af05383bb1
Snapshot before the quality program (relevance gates, tiered mapping, QA linter). v1 is the immutable before/after reference; evidence crawl was at ~175/3039 occupations when tagged. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PDKeXvpT6tENSvyQGLV1Uq
92 lines
3.8 KiB
Python
92 lines
3.8 KiB
Python
"""One-off: manual O*NET mapping for the flagship artificial-intelligence-engineer.
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The official ESCO<->O*NET crosswalk has no entry for this ESCO occupation, so
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tasks.md/tools.md were empty and the provenance pie showed no O*NET segment.
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Nearest O*NET 30.3 occupation: 15-2051.00 Data Scientists (model building,
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deployment, evaluation — the closest day-to-day match for AI engineering).
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Regenerates ONLY references/tasks.md + references/tools.md (p2 format),
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re-inserts the market-evidence section (p3c), updates manifest ids and the
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SKILL.md sources footer + Hot technologies. Does NOT touch the authored
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SKILL.md workflow or the depth files.
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"""
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import json
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import os
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import re
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import sys
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sys.path.insert(0, os.path.dirname(__file__))
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from db import connect
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import p3c_aggregate as p3c
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BASE = os.path.join(os.path.dirname(__file__), "..")
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SLUG = "artificial-intelligence-engineer"
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SOC = "15-2051.00"
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SOC_TITLE = "Data Scientists"
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PKG = os.path.join(BASE, "skills", SLUG)
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def main():
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cn = connect()
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cur = cn.cursor()
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cur.execute("SELECT task, task_type FROM onet_task WHERE soc_code=? ORDER BY task_id", SOC)
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tasks = cur.fetchall()
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cur.execute("""SELECT DISTINCT d.dwa_name FROM onet_task_dwa td
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JOIN onet_dwa d ON d.dwa_id=td.dwa_id
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WHERE td.soc_code=? ORDER BY d.dwa_name""", SOC)
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dwas = [r[0] for r in cur.fetchall()]
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cur.execute("""SELECT example, element_name, hot_technology FROM onet_software
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WHERE soc_code=? ORDER BY hot_technology DESC, example""", SOC)
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software = cur.fetchall()
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label = "artificial intelligence engineer"
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note = (f"Source: O*NET 30.3, occupation {SOC} ({SOC_TITLE}) — manual nearest-"
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f"occupation mapping; the official ESCO crosswalk has no entry for "
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f"this ESCO occupation.")
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# --- tasks.md ---
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tl = [f"# Tasks & work activities — {label}", "", note, "", "## Task statements", ""]
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tl += [f"- **[{t.task_type or 'n/a'}]** {t.task}" for t in tasks]
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if dwas:
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tl += ["", "## Detailed work activities", ""] + [f"- {d}" for d in dwas]
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open(os.path.join(PKG, "references", "tasks.md"), "w", encoding="utf-8",
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newline="\n").write("\n".join(tl) + "\n")
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# --- tools.md (market section is re-added by p3c below) ---
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ol = [f"# Tools & technology — {label}", "", note, "",
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"| Software | Category | Hot technology |", "|---|---|---|"]
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ol += [f"| {s.example} | {s.element_name} | {'yes' if s.hot_technology == 'Y' else ''} |"
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for s in software]
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open(os.path.join(PKG, "references", "tools.md"), "w", encoding="utf-8",
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newline="\n").write("\n".join(ol) + "\n")
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# --- manifest ---
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mp = os.path.join(PKG, "manifest.json")
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m = json.load(open(mp, encoding="utf-8"))
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m["ids"]["onet_soc"] = SOC
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m["ids"]["crosswalk_match"] = f"manual nearest occupation ({SOC_TITLE})"
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json.dump(m, open(mp, "w", encoding="utf-8"), indent=2)
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# --- SKILL.md: sources footer + Hot technologies ---
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sp = os.path.join(PKG, "SKILL.md")
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text = open(sp, encoding="utf-8").read()
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text = text.replace("O*NET 30.3 (None)", f"O*NET 30.3 ({SOC}, manual nearest match)")
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if "## Hot technologies" not in text:
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hot = [s.example for s in software if s.hot_technology == "Y"][:10]
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if hot:
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block = "## Hot technologies\n\n" + "\n".join(f"- {h}" for h in hot) + "\n\n"
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text = re.sub(r"(?m)^---\n\*Sources:", block + "---\n*Sources:", text, count=1)
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open(sp, "w", encoding="utf-8", newline="\n").write(text)
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# --- market-evidence sections back into tools/skills ---
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p3c.aggregate_for_occupation(cn, SLUG, os.path.join(BASE, "skills"))
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cn.close()
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print(f"tasks: {len(tasks)}, dwas: {len(dwas)}, software: {len(software)} — "
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f"{SLUG} remapped to {SOC} ({SOC_TITLE})")
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if __name__ == "__main__":
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main()
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