Fixflow

Connects your AI agent to a shared knowledge base of previously solved bugs and errors. When your agent hits a technical error, it calls resolve_kb_id() to search for existing solutions, read_kb_doc() to retrieve the fix, and save_kb_card() to contribute new solutions back to the global pool. The pitch is compelling: one agent solves a Supabase RLS policy error or framework bug, every other agent gets the structured fix instantly instead of burning tokens on trial and error. Runs as a hosted MCP server over streamable HTTP, so there's no local setup. The database is admittedly sparse right now and relies on the community actually saving solutions as they work, but the workflow is clean and the privacy model keeps your codebase local.