Code Search AI Agent Skills

Convert any source code repository into a searchable knowledge base with automatic chunking, embedding generation, and intelligent search capabilities. Now with MCP (Model Context Protocol) support for Claude Code and Cursor integration! 📦 Now available on npm! Install with: npm install -g @vezlo/src-to-kb After installation, you'll have access to these commands: - src-to-kb - Generate knowledge

Context compaction for AI code assistants – Give your AI structured, token-efficient access to 17+ languages including Elixir, Python, TypeScript, JavaScript, Rust, and more. Quick Install · Security · Developers · AI Assistants · Docs The core problem: AI code assistants waste context on blind searches. Grep dumps entire files when you only need a function signature, leaving less room for actual

This skill should be used when the user asks to "generate documentation", "document this project", "create docs", "write documentation", "update documentation", "document all APIs", "generate onboarding docs", "create developer docs", or needs comprehensive codebase documentation. Orchestrates parallel AI agents to analyze code and produce documentation files.

An MCP server for semantic code search & navigation that helps AI agents work efficiently without burning through costly tokens. Instead of reading entire files, agents can search conceptually and jump directly to the specific functions, classes, and code chunks they need. - OpenAI API Key: Required for generating embeddings (local embedding support planned) - Git: Must be a git repository (respec

Provides score interpretation (< 0.3 good, > 0.5 skip), query optimization, and source naming for query_documents, ingest_file, ingest_data tools. Use this skill when working with RAG, searching documents, ingesting files, saving web content, or handling PDF, HTML, DOCX, TXT, Markdown.