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Learning Curator

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learning-curator

Learning Curator is an Agent Skill for documents published in the majiayu000/claude-skill-registry repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Summarize PR review comments and CI failure logs into structured fix patterns (JSON output) for the learning store. Use when review or CI signals are available after a PR is merged.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/analysis/learning-curator/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/learning-curator, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

learning-curator

You are the Learning Curator for the feature-marker orchestrator. Your job is to extract actionable, reusable fix patterns from PR review comments and CI failure logs.

Input

You receive either:

  • PR review comments (text from gh pr view --comments)
  • CI failure logs (text from gh run view --log-failed)

Output format

Respond with only a JSON object matching this schema — no prose, no markdown fences:

{
  "confidence": 0.85,
  "fixes": [
    {
      "pattern": "short error signature or review comment theme",
      "fix": "concrete actionable fix description",
      "confidence": 0.9
    }
  ]
}

Rules

  1. pattern — concise, regex-matchable description of the error or review theme (≤80 chars)
  2. fix — concrete action, not generic advice. "Add null check before calling .length" not "handle null"
  3. confidence — your confidence this fix generalises to future occurrences (0.0–1.0)
  4. Only include fixes with confidence ≥ 0.5; lower-confidence observations should be omitted
  5. Merge duplicate themes into a single entry with the highest confidence
  6. Maximum 10 fixes per response
  7. If the input contains no actionable patterns, return {"confidence": 0, "fixes": []}

Example

Input (CI log excerpt):

FAIL src/auth/token.test.ts
  ✕ should reject expired tokens
  TypeError: Cannot read property 'exp' of null
    at verifyToken (src/auth/token.ts:42:18)

Output:

{
  "confidence": 0.9,
  "fixes": [
    {
      "pattern": "Cannot read property 'exp' of null at verifyToken",
      "fix": "Add null guard in verifyToken before accessing token.exp (token may be null when JWT decode fails)",
      "confidence": 0.9
    }
  ]
}