engineer-expertise-extractor

name	engineer-expertise-extractor
description	Research and extract an engineer's coding style, patterns, and best practices from their GitHub contributions. Creates structured knowledge base for replicating their expertise.

Engineer Expertise Extractor

Extract and document an engineer's coding expertise by analyzing their GitHub contributions, creating a structured knowledge base that captures their coding style, patterns, best practices, and architectural decisions.

What This Skill Does

Researches an engineer's work to create a "digital mentor" by:

• Analyzing Pull Requests - Extract code patterns, review style, decisions
• Extracting Coding Style - Document their preferences and conventions
• Identifying Patterns - Common solutions and approaches they use
• Capturing Best Practices - Their quality standards and guidelines
• Organizing Examples - Real code samples from their work
• Documenting Decisions - Architectural choices and reasoning

Why This Matters

Knowledge Preservation:

• Capture expert knowledge before they leave
• Document tribal knowledge
• Create mentorship materials
• Onboard new engineers faster

Consistency:

• Align team coding standards
• Replicate expert approaches
• Maintain code quality
• Scale expertise across team

Learning:

• Learn from senior engineers
• Understand decision-making
• See real-world patterns
• Improve code quality

How It Works

1. Research Phase

Using GitHub CLI (gh), the skill:

• Fetches engineer's pull requests
• Analyzes code changes
• Reviews their comments and feedback
• Extracts patterns and conventions
• Identifies their expertise areas

2. Analysis Phase

Categorizes findings into:

• Coding Style - Formatting, naming, structure
• Patterns - Common solutions and approaches
• Best Practices - Quality guidelines
• Architecture - Design decisions
• Testing - Testing approaches
• Code Review - Feedback patterns
• Documentation - Doc style and practices

3. Organization Phase

Creates structured folders:

engineer_profiles/
└── [engineer_name]/
    ├── README.md (overview)
    ├── coding_style/
    │   ├── languages/
    │   ├── naming_conventions.md
    │   ├── code_structure.md
    │   └── formatting_preferences.md
    ├── patterns/
    │   ├── common_solutions.md
    │   ├── design_patterns.md
    │   └── code_examples/
    ├── best_practices/
    │   ├── code_quality.md
    │   ├── testing_approach.md
    │   ├── performance.md
    │   └── security.md
    ├── architecture/
    │   ├── design_decisions.md
    │   ├── tech_choices.md
    │   └── trade_offs.md
    ├── code_review/
    │   ├── feedback_style.md
    │   ├── common_suggestions.md
    │   └── review_examples.md
    └── examples/
        ├── by_language/
        ├── by_pattern/
        └── notable_prs/

Output Structure

Engineer Profile README

Contains:

• Engineer overview
• Areas of expertise
• Languages and technologies
• Key contributions
• Coding philosophy
• How to use this profile

Coding Style Documentation

Captures:

• Naming conventions (variables, functions, classes)
• Code structure preferences
• File organization
• Comment style
• Formatting preferences
• Language-specific idioms

Example:

# Coding Style: [Engineer Name]

## Naming Conventions

### Variables

- Use descriptive names: `userAuthentication` not `ua`
- Boolean variables: `isActive`, `hasPermission`, `canEdit`
- Collections: plural names `users`, `items`, `transactions`

### Functions

- Verb-first: `getUserById`, `validateInput`, `calculateTotal`
- Pure functions preferred
- Single responsibility

### Classes

- PascalCase: `UserService`, `PaymentProcessor`
- Interface prefix: `IUserRepository`
- Concrete implementations: `MongoUserRepository`

## Code Structure

### File Organization

- One class per file
- Related functions grouped together
- Tests alongside implementation
- Clear separation of concerns

### Function Length

- Max 20-30 lines preferred
- Extract helper functions
- Single level of abstraction

Patterns Documentation

Captures:

• Recurring solutions
• Design patterns used
• Architectural patterns
• Problem-solving approaches

Example:

# Common Patterns: [Engineer Name]

## Dependency Injection

Used consistently across services:

\`\`\`typescript // Pattern: Constructor injection class UserService { constructor( private readonly userRepo: IUserRepository, private readonly logger: ILogger ) {} } \`\`\`

**Why:** Testability, loose coupling, clear dependencies

## Error Handling

Consistent error handling approach:

\`\`\`typescript // Pattern: Custom error types + global handler class ValidationError extends Error { constructor(message: string) { super(message); this.name = 'ValidationError'; } }

// Usage if (!isValid(input)) { throw new ValidationError('Invalid input format'); } \`\`\`

**Why:** Type-safe errors, centralized handling, clear debugging

Best Practices Documentation

Captures:

