Parse Python, Rust, and C++ codebases into SQLite-indexed semantic maps.
npx skills add https://github.com/ChipFlow/context-daddy --skill context-daddy
Please help me install this Agent Skill. Skill: context-daddy Source: https://github.com/ChipFlow/context-daddy/tree/main Command: npx skills add https://github.com/ChipFlow/context-daddy --skill context-daddy
This skill solves the problem of AI assistants struggling to comprehend large codebases due to inefficient exploration, context window limitations, and loss of "tribal knowledge" across sessions. It provides a structured, fast way for Claude to understand code.
search_symbols, list_files), significantly faster and more accurate than traditional grep.AuthService class works, use get_symbol_content("AuthService") to instantly retrieve its full source code and docstrings without manually searching files.Use the context-daddy skill to list all Python files in your project.
High-intent search queries and answers about installing and using this skill.
Semantic code exploration allows AI assistants to understand large codebases by parsing source code with tree-sitter and indexing symbols in SQLite. This enables fast retrieval of functions and classes, preventing context window exhaustion during software development.
Yes, tree-sitter parsing builds a semantic map for Python, Rust, and C++ repositories, enabling fast symbol search. Using MCP tools like search_symbols, you can accurately locate functions and classes by name or pattern without relying on traditional grep.
Persistent context management captures project narratives and learnings that survive context compaction. By storing tribal knowledge structurally, Claude retains critical architectural insights and evolving documentation across multiple coding sessions.
Using semantic symbol search via MCP tools is the best way to retrieve class source code. Calling get_symbol_content with the class name instantly extracts full source code and docstrings from the SQLite index, bypassing manual file searching.
Yes, the Model Context Protocol server integrates with Anthropic Claude to deliver fast symbol search and narrative documentation. It exposes MCP tools like list_files and search_symbols to facilitate efficient codebase navigation and architectural understanding.
AI assistants lose architectural insights during long sessions due to context window limitations and compaction. Implementing a persistent context management system that survives compaction ensures critical project narratives and learnings are retained.
DevelataConvert Rust and Python source code into Universal Logic Graph files.
butttonsQuery a SQLite-indexed codebase for symbol references and dependency analysis.
Moliboy5000Configure MCP servers like Context7, Exa, Filesystem, and GitHub via CLI.
ollieb89Search code symbols and retrieve implementations using tree-sitter AST parsing.