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Zhongwei Li
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skills/skill/SKILL.md
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skills/skill/SKILL.md
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---
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name: skill-factory
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description: Autonomous skill creation agent that analyzes requests, automatically selects the best creation method (documentation scraping via Skill_Seekers, manual TDD construction, or hybrid), ensures quality compliance with Anthropic best practices, and delivers production-ready skills without requiring user decision-making or navigation
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when_to_use: when you need to create any Claude skill and want it done automatically with guaranteed quality - works for documentation-based skills, GitHub repositories, PDFs, custom workflows, or hybrid approaches
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version: 0.1.0
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---
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# Skill Factory
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**Autonomous skill creation - just tell me what you need, I'll handle everything.**
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## What This Does
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You request a skill, I deliver a production-ready skill with guaranteed quality (score >= 8.0/10).
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**No decision-making required. No tool selection. No quality checking. Just results.**
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### Anthropic's Official Best Practices
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For comprehensive guidance on creating effective skills, see:
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- **[references/overview.md](references/overview.md)** - Complete overview of Agent Skills architecture, progressive disclosure, and how Skills work across different platforms (API, Claude Code, Agent SDK, claude.ai)
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- **[references/quickstart.md](references/quickstart.md)** - Quick tutorial on using pre-built Agent Skills in the Claude API with practical code examples
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- **[references/best-practices.md](references/best-practices.md)** - Detailed authoring best practices including core principles, skill structure, progressive disclosure patterns, workflows, evaluation strategies, and common patterns
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- **[references/anthropic-best-practices.md](references/anthropic-best-practices.md)** - Quality scoring system (10/10 criteria) used by skill-factory
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These references provide Anthropic's official guidance and are consulted during the quality assurance phase.
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## Usage
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Simply describe the skill you need:
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```
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"Create a skill for Anchor development with latest docs and best practices"
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"Create a React skill from react.dev with comprehensive examples"
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"Create a skill for Solana transaction debugging workflows"
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"Create a skill for writing technical documentation following company standards"
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```
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**I will automatically:**
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1. ✅ Analyze your request
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2. ✅ Select the optimal creation method
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3. ✅ Create the skill
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4. ✅ Run quality assurance loops (until score >= 8.0)
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5. ✅ Test with automated scenarios
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6. ✅ Deliver ready-to-use skill with stats
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## What You Get
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```
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✅ anchor-development skill ready!
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📊 Quality Score: 8.9/10 (Excellent)
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📝 Lines: 412 (using progressive disclosure)
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📚 Coverage: 247 documentation pages
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💡 Examples: 68 code samples
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🧪 Test Pass Rate: 100% (15/15 scenarios)
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📁 Location: ~/.claude/skills/anchor-development/
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📦 Zip: ~/Downloads/anchor-development.zip
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Try it: "How do I create an Anchor program?"
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```
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## How It Works (Behind the Scenes)
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### Phase 1: Request Analysis (Automatic)
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I analyze your request to determine:
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**Source Detection:**
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- Documentation URL/mention? → Automated scraping path
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- "Latest docs", "current version"? → Automated path
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- GitHub repository mention? → Automated path
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- PDF/manual path? → Automated path
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- Custom workflow/process description? → Manual TDD path
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- Both documentation AND custom needs? → Hybrid path
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**Quality Requirements Extraction:**
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- "Best practices" → Enforce quality gates
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- "Latest version" → Scrape current docs
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- "Examples" → Ensure code samples included
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- "Comprehensive" → Verify coverage completeness
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### Phase 2: Execution (Automatic)
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**Path A: Documentation-Based (Skill_Seekers)**
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```
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Detected: Documentation source available
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Method: Automated scraping with quality enhancement
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Steps I take:
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1. Check Skill_Seekers installation (install if needed)
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2. Configure scraping parameters automatically
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3. Run scraping with optimal settings
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4. Monitor progress
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5. Initial quality check
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6. If score < 8.0: Run enhancement loop
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7. Re-score until >= 8.0
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8. Test with auto-generated scenarios
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9. Package and deliver
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```
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**Path B: Custom Workflows (Manual TDD)**
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```
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Detected: Custom workflow/process
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Method: Test-Driven Documentation (obra methodology)
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Steps I take:
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1. Create pressure test scenarios
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2. Run baseline (without skill)
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3. Document agent behavior
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4. Write minimal skill addressing baseline
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5. Test with skill present
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6. Identify rationalizations/gaps
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7. Close loopholes
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8. Iterate until bulletproof
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9. Package and deliver
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```
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**Path C: Hybrid**
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```
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Detected: Documentation + custom requirements
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Method: Scrape then enhance
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Steps I take:
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1. Scrape documentation (Path A)
