AI product Open source
Habit Hooks is a developer tool that steers AI coding agents toward code-quality and refactoring practices. It runs project linters and replaces each raw rule violation with a short coaching guide that explains an actionable fix, using the linter finding as a cue and the guide as the response rather than presenting the agent with a bare metric. The project is designed to reduce metric gaming, technical debt, and the context needed for later tasks; its repository reports an independent study in which coaching produced genuine fixes more often than bare linter targets.
It is installed as a Python package with `uv`, pip, pipx, or Homebrew, then initialized in a project with `habit-hooks init`. Initialization detects the project language and writes `.habit-hooks/config.toml`; separate plugins are provided for Python, TypeScript, PHP, and Java, with language-specific detectors such as Ruff, ESLint, and jscpd enabled separately.
1 use taken from transcripts — each links to the moment in the video.
Combines linter sensor output with refactoring guidance to catch and fix poorly designed code from coding agents, reducing token usage and future errors.
1 in the library.