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Repomix

  • 19 installs
  • 95 repo stars
  • Updated July 7, 2026
  • binjuhor/shadcn-lar

Repomix (skill) is a Claude skill that wraps the Repomix CLI to pack an entire repository into a single AI-friendly file for LLM consumption.

About

This skill wraps the Repomix CLI to package an entire repository into a single AI-friendly file for feeding to LLMs. It covers include/exclude filters, output formats, comment removal, remote-repo packing, token counting, and Secretlint-based security checks. A developer uses it to prepare codebase context for LLM analysis, review, or security audits.

  • Packs a whole repo into one AI-friendly XML/Markdown/JSON/plain file
  • Processes remote repos without cloning via --remote owner/repo
  • Counts tokens per file and per repo, with a --token-count-tree view

Repomix by the numbers

  • 19 all-time installs (skills.sh)
  • Ranked #10,541 of 16,556 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 2, 2026 (Skillselion catalog sync)
At a glance

repomix capabilities & compatibility

Free open-source CLI; no API keys required.

Capabilities
research · code review
Works with
github
Use cases
token optimization · code review · security audit · documentation
Pricing
Free
From the docs

What repomix says it does

Repomix packs entire repositories into single, AI-friendly files. Perfect for feeding codebases to LLMs like Claude, ChatGPT, and Gemini.
SKILL.md
Repomix uses Secretlint to detect sensitive data (API keys, passwords, credentials, private keys, AWS secrets).
SKILL.md
npx skills add https://github.com/binjuhor/shadcn-lar --skill repomix

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Listed on Skillselion
Installs19
repo stars95
Last updatedJuly 7, 2026
Repositorybinjuhor/shadcn-lar

What it does

Package a codebase into one AI-friendly file with token counts so an LLM can analyze, review, or audit it.

When should I use this skill?

You need to feed a whole codebase to an LLM for analysis, review, security audit, or documentation context.

What you get

A single AI-friendly file (XML/Markdown/JSON/plain) with token counts, ready to paste into an LLM.

  • Single packed repo file (repomix-output.xml or chosen format)
  • Per-file and per-repo token counts

By the numbers

  • 4 output formats (XML, Markdown, JSON, plain)
  • 5-step implementation workflow

Files

SKILL.mdMarkdownGitHub ↗

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

Token Count Optimization

Understanding your codebase's token distribution is crucial for optimizing AI interactions. Use the --token-count-tree option to visualize token usage across your project:

repomix --token-count-tree

This displays a hierarchical view of your codebase with token counts:

🔢 Token Count Tree:
────────────────────
└── src/ (70,925 tokens)
    ├── cli/ (12,714 tokens)
    │   ├── actions/ (7,546 tokens)
    │   └── reporters/ (990 tokens)
    └── core/ (41,600 tokens)
        ├── file/ (10,098 tokens)
        └── output/ (5,808 tokens)

You can also set a minimum token threshold to focus on larger files:

repomix --token-count-tree 1000  # Only show files/directories with 1000+ tokens

This helps you:

  • Identify token-heavy files that might exceed AI context limits
  • Optimize file selection using --include and --ignore patterns
  • Plan compression strategies by targeting the largest contributors
  • Balance content vs. context when preparing code for AI analysis

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:

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 - 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

Related skills

FAQ

Can it process a remote GitHub repo without cloning?

Yes, use npx repomix --remote owner/repo or a full GitHub URL.

What output formats are supported?

XML, Markdown, JSON, and plain text via the --style option.

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