Package code repositories into XML, Markdown, and JSON for LLM context.
npx skills add https://github.com/IoT-VN/tuya-energy-dashboard --skill repomix-iot-vn
Please help me install this Agent Skill. Skill: repomix Source: https://github.com/IoT-VN/tuya-energy-dashboard/tree/main/.opencode/skills/repomix Command: npx skills add https://github.com/IoT-VN/tuya-energy-dashboard --skill repomix-iot-vn
This Skill streamlines the process of preparing entire code repositories or specific modules for analysis by Large Language Models (LLMs), ensuring efficient context preparation and code understanding.
Use repomix to package the current directory into a markdown file named 'codebase.md'.
High-intent search queries and answers about installing and using this skill.
To package a codebase for AI analysis, convert repository files into AI-friendly formats like XML, Markdown, or JSON. This process manages token counts for LLM context windows and respects .gitignore rules during extraction.
The best way to prepare a local repository for LLM context preparation is by packaging source files into a single document. This method strips comments and converts codebases into formats optimized for AI model ingestion.
Yes, you can run security audits on code before sharing it with AI models. Repository packaging tools identify sensitive data within your codebase, ensuring safe handling during LLM context preparation.
Repository packaging works with both local and remote repositories. It automatically respects .gitignore configurations to exclude unnecessary files, ensuring only relevant source code is packaged for AI analysis.
Manage token counts for LLM context windows by packaging the codebase into consolidated formats like XML or Markdown. This approach strips comments and isolates relevant source files to optimize AI ingestion.
You can convert source files into AI-friendly outputs including XML, Markdown, JSON, and plain text. These formats facilitate efficient codebase ingestion and analysis by Large Language Models.
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