• Quality standards
• Testing approaches
• Performance guidelines
• Security practices
• Documentation standards

Example:

# Best Practices: [Engineer Name]

## Testing

### Unit Test Structure

- AAA pattern (Arrange, Act, Assert)
- One assertion per test preferred
- Test names describe behavior
- Mock external dependencies

\`\`\`typescript describe('UserService', () => { describe('createUser', () => { it('should create user with valid data', async () => { // Arrange const userData = { email: '[e-mail]', name: 'Test' }; const mockRepo = createMockRepository();

      // Act
      const result = await userService.createUser(userData);

      // Assert
      expect(result.id).toBeDefined();
      expect(result.email).toBe(userData.email);
    });

}); }); \`\`\`

### Test Coverage

- Aim for 80%+ coverage
- 100% coverage for critical paths
- Integration tests for APIs
- E2E tests for user flows

## Code Review Standards

### What to Check

- [ ] Tests included and passing
- [ ] No console.logs remaining
- [ ] Error handling present
- [ ] Comments explain "why" not "what"
- [ ] No hardcoded values
- [ ] Security considerations addressed

Architecture Documentation

Captures:

• Design decisions
• Technology choices
• Trade-offs made
• System design approaches

Example:

# Architectural Decisions: [Engineer Name]

## Decision: Microservices vs Monolith

**Context:** Scaling user service **Decision:** Start monolith, extract services when needed **Reasoning:**

- Team size: 5 engineers
- Product stage: MVP
- Premature optimization risk
- Easier debugging and deployment

**Trade-offs:**

- Monolith pros: Simpler, faster development
- Monolith cons: Harder to scale later
- Decision: Optimize for current needs, refactor when hitting limits

## Decision: REST vs GraphQL

**Context:** API design for mobile app **Decision:** REST with versioning **Reasoning:**

- Team familiar with REST
- Simple use cases
- Caching easier
- Over-fetching not a problem yet

**When to reconsider:** If frontend needs complex queries

Code Review Documentation

Captures:

• Feedback patterns
• Review approach
• Common suggestions
• Communication style

Example:

# Code Review Style: [Engineer Name]

## Review Approach

### Priority Order

1. Security vulnerabilities
2. Logic errors
3. Test coverage
4. Code structure
5. Naming and style

### Feedback Style

- Specific and constructive
- Explains "why" behind suggestions
- Provides examples
- Asks questions to understand reasoning

### Common Suggestions

**Security:**

- "Consider input validation here"
- "This query is vulnerable to SQL injection"
- "Should we rate-limit this endpoint?"

**Performance:**

- "This N+1 query could be optimized with a join"
- "Consider caching this expensive operation"
- "Memoize this pure function"

**Testing:**

- "Can we add a test for the error case?"
- "What happens if the API returns null?"
- "Let's test the boundary conditions"

**Code Quality:**

- "Can we extract this into a helper function?"
- "This function is doing too many things"
- "Consider a more descriptive variable name"

Using This Skill

Extract Engineer Profile

./scripts/extract_engineer.sh [github-username]

Interactive workflow:

1. Enter GitHub username
2. Select repository scope (all/specific org)
3. Choose analysis depth (last N PRs)
4. Specify focus areas (languages, topics)
5. Extract and organize findings

Output: Structured profile in engineer_profiles/[username]/

Analyze Specific Repository

./scripts/analyze_repo.sh [repo-url] [engineer-username]

Focuses analysis on specific repository contributions.

Update Existing Profile

./scripts/update_profile.sh [engineer-username]

Adds new PRs and updates existing profile.

Research Sources

GitHub CLI Queries

Pull Requests:

gh pr list --author [username] --limit 100 --state all
gh pr view [pr-number] --json title,body,files,reviews,comments

Code Changes:

gh pr diff [pr-number]
gh api repos/{owner}/{repo}/pulls/{pr}/files

Reviews:

gh pr view [pr-number] --comments
gh api repos/{owner}/{repo}/pulls/{pr}/reviews

Commits:

gh api search/commits --author [username]

Analysis Techniques

Pattern Recognition:

• Identify recurring code structures
• Extract common solutions
• Detect naming patterns
• Find architectural choices

Style Extraction:

• Analyze formatting consistency
• Extract naming conventions
• Identify comment patterns
• Detect structural preferences

Best Practice Identification:

• Look for testing patterns
• Find error handling approaches
• Identify security practices
• Extract performance optimizations

Use Cases

1. Onboarding New Engineers

Problem: New engineer needs to learn team standards Solution: Provide senior engineer's profile as reference

