Agent skill

Engineer Expertise Extractor

by jamesrochabrun in jamesrochabrun/skills

Research and extract an engineer's coding style, patterns, and best practices from their GitHub contributions.

MITAuto-check passedDevelopment

Install Engineer Expertise Extractor

skills CLI
$ npx skills add jamesrochabrun/skills --skill engineer-expertise-extractor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jamesrochabrun/skills engineer-expertise-extractor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jamesrochabrun/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/engineer-expertise-extractor .claude/skills/engineer-expertise-extractor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
engineer-expertise-extractor
GitHub stars
216
Token cost
~3.8k tokens
SKILL.md length
898 words
Files
2 (incl. scripts)
Skills in repo
23
Repo updated
First seen
Licence
MIT

At a glance

Research and extract an engineer's coding style, patterns, and best practices from their GitHub contributions.

  • Works in 8 steps: Research Phase → Analysis Phase → Organization Phase → …
  • Tasks that involve Knowledge bases
  • SKILL.md covers What This Skill Does, Why This Matters, How It Works and Output Structure, plus 5 more sections
  • Runs Shell scripts from its folder; calls gh

What it does

Engineer Expertise Extractor is an agent skill from jamesrochabrun/skills. Research and extract an engineer's coding style, patterns, and best practices from their GitHub contributions. Creates structured knowledge base for replicating their expertise.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/extract_engineer.sh`).

It sits in Development, covering Knowledge bases. It works with GitHub. The licence is MIT.

When your agent uses it

  • Tasks that involve Knowledge bases

Example prompts

  • “/engineer-expertise-extractor”

Requirements

  • A Bash shell

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Research Phase
  2. Analysis Phase
  3. Organization Phase
  4. Onboarding New Engineers
  5. Code Review Training
  6. Knowledge Transfer
  7. Establishing Team Standards
  8. AI Agent Training

What it can do on your machine

Read from SKILL.md and the folder at commit 2482c17. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Engineer Expertise Extractor loads about 3.8k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 898 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jamesrochabrun/skills at commit 2482c17, republished under its MIT licence (© jamesrochabrun). 898 words, ~3,775 tokens.

Download SKILL.mdSave it as .claude/skills/engineer-expertise-extractor/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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:

markdown
# 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:

markdown
# 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:

markdown
# 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:

markdown
# 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:

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

Focuses analysis on specific repository contributions.

Update Existing Profile
bash
./scripts/update_profile.sh [engineer-username]

Adds new PRs and updates existing profile.

Research Sources

GitHub CLI Queries

Pull Requests:

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

Code Changes:

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

Reviews:

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

Commits:

bash
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
Show full SKILL.md (346 more words)Show less
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."

© jamesrochabrun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (scripts) in skills/engineer-expertise-extractor of jamesrochabrun/skills.

  • SKILL.md
  • scripts/extract_engineer.sh

Open the folder on GitHubat commit 2482c17

Compare with similar skills

Engineer Expertise Extractor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Engineer Expertise Extractor compared with similar skills
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Engineer Expertise Extractor this skilljamesrochabrun/skills216—~3.8kAutomated safety check: PassMIT
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GitHub Kbsundial-org/awesome-openclaw-skills663—~1.2kAutomated safety check: PassNone
Knowledge Opsaffaan-m/ECC276k2 repos~1.7kAutomated safety check: PassMIT
Copilot Spacesgithub/awesome-copilot40k1 repos~2.2kAutomated safety check: PassMIT
Update GuidelinesAzure/azure-sdk-tools134—~1.9kAutomated safety check: PassMIT

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Works with

Questions about Engineer Expertise Extractor

What does Engineer Expertise Extractor do?

Research and extract an engineer's coding style, patterns, and best practices from their GitHub contributions. Engineer Expertise Extractor is an agent skill from jamesrochabrun/skills. Research and extract an engineer's coding style, patterns, and best practices from their GitHub contributions.

When should I use Engineer Expertise Extractor?

Engineer Expertise Extractor fits situations like: tasks that involve Knowledge bases.

How do I install Engineer Expertise Extractor in Claude Code?

Run `npx skills add jamesrochabrun/skills --skill engineer-expertise-extractor -a claude-code`. Or copy the skill folder (skills/engineer-expertise-extractor in jamesrochabrun/skills) into .claude/skills/engineer-expertise-extractor in your project. Claude Code loads it when a task matches its description.

How do I install Engineer Expertise Extractor in Codex?

Run `npx skills add jamesrochabrun/skills --skill engineer-expertise-extractor -a codex`. Or copy the skill folder (skills/engineer-expertise-extractor in jamesrochabrun/skills) into .agents/skills/engineer-expertise-extractor in your project. Codex loads it when a task matches its description.

Can I use Engineer Expertise Extractor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jamesrochabrun/skills --skill engineer-expertise-extractor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/engineer-expertise-extractor, .gemini/skills/engineer-expertise-extractor, .github/skills/engineer-expertise-extractor and .opencode/skills/engineer-expertise-extractor in your project.

What does Engineer Expertise Extractor need to run?

Going by SKILL.md and its folder, Engineer Expertise Extractor needs a shell for the scripts in its folder and the command-line tools its instructions call (gh). Our summary lists: A Bash shell.

Does Engineer Expertise Extractor access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Engineer Expertise Extractor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Engineer Expertise Extractor use?

Engineer Expertise Extractor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Engineer Expertise Extractor use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Engineer Expertise Extractor?

Skills that share tags, products or a category with Engineer Expertise Extractor: GitHub Issue Summary (ascend-ai-coding/awesome-ascend-skills, 174 stars), GitHub Kb (sundial-org/awesome-openclaw-skills, 663 stars), Knowledge Ops (affaan-m/ECC, 276k stars) and Copilot Spaces (github/awesome-copilot, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Engineer Expertise Extractor?

jamesrochabrun (a GitHub user) maintains it in jamesrochabrun/skills, which has 216 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on January 14, 2026.

Source: jamesrochabrun/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.