context-hub
A command-line tool that supplies AI coding agents with curated, version-tracked documentation so they write real, working code instead of inventing interfaces that don't exist.
Context Hub is a tool aimed at AI coding assistants, the kind that write code on your behalf inside a terminal. The README starts with the problem it tries to solve: these assistants often make up programming interfaces that do not exist, and they forget what they figured out in an earlier session. Context Hub gives them a supply of curated, version-tracked documentation to read so they are more likely to write code that actually works.
An important point the README stresses is that the tool is meant for the AI agent to use, not the person. You install a command-line program called chub, then tell your assistant to run it. The basic loop is simple: the agent searches for a topic, fetches the relevant document in the right programming language, reads it, and writes its code. The documents are kept as plain markdown files in this same repository, so you can see exactly what your agent is reading and suggest changes.
Beyond fetching docs, Context Hub is built around the idea of agents that improve over time. When an assistant hits a gap or a quirk, it can attach a local note, called an annotation, to a document. That note comes back automatically the next time the document is fetched, so the lesson is not lost. Separately, agents can send up or down feedback to the people who write the docs, which is meant to help the documentation get better for everyone.
The README lists the available commands for searching, fetching, annotating, and giving feedback, and mentions an incremental fetch feature that lets an agent pull only the specific files it needs instead of everything at once. Anyone can contribute new documentation through pull requests using a defined markdown format. The project is published on the npm package registry, requires a recent version of Node.js, and is released under the MIT license. It is associated with Andrew Ng, a well-known figure in the AI field.
Where it fits
- Tell your AI coding assistant to run chub search before writing any library code so it reads real docs first.
- Let your AI agent attach a local annotation to a doc when it finds a quirk, so the lesson is saved for next time.
- Contribute a new documentation page for a missing library so all agents using context-hub benefit.
- Use the incremental fetch command to pull only the specific doc files your agent needs instead of everything at once.