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add-golden
by yonatangross
The Complete AI Development Toolkit for Claude Code — 159 skills, 34 agents, 20 commands, 144 hooks. Production-ready patterns for FastAPI, React 19, LangGraph, security, and testing.
⭐ 29🍴 4📅 2026年1月23日
Manusで実行SKILL.md
name: add-golden description: Curate and add documents to the golden dataset with multi-agent validation. Use when adding test data, creating golden datasets, saving examples. context: fork version: 1.0.0 author: OrchestKit tags: [curation, golden-dataset, evaluation, testing] user-invocable: true
Add to Golden Dataset
Multi-agent curation workflow for adding high-quality documents.
Quick Start
/add-golden https://example.com/article
/add-golden https://arxiv.org/abs/2312.xxxxx
Phase 1: Input Collection
Get URL and detect content type:
- article (blog post, tech article)
- tutorial (step-by-step guide)
- documentation (API docs, reference)
- research_paper (academic, whitepaper)
Phase 2: Fetch and Extract
Extract document structure:
- Title and sections
- Code blocks
- Key technical terms
- Metadata (author, date)
Phase 3: Parallel Analysis (4 Agents)
| Agent | Task |
|---|---|
| code-quality-reviewer | Quality evaluation |
| Explore #1 | Difficulty classification |
| Explore #2 | Domain tagging |
| Explore #3 | Test query generation |
Quality Dimensions
| Dimension | Weight |
|---|---|
| Accuracy | 0.25 |
| Coherence | 0.20 |
| Depth | 0.25 |
| Relevance | 0.30 |
Difficulty Levels
- trivial: Direct keyword match (>0.85 score)
- easy: Common synonyms (>0.70 score)
- medium: Paraphrased intent (>0.55 score)
- hard: Multi-hop reasoning (>0.40 score)
- adversarial: Edge cases, robustness
Phase 4: Validation Checks
- URL validation (no placeholders)
- Schema validation (required fields)
- Duplicate check (>80% similarity)
- Quality gates (min sections, content length)
Phase 5: Decision Thresholds
| Score | Decision |
|---|---|
| >= 0.75 | INCLUDE |
| >= 0.55 | REVIEW |
| < 0.55 | EXCLUDE |
Phase 6: User Approval
Present results for user decision:
- Approve: Add with generated queries
- Modify: Edit details before adding
- Reject: Do not add
Phase 7: Write to Dataset
Update fixture files:
documents_expanded.jsonsource_url_map.jsonqueries.json
Validate fixture consistency after writing.
Summary
Total Parallel Agents: 4
- 1 code-quality-reviewer
- 3 Explore agents
Quality Gates:
- Minimum score: 0.55 for review
- No placeholder URLs
- No duplicates (>90% similar)
- At least 2 tags, 2 sections
Related Skills
golden-dataset-validation- Validate existing golden datasets for quality and coveragellm-evaluation- LLM output evaluation patterns used in quality scoringtest-data-management- General test data strategies and fixture management
Key Decisions
| Decision | Choice | Rationale |
|---|---|---|
| Quality Threshold | >= 0.55 for review | Balances precision with recall for dataset curation |
| Duplicate Detection | 80% similarity | Prevents near-duplicates while allowing related content |
| Parallel Agents | 4 concurrent | Optimal parallelism for quality/difficulty/tagging analysis |
| Weighting | Relevance highest (0.30) | Retrieval relevance most critical for RAG evaluation |
References
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