Extraction de connaissances PR

VérifiéSûr

Extrait les connaissances utiles des PR (commentaires, fichiers de contexte) et les enregistre sous forme de documentation structurée dans docs/learnings/. Utilisez-le après une revue de pull request ou sur commande /compound pour capitaliser sur les décisions et apprentissages.

Spar Skills Guide Bot
DocumentationIntermédiaire
8002/06/2026
Claude Code
#knowledge-extraction#pr-documentation#learning-docs#automated-documentation

Recommandé pour

Notre avis

Extrait automatiquement les connaissances issues de revues de code et de commentaires de PR pour générer des documents structurés dans docs/learnings/.

Points forts

  • Automatise la documentation post-PR en extrayant les retours pertinents
  • Intègre des sources multiples (PR, fichiers de contexte existants)
  • Gère les doublons et ajoute des références croisées entre documents

Limites

  • Nécessite des outils GitHub CLI (gh) et un dépôt Git configuré
  • Dépend de la qualité des commentaires de PR pour extraire des informations utiles
  • Ne fonctionne qu'avec des PR de la branche courante ou numéro de PR spécifié
Quand l'utiliser

Utilisez cette compétence après avoir fusionné une PR ou lorsque vous voulez documenter des apprentissages issus du processus de revue.

Quand l'éviter

Évitez de l'utiliser pour des changements mineurs sans retours significatifs, ou lorsque le projet ne suit pas la structure .dev/specs/{name}/.

Analyse de sécurité

Sûr
Score qualité92/100

The skill uses Bash only for local file reads and GitHub CLI operations, with no destructive commands, network exfiltration, or execution of arbitrary code. All actions are confined to the workspace and legitimate PR documentation.

Aucun point d'attention détecté

Exemples

Document PR learnings
/compound
Specify PR number
/compound 123
Document from current branch
compound this

name: compound description: | This skill should be used when the user says "/compound", "compound this", "document learnings", "save what we learned", or after completing a PR. Extracts knowledge from PR context and saves to docs/learnings/. allowed-tools:

  • Read
  • Grep
  • Glob
  • Bash
  • Write
  • Edit
  • AskUserQuestion

Compound Skill

Extracts knowledge from PR context and saves structured documentation to docs/learnings/.

Workflow

Phase 1: Context Collection

  1. Identify PR number/branch

    • Use PR number if provided as argument
    • Otherwise, find PR from current branch: gh pr view --json number,body,title
    • If no PR exists: Prompt user to enter PR number directly or confirm proceeding without PR
  2. Extract Plan path

    • Find Plan path pattern in PR body: .dev/specs/{name}/PLAN.md
    • Regex: \.dev/specs/[^/]+/PLAN\.md
    • If no Plan path found: Prompt user to enter spec name directly or select from .dev/specs/ directory listing
  3. Derive Context path

    • Extract spec name from Plan path
    • Context directory: .dev/specs/{name}/context/
  4. Parallel collection (run following commands simultaneously, skip if files don't exist)

    # Context files (treat as empty if not found)
    cat .dev/specs/{name}/context/learnings.md 2>/dev/null || echo ""
    cat .dev/specs/{name}/context/decisions.md 2>/dev/null || echo ""
    cat .dev/specs/{name}/context/issues.md 2>/dev/null || echo ""
    
    # PR comments and reviews (collect as JSON for stability)
    gh pr view {pr_number} --json comments,reviews
    

Error Handling:

  • If no context files exist AND no PR comments → Notify user and request manual input
  • At least 1 source required to proceed with document generation

Phase 2: Knowledge Extraction & Classification

2.1 Extract Valuable Feedback from PR Comments

Criteria for valuable feedback:

  • Code improvement suggestions
  • Bug/issue identification
  • Pattern/best practice mentions
  • "This would be better" type advice
  • Comments left with approval

Filter out:

  • Simple questions ("What is this?")
  • Confirmation requests ("Is this correct?")
  • Approval-only comments ("LGTM", "Approved")
  • Bot comments

Extraction keywords:

  • "suggest", "recommend", "better", "instead"
  • "pattern", "practice", "convention"
  • "issue", "bug", "fix"
  • "learned", "TIL", "note"

Extracted information:

  • author
  • body
  • file_path (if inline comment)
  • created_at

2.2 Analyze Context Files

| File | Purpose | |------|---------| | learnings.md | Direct learnings | | decisions.md | Decision rationale | | issues.md | Out of scope issues (for future reference) |

2.3 Synthesize

  1. Assess documentation value from collected sources
  2. Check for duplicates: Search docs/learnings/
  3. Classify problem type - Refer to references/problem-types.md (relative to this skill directory)
  4. Generate tags

Phase 3: Document Generation

  1. Generate YAML frontmatter

    pr_number: {PR_NUMBER}
    date: {YYYY-MM-DD}
    problem_type: {TYPE}
    tags: [{TAGS}]
    plan_path: {PLAN_PATH}
    
  2. Write document using template

    • Template location: templates/LEARNING_TEMPLATE.md (relative to this skill directory)
    • Read template and substitute placeholders
  3. Determine filename

    • Format: {YYYY-MM-DD}-{short-title}.md
    • Example: 2024-01-15-api-error-handling.md
  4. Save

    • Path: docs/learnings/{filename}.md
  5. Add cross-references (if related documents exist)

    • Add new document link to Related section of existing documents

Usage Examples

# Specify PR number
/compound 123

# Use PR from current branch
/compound

Output

Outputs the created document path and summary:

Created: docs/learnings/2024-01-15-api-error-handling.md

Summary:
- Problem Type: error-handling
- Tags: api, typescript, validation
- Sources: learnings.md, 2 PR comments

<!-- TODO: Future extensions --> <!-- - [ ] Session ID based user feedback collection --> <!-- - [ ] CLAUDE.md auto-update suggestions --> <!-- - [ ] Detect existing document UPDATEs --> <!-- - [ ] Auto-categorization by problem_type (docs/solutions/{type}/) -->
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