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: 'test@example.com', 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.

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