Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token coun

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 Install Basic Usage 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: 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): Common Use Cases Code Review Preparation Security Audit Documentation Generation Bug Investigation Implementation Planning Command Line Reference File Selection Output Options Configuration 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: 1. Always review output before sharing 2. Use .repomixignore for sensitive files 3. Enable security checks for unknown codebases 4. Avoid packaging .env files 5. Check for hardcoded credentials Disable security checks if needed: Implementation Workflow When user requests repository packaging: 1. Assess Requirements Identify target repository (local/remote) Determine output format needed Check for sensitive data concerns 2. Configure Filters Set include patterns for relevant files Add ignore patterns for unnecessary files Enable/disable comment removal 3. Execute Packaging Run repomix with appropriate options Monitor token counts Verify security checks 4. Validate Output Review generated file Confirm no sensitive data Check token limits for target LLM 5. Deliver Context Provide packaged file to user Include token count summary Note any warnings or issues Reference Documentation For detailed information, see: [Configuration Reference](./references/configuration.md) Config files, include/exclude patterns, output formats, advanced options [Usage Patterns](./references/usage patterns.md) 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