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Control AI Code Changes with Goose and GitHub CLI

Job to be done: Prevent AI agents from making unwanted code changes by enforcing commit discipline.

🇳🇬 Ways to use this in Nigeria

Ideas to get you started, adapt to your situation.

  • Entrepreneur

    As a solo tech founder, use an AI agent to quickly add new features to your startup's MVP, knowing you can instantly revert any AI-introduced bugs or unwanted code to keep your product stable.

  • Student

    As a computer science student, use an AI agent to build a new feature for your final year project, ensuring every AI-generated code change is a separate, reviewable commit you can easily undo if it breaks your app.

  • 9-5 employee

    As a software developer, use an AI agent to refactor a complex module at work, ensuring each AI-suggested change is committed separately for easy review by your team lead and quick rollback if issues arise.

What you’ll get

A simple discipline that stops an AI coding agent from quietly making a mess of your project. By making the agent save a separate, clearly labelled “commit” (a saved snapshot in Git) after every change, your version history becomes a reliable undo button: if the AI does something you dislike, you roll back to the last good snapshot in seconds. This is intermediate: you use Git and a terminal, but the setup is short.

Tools you need

  • Goose (free): an open-source AI agent (by Block) that can read and edit your codebase. You give it the commit rule below.
  • GitHub CLI (free): the gh command-line tool for working with GitHub and Git from your terminal.

Steps

  1. Set up version control: install the GitHub CLI (gh) and make sure your project is a Git repository. Goose works smoothly with Git. (The GitHub MCP Server is a good alternative if you prefer it.)

  2. Always start on a new branch: before letting the AI touch anything, create a fresh “feature branch” (a separate line of work) so experiments stay isolated. Never let the agent commit straight to your main branch.

  3. Write the rule into a context file: create a file named .goosehints (or AGENTS.md) in your project. This file is the standing instruction the agent reads. The key line:

    Every time you make a change, make a commit with a clear message.

    This makes the agent checkpoint its own work automatically, turning each change into a reviewable snapshot.

  4. Prompt the agent: now you can let Goose build, fix, or refactor with confidence:

    Build the user authentication module based on the provided requirements.

    As it works, it should commit each change with a clear message, exactly as the rule instructs.

  5. Review, and roll back if needed: check the history with git log to see each AI change as its own commit. To undo one specific bad change safely (this makes a new commit that reverses it):

    git revert <commit-hash>

    To throw away everything and return to a known-good snapshot (this discards later work, so be sure):

    git reset --hard <commit-hash>

    Replace <commit-hash> with the ID shown in git log.

Original source

Based on a DEV Community post by blackgirlbytes describing how to keep AI agents in check by combining them with ordinary good practice: early, frequent, clearly-labelled commits.

Notes & variations

  • Free-tier viability: Goose is free and open-source and the GitHub CLI is free, so this costs nothing.
  • Common pitfall: letting the agent commit directly to main. Always work on a separate feature branch so a bad change never touches your stable code.
  • Tip for better results: keep the instruction in .goosehints (or AGENTS.md) short and unambiguous. Clear rules produce clear, frequent commits, which is what makes the undo button reliable.

Keep going

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