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Give AI assistants deep local codebase context, search, and stack insights.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "codeweave" yet — see the docs or source repo.
Using the current codebase index, summarize the project structure, core modules, main dependencies, and tech stack, then identify the first 5 files a new contributor should read.
A high-level project overview, key module explanations, dependency and stack list, plus a prioritized file-reading list.
Find the code related to user authentication and authorization, list the relevant files, functions, and call relationships, and highlight recent important changes from git history.
Locations of auth-related code, a view of key function relationships, and a summary of recent changes.
I’m planning to refactor the payment module. Analyze its upstream and downstream dependencies, potentially affected files, possible duplicate code, and the areas that need focused regression testing.
An impact analysis for the refactor, dependency notes, duplicate-code clues, and a prioritized testing checklist.
Search and navigate multiple code repositories with natural language understanding.
Retrieve GitHub code at symbol level to explore repos with far lower AI token costs.
Build a local code intelligence layer for AI-driven code exploration.
Search repositories semantically and turn codebases into AI-ready context and knowledge.
Gives AI assistants indexed search and analysis for multi-language codebases.
Navigate, search, and analyze TypeScript codebases with semantic intelligence tools.