Analyze repository architecture, commands, and conventions to generate a structured AGENTS.md.
npx skills add https://github.com/iimuz/dotfiles --skill create-agents
Please help me install this Agent Skill. Skill: create-agents Source: https://github.com/iimuz/dotfiles/tree/main/.config/copilot/skills/create-agents Command: npx skills add https://github.com/iimuz/dotfiles --skill create-agents
This skill provides a concise, maintainable AGENTS.md to document repository architecture, commands, and conventions for AI agents and new contributors.
Analyze the repository and generate an AGENTS.md describing its architecture, commands, and conventions.
High-intent search queries and answers about installing and using this skill.
To generate an AGENTS.md, analyze the repository's architecture, commands, and conventions. The skill produces a structured markdown file containing a project header, architecture overview, core commands, conventions, and deployment notes for AI agents to consume during project review.
An AGENTS.md file should include a project header, architecture overview, core commands, conventions, and important notes for deployment and security. This provides AI agents with the repository context needed to understand and review the codebase effectively.
You need to update the AGENTS.md when repository changes occur or when explicitly requested. Updating the documentation ensures AI agents and new contributors maintain accurate context regarding the repository's architecture, commands, and conventions.
Yes, you can align AGENTS.md with existing reference templates to ensure consistency across projects. The skill scopes the documentation generation to match these templates, maintaining a uniform structure for repository context and conventions.
No external dependencies are required to generate repository context for AI agents. The skill independently analyzes the repository and produces a concise, maintainable AGENTS.md file without needing additional packages or external tool integrations.
A structured AGENTS.md provides concise, maintainable repository context specifically scoped for AI agents, whereas standard README documentation targets human contributors. AGENTS.md focuses on architecture, commands, and conventions to optimize AI consumption during project review.
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