| name | curator |
| description | Full curator pipeline for autonomous learning from quality repositories. Executes: discovery → scoring → ranking → ingest → learn → vault sync. Writes to procedural memory AND Obsidian vault for Graph View visualization and graduation pipeline. Use for: populating procedural memory with domain patterns, first-time domain learning, comprehensive knowledge building. Triggers: /curator full, 'learn patterns from repos', 'build knowledge base'. |
| argument-hint | [full|quick|status] --type <domain> --lang <language> |
| user-invocable | true |
| context | fork |
| allowed-tools | ["Task","Bash","Read","Write","WebSearch","WebFetch"] |
Curator Pipeline Skill (v3.1.0)
Full Autonomous Learning Pipeline - Discovers, scores, and learns from quality repositories.
Role & Priorities
Priorities (ordered): quality → coverage → relevance → performance → speed
Scope: Repository discovery, quality scoring, pattern extraction, procedural memory population.
Agent Teams Integration (v2.88)
Optimal Scenario: C (Integrated)
Why Scenario C for Curator
- High coordination need: 5+ sequential pipeline stages
- Quality gates required: Each stage needs validation before proceeding
- Multi-tool operations: GitHub API, git, file processing, JSON manipulation
- Scalability: Can process multiple repositories in parallel
Scenario Analysis
| Criterion | Weight | Score | Rationale |
|---|
| Coordination Need | 25% | 8/10 | Multi-stage pipeline requires orchestration |
| Specialization Need | 25% | 5/10 | General API/git skills sufficient |
| Quality Gate Need | 20% | 9/10 | Each stage needs validation |
| Tool Restriction Need | 15% | 3/10 | Needs broad tool access |
| Scalability | 15% | 8/10 | Can process many repos |
| Total | 100% | 6.9/10 | Scenario C optimal |
Workflow (Scenario C)
TeamCreate(team_name="curator-pipeline", description="Learning from ${DOMAIN} repos")
Task(subagent_type="ralph-researcher", prompt="Search GitHub for ${DOMAIN} repositories")
→ Returns candidate list
Task(subagent_type="ralph-reviewer", prompt="Score ${REPO_1} quality")
Task(subagent_type="ralph-reviewer", prompt="Score ${REPO_2} quality")
→ Returns quality scores
Team lead aggregates scores and selects top N
Task(subagent_type="ralph-coder", prompt="Clone and extract patterns from ${TOP_REPO}")
→ Returns extracted patterns
TeammateIdle hook validates pattern quality
TaskCompleted hook verifies manifest population
Procedural memory updated automatically
Pipeline Stages
1. Discovery (curator-discovery.sh)
--type <domain>
--lang <language>
--tier <tier>
Output: Candidate repository list with metadata.
2. Scoring (curator-scoring.sh)
Quality metrics:
- Star count and trend
- Recent commit activity
- Documentation quality
- Test coverage indicators
- Organization reputation
Output: Scored repository list (0-100).
3. Ranking (curator-rank.sh)
--max <n>
--diversity
Output: Ranked candidate list.
4. Ingest (curator-ingest.sh)
--clone-depth 1
Output: Cloned repositories in corpus/pending/.
5. Approve (curator-approve.sh)
--auto
--threshold 75
Output: Repositories moved to corpus/approved/.
6. Learn (curator-learn.sh) - GAP FIXES v2.88
Output:
- Updated
.claude/rules/learned/ (MemPalace taxonomy)
- Manifest with
files[] array
- Domain-categorized rules
Commands
Full Pipeline
/curator full --type backend --lang typescript
Executes all stages: discovery → scoring → ranking → ingest → approve → learn.
Quick Pipeline
/curator quick --type security --lang python --repo owner/repo
Skips discovery, learns from specific repository.
Status Check
/curator status
Shows:
- Approved repositories count
- Rules per domain
- Learning gaps
Configuration
{
"curator": {
"max_repos_per_run": 3,
"min_stars": 100,
"clone_depth": 1,
"auto_approve_threshold": 75,
"domains": ["backend", "frontend", "database", "security", "devops", "testing"]
},
"auto_learn": {
"enabled": true,
"blocking": false,
"min_rules_domain": 3
}
}
Quality Gates (v2.88)
| Stage | Gate | Failure Action |
|---|
| Discovery | Results > 0 | Retry with broader search |
| Scoring | Top score >= 60 | Lower threshold or expand search |
| Ingest | Clone success | Skip repo, continue |
| Learn | Patterns > 0 | Log warning, proceed |
GAP Fixes Applied (v2.88)
GAP-C01: Manifest Files[] Population
Before:
{"files": [], "patterns_extracted": 0}
After:
{
"files": ["src/handler.ts", "src/middleware.ts"],
"patterns_extracted": 5,
"detected_domain": "backend",
"detected_language": "typescript"
}
GAP-C02: Domain Detection
Rules now automatically categorized:
- Keyword analysis of repository content
- File extension detection
- Configuration file inspection
Related Skills
/curator-repo-learn - Single repository learning (Scenario B)/repo-learn - Alias for curator-repo-learn/smart-fork - Pattern extraction from external repos
Hooks Integration
| Hook | Trigger | Purpose |
|---|
orchestrator-auto-learn.sh | PreToolUse (Task) | Detect learning gaps |
| (removed in v3.0) | UserPromptSubmit | (curator-suggestion.sh deleted) |
continuous-learning.sh | Stop | Extract from session → vault |
vault-index-updater.sh | SessionEnd | Update vault indices |
Action Reporting (v2.93.0)
Esta skill genera reportes automáticos completos para trazabilidad:
Reporte Automático
Cuando esta skill completa, se genera automáticamente:
- En la conversación de Claude: Resultados visibles
- En el repositorio:
docs/actions/curator/{timestamp}.md
- Metadatos JSON:
.claude/metadata/actions/curator/{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
ls -lt docs/actions/curator/
cat $(ls -t docs/actions/curator/*.md | head -1)
grep -l "Status: FAILED" docs/actions/curator/*.md
Generación Manual (Opcional)
source .claude/lib/action-report-lib.sh
start_action_report "curator" "Task description"
complete_action_report "success" "Summary" "Recommendations"
Referencias del Sistema