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2. Identify gaps vs requirements
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3. Fill gaps with TDD approach (Path B)
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4. Unify and test as whole
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5. Quality loop until >= 8.0
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6. Package and deliver
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```
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### Phase 3: Quality Assurance Loop (Automatic)
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**I enforce Anthropic best practices:**
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```python
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while quality_score < 8.0:
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issues = analyze_against_anthropic_guidelines(skill)
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if "vague_description" in issues:
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improve_description_specificity()
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if "missing_examples" in issues:
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extract_or_generate_examples()
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if "too_long" in issues:
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apply_progressive_disclosure()
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if "poor_structure" in issues:
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reorganize_content()
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quality_score = rescore()
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```
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**Quality Criteria (Anthropic Best Practices):**
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- ✅ Description: Specific, clear, includes when_to_use
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- ✅ Conciseness: <500 lines OR progressive disclosure
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- ✅ Examples: Concrete code samples, not abstract
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- ✅ Structure: Well-organized, clear sections
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- ✅ Name: Follows conventions (lowercase, hyphens, descriptive)
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**Important**: The quality assurance process consults [references/best-practices.md](references/best-practices.md) for Anthropic's complete authoring guidelines and [references/anthropic-best-practices.md](references/anthropic-best-practices.md) for the 10-point scoring criteria.
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### Phase 4: Testing (Automatic)
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**I generate and run test scenarios:**
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```python
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# Auto-generate test cases from skill content
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test_cases = extract_key_topics(skill)
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for topic in test_cases:
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query = f"How do I {topic}?"
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# Test WITHOUT skill (baseline)
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baseline = run_query_without_skill(query)
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# Test WITH skill
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with_skill = run_query_with_skill(query)
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# Verify improvement
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if not is_better(with_skill, baseline):
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identify_gap()
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enhance_skill()
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retest()
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```
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### Phase 5: Delivery (Automatic)
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```
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Package skill:
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- Create skill directory structure
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- Generate SKILL.md with frontmatter
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- Create reference files (if using progressive disclosure)
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- Add examples directory
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- Create .zip for easy upload
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- Install to ~/.claude/skills/ (if desired)
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- Generate summary statistics
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```
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## Progress Reporting
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You'll see real-time progress:
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```
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🔍 Analyzing request...
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✅ Detected: Documentation-based (docs.rs/anchor-lang)
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✅ Requirements: Latest version, best practices, examples
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🔄 Creating skill...
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📥 Scraping docs.rs/anchor-lang... (2 min)
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📚 Extracting 247 pages...
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💾 Organizing content...
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📊 Quality check: 7.4/10
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⚠️ Issues found:
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- Description too generic (fixing...)
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- Missing examples in 4 sections (adding...)
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- Some outdated patterns (updating...)
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🔧 Enhancing skill...
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✏️ Description improved
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📝 Examples added
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🔄 Patterns updated
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📊 Quality check: 8.9/10 ✅
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🧪 Testing...
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✅ 15/15 scenarios passing
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✅ anchor-development skill ready!
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```
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## Dependencies
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**Required:**
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- Python 3.10+ (for quality scripts)
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- bash (for automation scripts)
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**Optional (auto-installed when needed):**
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- Skill_Seekers (for documentation scraping)
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- Will prompt for installation on first documentation-based request
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- One-command setup: `scripts/install-skill-seekers.sh`
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## Configuration
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**Environment variables (optional):**
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```bash
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# Skill_Seekers installation path
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export SKILL_SEEKERS_PATH="$HOME/Skill_Seekers"
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# Output directory for created skills
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export SKILL_OUTPUT_DIR="$HOME/.claude/skills"
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# Minimum quality score (default: 8.0)
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export MIN_QUALITY_SCORE="8.0"
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# Auto-install dependencies (default: prompt)
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export AUTO_INSTALL_DEPS="true"
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```
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## Examples
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**Example 1: Documentation Skill**
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```
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User: "Create a React skill from react.dev"
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Agent:
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🔍 Analyzing... → Documentation-based
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🔄 Scraping react.dev... → 3 min
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📊 Quality: 7.8 → 8.6 ✅
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🧪 Testing: 12/12 passing ✅
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✅ react-development skill ready (8.6/10)
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```
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**Example 2: Custom Workflow Skill**
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```
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User: "Create a skill for debugging Solana transaction failures"
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Agent:
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🔍 Analyzing... → Custom workflow (no docs to scrape)
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📝 Using TDD methodology...
