE ej.occupation_slug=? AND ee.entity_type='tools'
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GROUP BY ee.entity HAVING COUNT(DISTINCT ee.job_id) >= ?
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ORDER BY COUNT(DISTINCT ee.job_id) DESC, ee.entity
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""", slug, REPORT_MIN_JOBS).fetchall()
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if hot_rows:
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block = [f"\n{HOT_MARKER}", "",
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"## Hot technologies",
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"",
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f"Top tools from {total} gated job ads "
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f"(see references/market.md, as of "
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f"{date.today().isoformat()}):", ""]
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block += [f"- {e} — {round(100.0*n/total)} %"
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for e, n in hot_rows]
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block += ["", HOT_MARKER]
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stext = re.sub(r"(?m)^---\n\*Sources:",
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"\n".join(block) + "\n\n---\n*Sources:",
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stext, count=1)
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open(skill_path, "w", encoding="utf-8", newline="\n").write(stext)
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stamp = date.today().isoformat()
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for fname, groups in by_file.items():
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path = os.path.join(skills_dir, slug, "references", fname)
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