repomix
Repomix packages entire code repositories into single, AI-friendly files for analysis by language models. Use it when preparing codebases for Claude or other LLMs, creating repository snapshots for context windows, analyzing third-party libraries, conducting security audits, generating documentation, or investigating bugs across large codebases. The tool supports multiple output formats, respects .gitignore patterns, counts tokens for context management, and can process remote repositories without cloning.
git clone --depth 1 https://github.com/mrgoonie/claudekit-skills /tmp/repomix && cp -r /tmp/repomix/.claude/skills/repomix ~/.claude/skills/repomixSKILL.md
# 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 ```bash repomix --version ``` ### Install ```bash # npm npm install -g repomix # Homebrew (macOS/Linux) brew install repomix ``` ### Basic Usage ```bash # 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: ```bash # 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): ```bash repomix --remove-comments ``` ## Common Use Cases ### Code Review Preparation ```bash # Package feature branch for AI review repomix --include "src/**/*.ts" --remove-comments -o review.md --style markdown ``` ### Security Audit ```bash # Package third-party library npx repomix --remote vendor/library --style xml -o audit.xml ``` ### Documentation Generation ```bash # Package with docs and code repomix --include "src/**,docs/**,*.md" --style markdown -o context.md ``` ### Bug Investigation ```bash # Package specific modules repomix --include "src/auth/**,src/api/**" -o debug-context.xml ``` ### Implementation Planning ```bash # Full codebase context repomix --remove-comments --copy ``` ## Command Line Reference ### File Selection ```bash # Include specific patterns repomix --include "src/**/*.ts,*.md" # Ignore additional patterns repomix -i "tests/**,*.test.js" # Disable .gitignore rules repomix --no-gitignore ``` ### Output Options ```bash # 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 ```bash # 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: 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: ```bash repomix --no-security-check ``` ## 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
Manage MCP (Model Context Protocol) server integrations - discover tools/prompts/resources, analyze relevance for tasks, and execute MCP capabilities. Use when need to work with MCP servers, discover available MCP tools, filter MCP capabilities for specific tasks, execute MCP tools programmatically, or implement MCP client functionality. Keeps main context clean by handling MCP discovery in subagent context.
Stage all files and create a commit.
Stage, commit and push all code in the current branch
Create a pull request
Create a new agent skill
Utilize tools of Model Context Protocol (MCP) servers
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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.