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🧪 RED: Testing baseline...
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✏️ GREEN: Writing skill...
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🔄 REFACTOR: Closing loopholes...
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📊 Quality: 8.3 ✅
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✅ solana-transaction-debugging skill ready (8.3/10)
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```
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**Example 3: Hybrid Skill**
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```
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User: "Create an Anchor skill with docs plus custom debugging workflows"
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Agent:
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🔍 Analyzing... → Hybrid (docs + custom)
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📥 Scraping docs.rs/anchor-lang... → 2 min
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📝 Adding custom debugging workflows...
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🔄 Integrating and testing...
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📊 Quality: 8.9 ✅
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✅ anchor-development skill ready (8.9/10)
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```
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## Quality Guarantee
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**Every skill delivered by skill-factory:**
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- ✅ Scores >= 8.0/10 on Anthropic best practices
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- ✅ Has concrete examples (not abstract)
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- ✅ Follows structure conventions
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- ✅ Tested with auto-generated scenarios
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- ✅ Ready to use immediately
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**If quality < 8.0, I keep working until it reaches 8.0+**
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## Troubleshooting
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**Skill_Seekers installation fails:**
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```bash
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# Manual installation
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git clone https://github.com/yusufkaraaslan/Skill_Seekers ~/Skill_Seekers
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cd ~/Skill_Seekers
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pip install -r requirements.txt
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# Or use installation script
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~/Projects/claude-skills/skill-factory/skill/scripts/install-skill-seekers.sh
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```
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**Quality score stuck below 8.0:**
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- I'll report what's blocking and suggest manual review
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- Check references/anthropic-best-practices.md for criteria
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- Run manual enhancement if needed
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**Want to understand methodology:**
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- See references/obra-tdd-methodology.md (testing approach)
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- See references/anthropic-best-practices.md (quality criteria)
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- See references/skill-seekers-integration.md (automation details)
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## Reference Files
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**Anthropic Official Documentation:**
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- references/overview.md - Agent Skills architecture, progressive disclosure, and platform details
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- references/quickstart.md - Quick tutorial on using pre-built Agent Skills in the Claude API
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- references/best-practices.md - Comprehensive authoring guidelines from Anthropic
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- references/anthropic-best-practices.md - Quality scoring system (10/10 criteria)
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**Skill Factory Implementation Details:**
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- references/obra-tdd-methodology.md - Full TDD testing approach
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- references/skill-seekers-integration.md - Automation documentation
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- references/request-analysis.md - How requests are parsed
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- references/quality-loops.md - Enhancement algorithms
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## Scripts Reference
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Available helper scripts in `scripts/` directory:
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- **check-skill-seekers.sh** - Check if Skill_Seekers is installed
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- **install-skill-seekers.sh** - One-command Skill_Seekers setup
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- **quality-check.py** - Score any skill against Anthropic best practices
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Usage examples:
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```bash
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# Check Skill_Seekers installation
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./scripts/check-skill-seekers.sh
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# Install Skill_Seekers
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./scripts/install-skill-seekers.sh
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# Quality check a skill
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python3 ./scripts/quality-check.py /path/to/skill/SKILL.md
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```
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## Philosophy
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**You don't want to:**
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- Navigate decision trees
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- Choose between tools
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- Check quality manually
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- Test with subagents yourself
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- Wonder if output is good
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**You want to:**
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- Describe what you need
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- Get high-quality result
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- Start using immediately
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**That's what skill-factory delivers.**
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## Credits
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Built on top of excellent tools:
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- [Skill_Seekers](https://github.com/yusufkaraaslan/Skill_Seekers) - Documentation scraping
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- [obra/superpowers-skills](https://github.com/obra/superpowers-skills) - TDD methodology
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- [Anthropic skill-creator](https://github.com/anthropics/skills) - Best practices
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Skill-factory orchestrates these tools with automatic quality assurance and testing.
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---
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**Just tell me what skill you need. I'll handle the rest.**
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Reference in New Issue
Block a user