Benefits:

• Real examples from codebase
• Understand team conventions
• See decision-making process
• Learn best practices

2. Code Review Training

Problem: Teaching good code review skills Solution: Study experienced reviewer's feedback patterns

Benefits:

• Learn what to look for
• Understand feedback style
• See common issues
• Improve review quality

3. Knowledge Transfer

Problem: Senior engineer leaving, knowledge lost Solution: Extract their expertise before departure

Benefits:

• Preserve tribal knowledge
• Document decisions
• Maintain code quality
• Reduce bus factor

4. Establishing Team Standards

Problem: Inconsistent coding styles across team Solution: Extract patterns from best engineers, create standards

Benefits:

• Evidence-based standards
• Real-world examples
• Buy-in from team
• Consistent codebase

5. AI Agent Training

Problem: Agent needs to code like specific engineer Solution: Provide extracted profile to agent

Benefits:

• Match expert's style
• Follow their patterns
• Apply their best practices
• Maintain consistency

Profile Usage by Agents

When an agent has access to an engineer profile, it can:

Code Generation:

• Follow extracted naming conventions
• Use identified patterns
• Apply documented best practices
• Match architectural style

Code Review:

• Provide feedback in engineer's style
• Check for common issues they'd catch
• Apply their quality standards
• Match their priorities

Problem Solving:

• Use their common solutions
• Follow their architectural approach
• Apply their design patterns
• Consider their trade-offs

Example Agent Prompt:

"Using the profile at engineer_profiles/senior_dev/, write a user service
following their coding style, patterns, and best practices. Pay special
attention to their error handling approach and testing standards."

Best Practices

Research Ethics

DO:

• ✅ Get permission before extracting
• ✅ Focus on public contributions
• ✅ Respect privacy
• ✅ Use for learning and improvement

DON'T:

• ❌ Extract without permission
• ❌ Share profiles externally
• ❌ Include sensitive information
• ❌ Use for performance reviews

Profile Maintenance

Regular Updates:

• Refresh every quarter
• Add new significant PRs
• Update with latest patterns
• Archive outdated practices

Quality Control:

• Verify extracted patterns
• Review examples for relevance
• Update documentation
• Remove deprecated practices

Effective Usage

For Learning:

• Study patterns with context
• Understand reasoning behind choices
• Practice applying techniques
• Ask questions when unclear

For Replication:

• Start with style guide
• Reference patterns for similar problems
• Adapt to current context
• Don't blindly copy

Limitations

What This Extracts:

• ✅ Coding style and conventions
• ✅ Common patterns and approaches
• ✅ Best practices and guidelines
• ✅ Architectural decisions
• ✅ Review feedback patterns

What This Doesn't Capture:

• ❌ Real-time problem-solving process
• ❌ Verbal communication style
• ❌ Meeting discussions
• ❌ Design phase thinking
• ❌ Interpersonal mentoring

Future Enhancements

Potential additions:

• Slack message analysis (communication style)
• Design doc extraction (design thinking)
• Meeting notes analysis (decision process)
• Video analysis (pair programming sessions)
• Code metrics tracking (evolution over time)

Example Output

engineer_profiles/
└── senior_dev/
    ├── README.md
    │   # Senior Dev - Staff Engineer
    │   Expertise: TypeScript, Node.js, System Design
    │   Focus: API design, performance optimization
    │
    ├── coding_style/
    │   ├── typescript_style.md
    │   ├── naming_conventions.md
    │   └── code_structure.md
    │
    ├── patterns/
    │   ├── dependency_injection.md
    │   ├── error_handling.md
    │   └── examples/
    │       ├── service_pattern.ts
    │       └── repository_pattern.ts
    │
    ├── best_practices/
    │   ├── testing_strategy.md
    │   ├── code_quality.md
    │   └── performance.md
    │
    ├── architecture/
    │   ├── api_design.md
    │   ├── database_design.md
    │   └── scaling_approach.md
    │
    ├── code_review/
    │   ├── feedback_examples.md
    │   └── review_checklist.md
    │
    └── examples/
        └── notable_prs/
            ├── pr_1234_auth_refactor.md
            └── pr_5678_performance_fix.md

Summary

This skill transforms an engineer's GitHub contributions into a structured, reusable knowledge base. It captures their expertise in a format that:

• Humans can learn from - Clear documentation with examples
• Agents can replicate - Structured patterns and guidelines
• Teams can adopt - Evidence-based best practices
• Organizations can preserve - Knowledge that survives turnover

The goal: Make expertise scalable, learnable, and replicable.

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"The best way to learn is from those who have already mastered it."