codebase-context
Map your team's conventions before your AI agent starts searching.
You're tired of AI agents writing code that "just works" but still misses how your team actually builds things. They search too broadly, pick generic examples, and spend tokens exploring before they understand the shape of the repo.
codebase-context changes the first step. Start with a bounded conventions map that shows the architecture, dominant patterns, and strongest local examples. Then search for the exact file, symbol, or workflow you need.
Here's what codebase-context does:
Starts with a bounded conventions map - The first call shows architecture layers, active patterns, golden files, and next calls without dumping vendored repos, fixtures, generated output, or oversized entrypoint lists into the default surface.
Finds the right local example - Search does not just return code. Each result comes back with pattern signals, file relationships, and quality indicators so the agent can move from the map to the most relevant local example instead of wandering through raw hits.
