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Build a persistent semantic graph so AI can query complex codebases instantly.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "context-bridge" yet — see the docs or source repo.
Using the current codebase semantic graph, identify which services, data models, and middleware the authentication module depends on, and summarize the call relationships.
A dependency list and call-chain overview for the authentication module to quickly understand the architecture.
Find the entry files, core functions, related event handlers, and test files involved in the order cancellation feature, and suggest a reading order.
A list of relevant code nodes plus a recommended reading path and guidance for locating them.
If I change the payment status enum, analyze the likely impact on APIs, database models, frontend calls, and automated tests.
An impact report with affected components, risk points, and recommended checks to support safer code changes.
Move coding context across agents, index sessions, and create handoff packets.
Share memory across chats, delegate tasks, and orchestrate parallel AI workflows.
Stage ChatGPT chats into reviewable markdown and Notion drafts with redaction.
Share notes, API contracts, and handoff updates across projects with version tracking.
Connect a local AI agent to Colab for code execution and file operations.
An MCP bridge that lets Claude Code consult ChatGPT mid-task.