kunchenguid / no-mistakes
Summary
no-mistakes is a local Git proxy designed to automate code validation and ensure clean pull requests before changes reach a remote repository. It operates by intercepting "git push" commands, spinning up a disposable worktree, and running an AI-driven validation pipeline. This pipeline checks for issues, applies safe, mechanical fixes automatically, and escalates intent-touching changes for human approval. The tool supports various AI agents, including "claude", "codex", "rovodev", "opencode", "pi", and "acp:" via "acpx", and is non-blocking, allowing developers to continue working. It functions across macOS, Linux, and Windows, and can be triggered via "git push no-mistakes", a Terminal User Interface (TUI), or as an agent skill ("/no-mistakes"). Only after all checks pass does it forward the branch and automatically open a pull request.
Key takeaway
For Software Engineers aiming to streamline code review and ensure high-quality pull requests, no-mistakes offers a robust solution. You should integrate this local Git proxy to automate pre-push validation, reducing manual effort and preventing common errors from reaching the main branch. This approach ensures your contributions are clean by default, freeing up time for more complex development tasks and improving overall code integrity. Consider using "no-mistakes init" to quickly set up the gate for your repositories.
Key insights
Automate code validation and PR creation using an AI-driven Git proxy to ensure clean, pre-vetted code.
Principles
- Isolated worktrees for non-blocking validation.
- AI-driven checks with human oversight.
- Automated PR generation post-validation.
Method
The tool intercepts "git push", creates a disposable worktree, runs review, test, docs, and lint checks, applies safe fixes, escalates complex findings, then pushes and opens a PR.
In practice
- Use "git push no-mistakes" for gated commits.
- Initialize with "no-mistakes init" for setup.
- Integrate "/no-mistakes" skill for AI agents.
Topics
- Git Workflow Automation
- Code Quality Gates
- AI-driven Development
- Pull Request Automation
- Disposable Worktrees
- Developer Tools
Code references
Best for: Machine Learning Engineer, NLP Engineer, Computer Vision Engineer, Software Engineer, AI Engineer, MLOps Engineer
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