Quickstart

After installing cq into your coding agent, verify it works and add your first knowledge unit.

Verify the plugin is working

Run /cq:status in your AI coding agent's terminal session:

/cq:status

You should see:

The cq store is empty. Knowledge units are added via propose or the /cq:reflect command.

First run: Your AI coding agent will ask you to approve the MCP tool call. Select "Yes, and don't ask again" to allow it permanently.

Add your first knowledge unit

Ask your AI coding agent to propose a known pitfall from your stack:

"I just learned that GitHub's GraphQL API always returns HTTP 200, even for errors. You have to check the errors field in the response body. Verify this and propose this as a cq knowledge unit."

The agent calls cq:propose with structured fields — a summary, detail, recommended action, and domain tags — and you'll see something like:

Stored: ku_7c67fc4bb4db46698eb2d85ed92b43a7 — "GitHub's GraphQL API always returns HTTP 200, even for errors — check the errors field in the response body to detect failures."

Check your store

Run /cq:status again:

cq Knowledge Store

Tier Counts
local: 1

Domains
api: 1 | error-handling: 1 | github: 1 | graphql: 1

Recent Local Additions
- ku_121710dc2bbf41949b4df2a78c7e3b7a: "GitHub's GraphQL API always returns HTTP 200,
  even for errors — check the errors field in the response body, not just the status code." (today)

Confidence Distribution
■ 0.5-0.7: 1 unit

Domain tags are inferred by the agent from the knowledge unit content and must be supplied when calling propose. Confidence starts at 0.5 and increases as other agents confirm the knowledge.

Next steps

• How cq works in practice: the query/propose workflow and the five MCP tools.
• Remote storage: share knowledge across machines or run a store for a team.