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Found 21 Skills
Create install.md files optimized for AI agent execution. Use for ANY question about install.md files or request to create/review installation documentation for autonomous agent use.
Creating and maintaining CLAUDE.md project memory files that provide non-obvious codebase context. Use when (1) creating a new CLAUDE.md for a project, (2) adding architectural patterns or design decisions to existing CLAUDE.md, (3) capturing project-specific conventions that aren't obvious from code inspection.
Optimize AGENTS.md and rules for token efficiency. Auto-invoked when user asks about improving agent instructions, compressing AGENTS.md, or making rules more effective.
This skill should be used when the user asks to "generate an AGENTS.md", "create a CLAUDE.md", "write agent instructions", "set up AGENTS.md", "make an AGENTS.md for this repo", "configure agent behavior", or mentions generating, writing, or improving an AGENTS.md or CLAUDE.md file for a project.
Automatically sync Agents.md, claude.md and gemini.md files in the project to maintain content consistency. Supports automatic monitoring and manual triggering.
Extract project-specific coding rules and domain knowledge from existing codebase, generating markdown documentation for AI agents.
Writing conventions for scannable, token-efficient skills and prompts. Use when creating or reviewing SKILL.md files, AGENTS.md files, or any markdown-based agent instruction documents.
Review the current session for errors, issues, snags, and hard-won knowledge, then update the rules/ files (or AGENTS.md if no suitable rule file exists) with actionable learnings.
Create, optimize, update, and validate AGENTS.md files with maximum token efficiency. Use when the user asks to (1) create new AGENTS.md files for any repository, (2) optimize/condense existing AGENTS.md to reduce token count, (3) update/refresh AGENTS.md to sync with codebase changes, (4) validate AGENTS.md quality and completeness, or (5) improve AGENTS.md files to be more effective for AI agents. Always generates token-efficient, condensed output focused on actionable commands and patterns while maintaining model-agnostic language.