Analysis updated 2026-05-18
Break a large coding task into parallel sub-tasks handled by separate AI agents in isolated git worktrees.
Use a cheaper model to write code while a stronger model plans and a fresh-context reviewer checks the diff.
Automatically sync generated documentation with a companion skill when both are installed in the same repo.
| itboy79/brigade | 100/awesome-machine-learning | adam-s/car-diagnosis | |
|---|---|---|---|
| Stars | 8 | 8 | 8 |
| Language | Python | Python | Python |
| Last pushed | — | 2024-08-07 | — |
| Maintenance | — | Stale | — |
| Setup difficulty | moderate | easy | moderate |
| Complexity | 3/5 | 1/5 | 3/5 |
| Audience | developer | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires Python 3.9+ and access to at least one supported model provider such as Anthropic, OpenAI, or Ollama.
Brigade is a multi-agent build orchestrator packaged as a skill for Claude Code, built around the idea of splitting one big coding task across several AI agents that each play a different role, the way a professional kitchen splits cooking across different stations instead of asking one person to do everything. In Brigade's own terms, a strong model called the Chef reads the task and writes a set of tickets, which are self-contained sub-tasks, but never writes the code itself. Those tickets are handed to a group of cheaper or faster models called cooks, which can run on Anthropic, OpenAI-compatible, or Ollama models, and each cook works in its own git worktree when the work needs to be isolated from the others. Once a cook finishes, a separate reviewer model called the Pass looks only at the actual code diff and checks it against the acceptance criteria, without ever seeing the cook's explanation of what it did. If the Pass rejects the work, it goes back to the same cook, with its previous context kept, for up to two more attempts before it gets escalated to the user. A final step called Service runs an end to end check: building, testing, and integrating everything together. The stated reasoning is that big tasks tend to fail in a single agent because the context window fills up with implementation details, and because the same model that writes the code also ends up grading its own work. Splitting planning, cooking, and reviewing into separate contexts is meant to fix both problems, and using cheaper models for the repetitive cooking work while reserving the strongest model for planning is meant to save money too. Brigade has a companion project called Cerebro, a separate skill that writes reference documentation for a repository, and the two can work together automatically if both are installed, though neither requires the other. Installation is a git clone into a Claude Code skills folder, or running an included install script, after which the skill is triggered with a slash command or natural phrasing inside Claude Code. Configuration, including which model plays which role, is stored in a per-repo JSON config file. The project requires Python 3.9 or newer and is released under the MIT license.
A Claude Code skill that splits a coding task across separate planner, executor, and reviewer AI agents modeled on kitchen brigade roles.
Mainly Python. The stack also includes Python, Claude Code, Git.
MIT license: use, modify, and distribute freely, including commercially, as long as you keep the copyright notice.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.