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repomixrepomix 工具

Agent Skill

repomix 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

964

周安装

41

GitHub Stars

6

下载量

338
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:repomix(repomix 工具)
来源仓库:https://github.com/duc01226/easyplatform
仓库路径:skills/repomix
安装命令:
npx skills add https://github.com/duc01226/easyplatform --skill repomix
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/duc01226/easyplatform --skill repomix

简介

repomix 打包仓库代码为单个 AI 友好文件用于大模型分析。

  • 支持 include/exclude 模式控制内容范围与敏感信息过滤。
  • 输出前自动检测令牌数量与隐私数据泄露风险。
  • 适用于知识蒸馏与跨项目代码理解任务。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。
  • repomix 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ATTENTION ask user whether to skip.

Quick Summary

Goal: Package code repositories into single AI-friendly files using Repomix for LLM analysis.

Workflow:

  1. Assess — Identify target repo (local/remote), output format, sensitivity concerns
  2. Configure — Set include/ignore patterns, comment removal, output style
  3. Execute — Run repomix with options, monitor token counts
  4. Validate — Review output for sensitive data, check token limits for target LLM

Key Rules:

  • Always review output before sharing (security check for API keys, credentials)
  • Use --include patterns to stay within LLM context limits
  • Default output is XML; use --style markdown for readable output
  • Use --token-count-tree to identify token-heavy files before packaging

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

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

  1. Configure Filters

- Set include patterns for relevant files - Add ignore patterns for unnecessary files - Enable/disable comment removal

  1. Execute Packaging

- Run repomix with appropriate options - Monitor token counts - Verify security checks

  1. Validate Output

- Review generated file - Confirm no sensitive data - Check token limits for target LLM

  1. 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


Closing Reminders

  • IMPORTANT MUST ATTENTION break work into small todo tasks using TaskCreate BEFORE starting
  • IMPORTANT MUST ATTENTION search codebase for 3+ similar patterns before creating new code
  • IMPORTANT MUST ATTENTION cite file:line evidence for every claim (confidence >80% to act)
  • IMPORTANT MUST ATTENTION add a final review todo task to verify work quality

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

28.5%
按下载量换算96

windsurf

24.73%
按下载量换算84

OpenCode

15.59%
按下载量换算53

Codex

12.63%
按下载量换算43

Antigravity

7.05%
按下载量换算24

Gemini CLI

3.79%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/duc01226/easyplatform --skill repomix;npx skills add duc01226/easyplatform --skill "repomix" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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