Ctrlk
For the complete documentation index, see llms.txt. This page is also available as Markdown.

Introduction

Status: 0.x — expect breaking changes. See DEVELOPMENT.md for migration guides.

An open standard for shared agent learning — structured knowledge that prevents AI agents from repeating each other's mistakes.

The term cq is derived from two sources: colloquy (/ˈkɒl.ə.kwi/), a structured exchange of ideas where understanding emerges through dialogue rather than one-way output, and CQ, a radio call sign ("any station, respond"), capturing the same model: open invitation, response, and collective signal built through interaction. Both capture the same idea: agents broadcasting what they've learned and listening for what others already know.

Published components and tags

If you are looking for a specific cq component in a package registry, marketplace, or tagged GitHub release, use the names below.

Component
Where to get it
Published name
Release tag prefix

Plugin (Claude Code)

Claude plugin marketplace

mozilla-ai/cq (install as cq)

N/A

CLI

Homebrew/GitHub Releases

github.com/mozilla-ai/cq/cli

cli/vX.Y.Z

Go SDK

Go modules

github.com/mozilla-ai/cq/sdk/go

sdk/go/vX.Y.Z

Python SDK

PyPI

cq-sdk

sdk/python/X.Y.Z

Schema

PyPI and Go modules

cq-schema and github.com/mozilla-ai/cq/schema

schema/vX.Y.Z

Server image

GHCR and Docker Hub

ghcr.io/mozilla-ai/cq/server and mzdotai/cq-server

server/vX.Y.Z

Plugin Installation

Requires: uv, Python 3.11+

Optional (for Go SDK and Go CLI): Go 1.26.1+

Claude Code (plugin)

claude plugin marketplace add mozilla-ai/cq
claude plugin install cq

Other Agents

Run make setup-plugin before running the relevant Makefile target:

Agent
Install

OpenCode

make install-opencode

Cursor

make install-cursor

Windsurf

make install-windsurf

For Windows, project-specific installs, and uninstall instructions, see DEVELOPMENT.md.

Verify the plugin is working

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

You should see:

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:

Check your store

Run /cq:status again:

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.

How cq works in practice

You typically do not propose knowledge units manually. cq works through two agent workflows:

Skill-guided query/propose workflow

When your agent starts a task or encounters an error, the cq skill directs the agent to query the knowledge store before the agent retries.

If another agent has already solved this problem, your agent gets the relevant guidance immediately, instead of debugging from scratch.

If your agent discovers something that would save another agent time, for example:

  • Undocumented API behavior

  • Non-obvious workaround for a known issue

  • Solution to an error that required multiple failed attempts to resolve

Then it will propose that learning as a knowledge unit.

Session reflection workflow

Run /cq:reflect at the end of a session. cq reviews what happened, identifies learnings worth sharing (debugging breakthroughs, undocumented API behaviour, workarounds), and proposes them for you. It checks the store first to avoid duplicates.

The five MCP tools underneath:

Tool
What it does

query

Search the knowledge store before acting

propose

Submit a new knowledge unit

confirm

Endorse an existing KU that proved correct

flag

Mark a KU as wrong or stale

status

Show store statistics

Team sharing

By default, knowledge stays local on your machine.

To share knowledge units across remote agents, machines, or a team, run the server component which uses values from the .env file:

Create a user:

Then configure the required environment variables for the AI coding assistant:

Variable
Description

CQ_ADDR

Remote API URL (e.g., http://localhost:3000)

CQ_API_KEY

API key for authenticated write operations (propose, confirm, flag); optional for read-only use (query, stats). This can be generated in the remote server's UI dashboard.

Knowledge proposed locally will be automatically drained to the remote store when the plugin starts, and available to agents once graduated via human review.

Architecture

How the pieces fit together

cq runs across three runtime boundaries:

  1. Agent process — the plugin loads SKILL.md, which guides when and how the agent uses cq tools.

  2. Local MCP server — spawned via stdio, runs the Go based CLI (mcp-go), exposes the five tools above, owns the local SQLite store which defaults to ~/.local/share/cq/local.db.

  3. Remote API (optional) — runs in a Docker container as a separate FastAPI service. In production this would be hosted with auth, tenancy, and RBAC. See docs/architecture.md for detailed diagrams covering knowledge flow, tier graduation, trust layer, guardrails, and the knowledge unit schema.

Contributing

See CONTRIBUTING.md for project contribution guidelines, DEVELOPMENT.md for project structure, setup, and building from source; SECURITY.md for the security policy and vulnerability reporting guidance.

License

Apache 2.0

NextArchitecture

Last updated