SKILL.md
Full skill instructions
name description
tech-research-skill-builder
Research latest library documentation, industry best practices, and technical knowledge to automatically generate project-level skills. Use when asked to: (1) Research and create a skill for a library/framework, (2) Build a skill based on architectural patterns, (3) Generate skills from technical research, (4) Create domain-specific technical skills from web research, or (5) Any request combining research with skill creation.
Tech Research Skill Builder
Automatically research technical topics and generate comprehensive project-level skills with the latest documentation and best practices.
Overview
This skill enables automated creation of project-level skills through web research. It:
Conducts comprehensive web research on specified technical topics
Gathers library documentation, best practices, and code examples
Structures findings into an organized skill format
Generates a complete, ready-to-use skill package
Workflow
Step 1: Parse Request and Plan Research
When a user requests skill creation, identify:
Topic : The library, framework, or technical domain to research
Scope : What aspects to cover (API docs, patterns, best practices)
Output location : Where to create the skill (default: .claude/skills )
Step 2: Execute Comprehensive Research
Conduct research across four categories:
- Library Documentation
Search for:
Official documentation (latest version)
API references and method signatures
Getting started guides
Migration guides
Example searches:
[topic] official documentation 2025
[topic] API reference latest
[topic] getting started guide
- Best Practices
Search for:
Industry standards and conventions
Production deployment guidelines
Security best practices
Performance optimization
Example searches:
[topic] best practices 2025
[topic] production deployment
[topic] industry standards
- Code Examples
Search for:
Real-world usage patterns
Common implementations
Integration examples
Sample projects
Example searches:
[topic] code examples
[topic] common patterns
[topic] example project github
- Architectural Patterns
Search for:
Design patterns
Architecture decisions
Scalability patterns
Implementation strategies
Example searches:
[topic] architecture patterns
[topic] design patterns
[topic] implementation strategies
For detailed research strategies , see research-workflow.md .
Step 3: Structure Research Data
Organize findings into this format:
{ "topic" : " Topic name " , "metadata" : { "name" : " topic-name " , "description" : " Comprehensive description with triggers " }, "library_docs" : [ { "title" : " Doc title " , "summary" : " Overview " , "url" : " Source URL " , "key_points" : [ " Point 1 " , " Point 2 " ], "content" : " Detailed content " } ], "best_practices" : [ { "category" : " Category name " , "description" : " Practice description " , "guidelines" : [ " Guideline 1 " , " Guideline 2 " ], "source" : " Source URL " } ], "code_examples" : [ { "title" : " Example title " , "description" : " What it demonstrates " , "code" : " Code snippet " , "language" : " python " , "source" : " Source URL " } ], "architectural_patterns" : [ { "name" : " Pattern name " , "description" : " Pattern overview " , "use_cases" : [ " Use case 1 " , " Use case 2 " ], "trade_offs" : " Pros and cons " , "source" : " Source URL " } ] }
Save this structured data to a temporary JSON file for skill generation.
Step 4: Generate Skill Package
Use the generate_skill.py script to create the skill:
python .claude/skills/tech-research-skill-builder/scripts/generate_skill.py
/tmp/research_data.json
.claude/skills
This generates:
SKILL.md : Core skill file with frontmatter and navigation
references/core-concepts.md : Fundamental concepts and terminology
references/patterns.md : Implementation patterns and code examples
references/best-practices.md : Production guidelines and recommendations
references/api-reference.md : Detailed API documentation
For skill generation guidelines , see skill-generation-guide.md .
Step 5: Validate and Package
After generation:
Validate the skill structure :
python /root/.claude/skills/skill-creator/scripts/quick_validate.py
.claude/skills/[generated-skill-name]
Package the skill (if validation passes):
python /root/.claude/skills/skill-creator/scripts/package_skill.py
.claude/skills/[generated-skill-name]
Report to user : Provide the skill location and .skill file path
Example Usage
Example 1: Library-Specific Skill
User request:
"Research FastAPI and create a skill for it"
Workflow:
Parse: Topic = "FastAPI", Scope = comprehensive
Research:
FastAPI official docs (latest version)
Best practices for production deployment
Common patterns (authentication, database integration)
Architecture examples
Structure: Organize into JSON format
Generate: Create skill at .claude/skills/fastapi
Validate and package: Create fastapi.skill file
Example 2: Architectural Pattern Skill
User request:
"Create a skill for microservices architecture patterns"
Workflow:
Parse: Topic = "microservices architecture", Scope = patterns
Research:
Microservices design patterns
Best practices for service communication
Code examples (API gateways, service mesh)
Architecture decisions (monolith vs microservices)
Structure: Organize findings
Generate: Create skill at .claude/skills/microservices-architecture
Validate and package
Example 3: Domain-Specific Technical Skill
User request:
"Research authentication best practices and build a skill"
Workflow:
Parse: Topic = "authentication", Scope = best practices
Research:
Authentication patterns (OAuth, JWT, sessions)
Security best practices
Implementation examples
Industry standards
Structure: Organize by authentication type
Generate: Create skill at .claude/skills/authentication
Validate and package
Quality Criteria
Generated skills should meet these standards:
Current information : From 2025 or latest version
Comprehensive coverage : All major aspects of the topic
Practical examples : Real-world code and patterns
Clear organization : Logical structure with navigation
Valid structure : Passes skill validation
Proper triggers : Description includes when to use
Research Depth Guidelines
Adjust research depth based on topic complexity:
Quick (20-30 min) : Simple libraries, basic patterns
3-5 sources per category
Focus on official docs
Basic examples
Medium (1-2 hours) : Standard frameworks, common patterns
10-15 sources per category
Include community resources
Multiple examples
Deep (3-4 hours) : Complex systems, architectural patterns
20+ sources per category
Comprehensive coverage
Edge cases and advanced topics
Troubleshooting
Research yields limited results
Broaden search terms
Include alternative names for the technology
Search for related technologies/patterns
Generated skill has gaps
Conduct targeted follow-up research
Manually add missing sections
Update research data and regenerate
Validation fails
Check SKILL.md frontmatter format
Ensure description is comprehensive
Verify all reference files are linked
Advanced Usage
Custom Research Scope
Modify the research categories in scripts/research_and_build_skill.py to focus on specific aspects:
def collect_research_requirements ( self ) -> Dict [ str , List [ str ]]: return { "security_practices" : [...], # Custom category "performance_optimization" : [...],
Add or remove categories as needed
}
Multiple Topic Skills
For skills covering multiple related topics:
Research each topic separately
Merge research data
Organize references by topic
Generate unified skill
Skill Updates
To update an existing skill with new research:
Conduct fresh research
Merge with existing content
Regenerate skill
Replace old skill with updated version