Using CONTEXT.md Effectively

codectx compiles your codebase into a single CONTEXT.md file, but the real power comes from how you use it with AI models.

AI Coding Assistants (Cursor, Copilot, etc.)

Modern IDEs with AI features often have context limitations or struggle to autonomously locate architectural dependencies. By feeding CONTEXT.md directly into the IDE’s chat window, you ground the agent in reality.

Workflow:

1. Generate CONTEXT.md via codectx analyze .
2. Open your AI Chat and reference the file (e.g., using @CONTEXT.md).
3. Prompt: “I need to add a new REST endpoint for User Profiles. Based on @CONTEXT.md, which routers and database schemas need to be modified?”

The AI will output significantly more accurate answers because it has your dependency graph and core module structure explicitly laid out.

Automated Agentic Refactoring

When building autonomous agents to refactor code, passing the raw codebase often results in circular import errors or orphaned functions.

Workflow: Provide CONTEXT.md to your agent as its map. Instead of telling the agent to “Find where X is and fix it”, tell it: “Consult your CONTEXT.md map. Find the structural tier 1 routing modules. Add dependency injection to them.”

Codebase Exploration

When onboarding onto a new repo, CONTEXT.md acts as an automated README on steroids. Since it contains the dependency graphs and tiered file relevance, you can read it top-to-bottom to understand:

• What the most important files are (Tier 1 core modules).
• How the entry points map to background workers.
• What libraries are heavily utilized.