repomix▌
mrgoonie/claudekit-skills · updated Apr 8, 2026
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Repomix packs entire repositories into single, AI-friendly files. Perfect for feeding codebases to LLMs like Claude, ChatGPT, and Gemini.
Repomix Skill
Repomix packs entire repositories into single, AI-friendly files. Perfect for feeding codebases to LLMs like Claude, ChatGPT, and Gemini.
When to Use
Use when:
- Packaging codebases for AI analysis
- Creating repository snapshots for LLM context
- Analyzing third-party libraries
- Preparing for security audits
- Generating documentation context
- Investigating bugs across large codebases
- Creating AI-friendly code representations
Quick Start
Check Installation
repomix --version
Install
# npm
npm install -g repomix
# Homebrew (macOS/Linux)
brew install repomix
Basic Usage
# Package current directory (generates repomix-output.xml)
repomix
# Specify output format
repomix --style markdown
repomix --style json
# Package remote repository
npx repomix --remote owner/repo
# Custom output with filters
repomix --include "src/**/*.ts" --remove-comments -o output.md
Core Capabilities
Repository Packaging
- AI-optimized formatting with clear separators
- Multiple output formats: XML, Markdown, JSON, Plain text
- Git-aware processing (respects .gitignore)
- Token counting for LLM context management
- Security checks for sensitive information
Remote Repository Support
Process remote repositories without cloning:
# Shorthand
npx repomix --remote yamadashy/repomix
# Full URL
npx repomix --remote https://github.com/owner/repo
# Specific commit
npx repomix --remote https://github.com/owner/repo/commit/hash
Comment Removal
Strip comments from supported languages (HTML, CSS, JavaScript, TypeScript, Vue, Svelte, Python, PHP, Ruby, C, C#, Java, Go, Rust, Swift, Kotlin, Dart, Shell, YAML):
repomix --remove-comments
Common Use Cases
Code Review Preparation
# Package feature branch for AI review
repomix --include "src/**/*.ts" --remove-comments -o review.md --style markdown
Security Audit
# Package third-party library
npx repomix --remote vendor/library --style xml -o audit.xml
Documentation Generation
# Package with docs and code
repomix --include "src/**,docs/**,*.md" --style markdown -o context.md
Bug Investigation
# Package specific modules
repomix --include "src/auth/**,src/api/**" -o debug-context.xml
Implementation Planning
# Full codebase context
repomix --remove-comments --copy
Command Line Reference
File Selection
# Include specific patterns
repomix --include "src/**/*.ts,*.md"
# Ignore additional patterns
repomix -i "tests/**,*.test.js"
# Disable .gitignore rules
repomix --no-gitignore
Output Options
# Output format
repomix --style markdown # or xml, json, plain
# Output file path
repomix -o output.md
# Remove comments
repomix --remove-comments
# Copy to clipboard
repomix --copy
Configuration
# Use custom config file
repomix -c custom-config.json
# Initialize new config
repomix --init # creates repomix.config.json
Token Management
Repomix automatically counts tokens for individual files, total repository, and per-format output.
Typical LLM context limits:
- Claude Sonnet 4.5: ~200K tokens
- GPT-4: ~128K tokens
- GPT-3.5: ~16K tokens
Security Considerations
Repomix uses Secretlint to detect sensitive data (API keys, passwords, credentials, private keys, AWS secrets).
Best practices:
- Always review output before sharing
- Use
.repomixignorefor sensitive files - Enable security checks for unknown codebases
- Avoid packaging
.envfiles - Check for hardcoded credentials
Disable security checks if needed:
repomix --no-security-check
Implementation Workflow
When user requests repository packaging:
-
Assess Requirements
- Identify target repository (local/remote)
- Determine output format needed
- Check for sensitive data concerns
-
Configure Filters
- Set include patterns for relevant files
- Add ignore patterns for unnecessary files
- Enable/disable comment removal
-
Execute Packaging
- Run repomix with appropriate options
- Monitor token counts
- Verify security checks
-
Validate Output
- Review generated file
- Confirm no sensitive data
- Check token limits for target LLM
-
Deliver Context
- Provide packaged file to user
- Include token count summary
- Note any warnings or issues
Reference Documentation
For detailed information, see:
- Configuration Reference - Config files, include/exclude patterns, output formats, advanced options
- Usage Patterns - AI analysis workflows, security audit preparation, documentation generation, library evaluation
Additional Resources
- GitHub: https://github.com/yamadashy/repomix
- Documentation: https://repomix.com/guide/
- MCP Server: Available for AI assistant integration
How to use repomix on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add repomix
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches repomix from GitHub repository mrgoonie/claudekit-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate repomix. Access the skill through slash commands (e.g., /repomix) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★74 reviews- ★★★★★Henry Mehta· Dec 28, 2024
repomix is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Henry Rao· Dec 20, 2024
Keeps context tight: repomix is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Layla Kapoor· Dec 20, 2024
Registry listing for repomix matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Dec 16, 2024
Keeps context tight: repomix is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Isabella Robinson· Dec 8, 2024
repomix is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Isabella White· Nov 27, 2024
repomix reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Lucas Smith· Nov 23, 2024
repomix fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Arjun Sanchez· Nov 23, 2024
We added repomix from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Naina Singh· Nov 19, 2024
repomix reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Yuki Kapoor· Nov 11, 2024
Registry listing for repomix matched our evaluation — installs cleanly and behaves as described in the markdown.
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