How do I install the Add Golden skill?

Run npx skillmds add majiayu000/add-golden in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Add Golden safe to use?

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Which AI agents work with Add Golden?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Add Golden free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Add Golden?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Add Golden by majiayu000 — Agent Skill

Name: Add Golden
Rating: 4.5 (4 reviews)
Author: majiayu000

File contents

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.json
source_url_map.json
queries.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 coverage
llm-evaluation - LLM output evaluation patterns used in quality scoring
test-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

Quality Scoring

---name: add-goldendescription: Curate and add documents to the golden dataset with multi-agent validation. Use when adding test data, creating golden datasets, saving examples.--- # Add to Golden Dataset Multi-agent curation workflow for adding high-quality documents. ## Quick Start ```bash /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.json` - `source_url_map.json` - `queries.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 coverage - `llm-evaluation` - LLM output evaluation patterns used in quality scoring - `test-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 - [Quality Scoring](references/quality-scoring.md)

Frequently asked questions