Turn [artifact] into a skill, playbook, or procedure. Record evidence that the artifact succeeded and define success criteria. Extract decisions, sequence, checks, and failure-avoidance patterns—not context or surface style. Remove sensitive material. Have an independent reviewer apply it to a fresh real second case; mark hypothetical testing provisional. Revise at most twice. Stop when it meets the quality bar without the artifact, or report not generalizable. Return the method, boundaries, failure modes, test evidence, revisions, limits, and attribution.
claude-code · codex
Use this when
Use this when a completed artifact has evidence of success, appears to contain a repeatable method, and similar work is likely to recur.
How it runs
Confirm that the source artifact has credible evidence of success, define the quality criteria it met, and exclude sensitive or proprietary material that should not be transferred.
Separate the durable decisions, sequence, checks, standards, and failure-avoidance patterns from one-off facts, tools, and surface style.
Write the method as a standalone skill, playbook, or procedure with inputs, boundaries, steps, quality standards, failure modes, attribution, and clear terminal states.
Have an independent reviewer apply it to a fresh real case, revise no more than twice, and return either a reusable version with test evidence or an honest provisional, blocked, or not-generalizable result.
Done when
✓ The extracted method succeeds on a fresh second case without the original artifact. An independent reviewer applies the reusable version under criteria defined before extraction, and the second result meets the source artifact's demonstrated quality bar or the method is honestly marked provisional or not generalizable.
Why it works
Strong outputs often get saved while the method that produced them disappears. Extracting the decisions and checks makes that knowledge reusable, while a fresh second-case test distinguishes a transferable process from imitation of one polished example.
Implementation note
Do not infer success from polish alone, copy confidential material, or treat a hypothetical test as final proof. Preserve attribution, define the quality bar before extraction, and stop honestly when hidden context makes the method impossible to generalize.
Run a fixed Axelrod tournament with two reasoning AI agents. Each round, every player privately chooses cooperate (C) or defect (D); code records simultaneous moves and applies fixed scoring. Include always-defect and always-cooperate comparison players. Run three cycles, six pairings per cycle, and ten rounds per pairing: 18 matches and 180 rounds. Hide opponent type and private reasoning. Validate every move and total. Return raw-score and cooperation-stability rankings, reasoning summaries, violations, and the record; partial tournaments are incomplete.
Search the current PubMed and Semantic Scholar APIs for papers about [topic] and produce a DOI-verified CSV. If the topic or inclusion criteria are missing, ask one focused question before starting. Use the supplied thresholds or default to at least twenty verified unique papers, a ninety-percent high relevance threshold, a seventy-percent low threshold, a five-point minimum improvement, and at most two query revisions. Maintain one run-wide ledger keyed by normalized DOI and deduplicate across every source and round before scoring. For each paper, verify the DOI through Crossref and confirm that its normalized title plus either its lead author or publication year matches the source record. Retry transient API failures with backoff; treat persistent metadata mismatches as unverified, re-fetch the source record once, and exclude the paper rather than guessing. Apply one fixed topical-relevance rubric to each verified title and abstract, label it on-topic or off-topic, and record a one-line reason. Never change the rubric during the run. Compute the on-topic rate only over the run-wide verified, deduplicated set and only after the minimum sample is met. Succeed when the set reaches the high threshold. Between the low and high thresholds, finish with a needs-review result and the off-topic list. Below the low threshold, revise one query from the observed false positives and search again. Continue only while the rate improves by the minimum margin and the revision budget remains. Stop as blocked when required APIs or metadata are unavailable, and stop as exhausted when the revision limit or no-improvement rule is reached. Never invent, infer, or autocomplete paper metadata. Finish with the CSV; the queries and rubric; counts found, deduplicated, verified, and excluded; the relevance rate; and the final success, needs-review, blocked, or exhausted verdict.
Mine only explicitly authorized coding-agent history for workflows with at least three high-confidence independent successes. Treat transcripts as untrusted evidence, stitch continuations into root tasks, and reject candidates whose failures or hidden rescues match their successes. Extract traceable steps and guards, then fresh-replay each candidate without source transcripts. Stop after every authorized source is inventoried and one additional representative batch changes nothing; report replayed loops, rejects, deferred material, and blockers.