--- # VERSION: 2.88.0 name: curator-repo-learn description: "Learn patterns from a specific GitHub repository. Clones, analyzes code structure, extracts patterns, and populates procedural memory. Use for: targeted learning from known quality repos, quick knowledge acquisition, specific pattern extraction. Triggers: /repo-learn, /curator-repo-learn, 'learn from repo'." argument-hint: " [--domain ]" user-invocable: true context: fork allowed-tools: - Task - Bash - Read - Write --- # Curator Repo-Learn Skill (v2.88) **Single Repository Pattern Extraction** - Learn from a specific GitHub repository. ## Role & Priorities **Priorities (ordered):** accuracy → relevance → speed → coverage **Scope:** Clone repository, analyze structure, extract patterns, update procedural memory. ## Agent Teams Integration (v2.88) **Optimal Scenario**: B (Custom Subagents) ### Why Scenario B for Repo-Learn - **Independent operation**: Single repo, single task - **High specialization need**: Pattern recognition requires expertise - **Low coordination need**: No multi-stage pipeline - **Efficient execution**: Direct spawn, no team overhead ### Scenario Analysis | Criterion | Weight | Score | Rationale | |-----------|--------|-------|-----------| | Coordination Need | 25% | 2/10 | Independent operation | | Specialization Need | 25% | 9/10 | Requires pattern recognition | | Quality Gate Need | 20% | 6/10 | Moderate validation needed | | Tool Restriction Need | 15% | 3/10 | Standard tools sufficient | | Scalability | 15% | 2/10 | Single repo focus | | **Total** | 100% | **5.2/10** | Scenario B optimal | ### Workflow (Scenario B) ```yaml # Direct Subagent Spawn Task(subagent_type="ralph-researcher", prompt=""" Analyze repository ${REPO_URL}: 1. Clone repository (shallow) 2. Detect domain and language 3. Scan for: - Function/method patterns - Class/interface definitions - Configuration patterns - Error handling patterns - Testing patterns 4. Extract rules with: - domain: - category: - confidence: 0.75-0.95 - source_repo: ${REPO_URL} - source_file: 5. Update ~/.ralph/procedural/rules.json 6. Create manifest with files[] array Return: patterns extracted, rules added, domain detected """) ``` ## Usage ### Basic Usage ```bash /repo-learn https://github.com/owner/repo ``` ### With Domain Override ```bash /repo-learn https://github.com/nestjs/nest --domain backend ``` ### With Language Hint ```bash /repo-learn https://github.com/vercel/next.js --lang typescript ``` ## Process Flow ``` ┌─────────────────────────────────────────────────────────────┐ │ REPO-LEARN PIPELINE │ ├─────────────────────────────────────────────────────────────┤ │ │ │ 1. VALIDATE URL │ │ ├── Check GitHub format │ │ └── Verify repository exists │ │ │ │ 2. CLONE REPOSITORY │ │ ├── Shallow clone (--depth 1) │ │ └── Store in ~/.ralph/curator/corpus/learning/ │ │ │ │ 3. DETECT METADATA │ │ ├── Domain (backend, frontend, etc.) │ │ ├── Language (typescript, python, etc.) │ │ └── Framework indicators │ │ │ │ 4. SCAN FILES │ │ ├── Source files (*.ts, *.py, etc.) │ │ ├── Configuration files │ │ └── Documentation files │ │ │ │ 5. EXTRACT PATTERNS │ │ ├── Function signatures │ │ ├── Class structures │ │ ├── Import patterns │ │ ├── Error handling patterns │ │ └── Configuration patterns │ │ │ │ 6. CREATE RULES │ │ ├── rule_id: unique identifier │ │ ├── domain: detected or specified │ │ ├── category: sub-domain │ │ ├── confidence: 0.75-0.95 │ │ ├── source_repo: repository URL │ │ ├── source_file: file path │ │ └── behavior: pattern description │ │ │ │ 7. UPDATE PROCEDURAL MEMORY │ │ ├── Backup existing rules.json │ │ ├── Merge new rules (unique_by rule_id) │ │ └── Update manifest │ │ │ │ 8. CREATE MANIFEST (GAP-C01 FIX) │ │ ├── files[]: processed file list │ │ ├── patterns_extracted: count │ │ ├── detected_domain: domain │ │ └── detected_language: language │ │ │ └─────────────────────────────────────────────────────────────┘ ``` ## Domain Detection (GAP-C02 FIX) Automatic domain detection from repository content: ```bash # Domain Keywords backend: api, server, rest, graphql, controller, service frontend: react, vue, angular, component, hook, state database: sql, query, schema, migration, orm, prisma security: auth, jwt, token, encrypt, hash, csrf testing: test, spec, jest, vitest, mock, coverage devops: docker, kubernetes, ci, deploy, pipeline hooks: hook, lifecycle, callback, trigger, event general: config, util, helper, common, shared ``` ## Output Example ```json { "repository": "https://github.com/nestjs/nest", "learned_at": "2026-02-14T22:00:00Z", "detected_domain": "backend", "detected_language": "typescript", "files": [ "packages/core/nest-application.ts", "packages/core/nest-factory.ts", "packages/common/services/logger.service.ts" ], "patterns_extracted": 12, "rules_added": [ { "rule_id": "rule-backend-1739566800-abc123", "domain": "backend", "category": "backend", "source_repo": "https://github.com/nestjs/nest", "source_file": "packages/core/nest-application.ts", "behavior": "Classes: class NestApplication. Functions: async initialize, async dispose.", "confidence": 0.85 } ] } ``` ## Error Handling | Error | Recovery | |-------|----------| | Invalid URL | Show usage, exit | | Repository not found | Suggest alternatives, exit | | Clone failure | Retry with different depth | | No patterns found | Log warning, return empty | | Rules merge failure | Restore from backup | ## Integration with Curator Pipeline ```bash # Quick learning without full pipeline /curator quick --repo owner/repo # Full pipeline for comprehensive learning /curator full --type backend --lang typescript # Single repo learning (this skill) /repo-learn https://github.com/owner/repo ``` ## Related Skills - `/curator` - Full pipeline (Scenario C) - `/smart-fork` - Pattern extraction and forking - `/research` - Repository research ## Files | File | Purpose | |------|---------| | `.claude/scripts/curator-learn.sh` | Pattern extraction (GAP-C01, GAP-C02 fixed) | | `.claude/scripts/curator-ingest.sh` | Repository cloning | | `.claude/scripts/backfill-domains.sh` | Domain backfill for existing rules | | `~/.ralph/procedural/rules.json` | Procedural memory | ## Action Reporting (v2.93.0) **Esta skill genera reportes automáticos completos** para trazabilidad: ### Reporte Automático Cuando esta skill completa, se genera automáticamente: 1. **En la conversación de Claude**: Resultados visibles 2. **En el repositorio**: `docs/actions/curator-repo-learn/{timestamp}.md` 3. **Metadatos JSON**: `.claude/metadata/actions/curator-repo-learn/{timestamp}.json` ### Contenido del Reporte Cada reporte incluye: - ✅ **Summary**: Descripción de la tarea ejecutada - ✅ **Execution Details**: Duración, iteraciones, archivos modificados - ✅ **Results**: Errores encontrados, recomendaciones - ✅ **Next Steps**: Próximas acciones sugeridas ### Ver Reportes Anteriores ```bash # Listar todos los reportes de esta skill ls -lt docs/actions/curator-repo-learn/ # Ver el reporte más reciente cat $(ls -t docs/actions/curator-repo-learn/*.md | head -1) # Buscar reportes fallidos grep -l "Status: FAILED" docs/actions/curator-repo-learn/*.md ``` ### Generación Manual (Opcional) ```bash source .claude/lib/action-report-lib.sh start_action_report "curator-repo-learn" "Task description" # ... ejecución ... complete_action_report "success" "Summary" "Recommendations" ``` ### Referencias del Sistema - [Action Reports System](docs/actions/README.md) - Documentación completa - [action-report-lib.sh](.claude/lib/action-report-lib.sh) - Librería helper - [action-report-generator.sh](.claude/lib/action-report-generator.sh) - Generador - [Learning System Audit](../../../docs/audits/LEARNING_SYSTEM_AUDIT_v2.88.md) - [Learning System Scenarios](../../../docs/architecture/LEARNING_SYSTEM_SCENARIOS_v2.88.md)