Automate Your GitHub Workflow with Gemini CLI

Source: Dev.to

What Exactly Does This Tool Do?

Before we dive into setup, let’s clarify what we’re working with. The Gemini CLI GitHub Action gives you three main capabilities:

• Issue Triage – It reads new issues and automatically labels them (bug, feature, etc.)
• Code Fixes – It can analyze issues and write code to solve them
• Pull Request Reviews – It provides automated code reviews with suggestions

Think of it as having an AI developer on your team that works 24/7, never gets tired, and can handle the routine tasks that eat up your time.

Full review on YouTube

Prerequisites: What You Need to Know

This guide assumes you have basic familiarity with:

• GitHub repositories (creating repos, issues, and pull requests)
• Command‑line basics
• A project you can test with

If you’re new to GitHub, spend some time with their getting started guide first. You’ll need to be comfortable creating issues and managing repositories.

Step‑by‑Step Setup Guide

Step 1 – Install and Update Gemini CLI

Make sure you have the Gemini CLI installed and updated to the latest version. If you don’t have it yet, check the official installation guide.

# Verify installation
gemini --version

Tip: Using the latest version is crucial for the GitHub integration to work properly.

Step 2 – Set Up the GitHub Action

Navigate to your project directory and run:

gemini setup github

The command takes just a few seconds and adds the necessary GitHub Action files to your repository. You’ll see new files in the .github/workflows directory that define how the AI teammate will respond to different events.

Step 3 – Commit and Push

Commit the newly added files and push them to GitHub. The actions won’t run until the files are in the remote repository.

git add .
git commit -m "Add Gemini CLI GitHub Actions"
git push

Step 4 – Add Your Gemini API Key

This step isn’t obvious from the documentation but is absolutely essential.

1. Go to your GitHub repository → Settings.

2. Navigate to Security → Secrets and variables → Actions.

3. Click New repository secret.

4. Name it GEMINI_API_KEY.

5. For the value, obtain an API key from Google AI Studio:

   • Open Google AI Studio in a new tab.
   • Click Get API key → Create new API key.
   • Copy the generated string and paste it as the secret value.

Testing Your New AI Teammate

Now for the fun part! Let’s see what this thing can actually do.

Testing Issue Triage

I created a new issue in my Flutter shopping‑list project with the following description:

When a user hits enter on edit mode of an item, the system will end the edit mode and update as usual, and open a new empty item under the edited item that is focused and ready to be edited.

To trigger triage, I added this comment to the issue:

@gemini-cli triage this issue

Within a few minutes, the action ran and automatically labeled the issue as bug. The action logs showed that it analyzed the description and determined the problem described missing functionality rather than a new feature request.

Testing Code Fixes

Next, I asked the AI to fix the issue:

@gemini-cli fix this issue

The AI spent several minutes analyzing the code, creating a detailed plan with checkboxes, and then implementing the changes. The workflow looked like this:

1. Read and analyze the issue.
2. Examine the existing codebase.
3. Create a detailed implementation plan.
4. Execute the plan step‑by‑step.
5. Create a new branch with the changes.

The result was a new branch (e.g., fix-enter-key-functionality) containing the working code.

Testing Pull Request Review

Finally, I manually created a pull request from the branch the AI generated (the AI couldn’t create the PR automatically for some reason). Almost immediately, another action triggered and posted an automated review with suggestions and potential improvements.

Wrap‑Up

The Gemini CLI GitHub Action turned out to be a surprisingly capable “AI teammate.” It can:

• Label issues automatically, saving you triage time.
• Generate code fixes without manual intervention, producing branches ready for review.
• Provide automated PR reviews, catching style issues and potential bugs early.

Give it a try on a small side project first, then consider rolling it out to larger repositories. As with any automation, keep an eye on the output and adjust prompts or configurations as needed. Happy coding!

What Actually Worked (And What Didn’t)

Let’s be honest about the results.

The good news: the code actually worked! When I tested my Flutter app, pressing Enter while editing an item updated the item and created a new one. The functionality was implemented correctly despite my vague description.

However, there were some limitations:

• The AI had trouble with Flutter/Dart compared to JavaScript projects (there’s simply more training data available for web technologies).
• It took longer to debug and correct Flutter‑specific issues.
• The autonomous nature felt strange compared to tools like Cursor, where I can approve changes line by line.
• It made some unwanted changes to my configuration files.
• The pull‑request creation failed, requiring manual intervention.

Pro Tips for Better Results

Based on my testing, here are some recommendations:

1. Write Better Issue Descriptions

Even though my vague description worked, you’ll get much better results with detailed requirements, acceptance criteria, and context about your project. Always break the work down into small pieces.

2. Use Different AI Tools for Different Tasks

I recommend using separate AI tools for writing code versus reviewing it. For example, let Claude write the code and use Gemini (or another tool) for the review. This provides a fresh perspective and catches issues the original tool might miss.

3. Review the Prompts

One of the best features is that all the prompts are visible and customizable. Check out the .github/workflows files to see exactly what instructions the AI is following, and modify them to match your team’s standards.

4. Start Small

Test this on smaller, non‑critical repositories first. Get comfortable with how it works before deploying it on your main projects.

The Bottom Line: Should You Try It?

Absolutely. Even with its limitations, this tool represents a significant step forward in AI‑assisted development. It’s particularly valuable for:

• Solo developers who want help with routine tasks
• Open‑source maintainers dealing with lots of issues and PRs
• Teams looking to standardize their review process
• Anyone curious about autonomous AI coding

The setup is straightforward, it’s free to use (you only pay for Gemini API usage), and the prompts are completely customizable. Even if you don’t use it as‑is, studying the prompts and workflow structure provides excellent insights into effective AI automation.

Ready to Get Started?

Here’s your action plan:

1. Pick a test repository (not your main project!).
2. Follow the setup steps above.
3. Create a simple issue to test triage functionality.
4. Try the fix command on something small.
5. Experiment with customizing the prompts to match your workflow.

Remember, this is still early‑stage technology. Approach it with curiosity rather than expecting perfection, and you’ll likely find genuinely useful automation for your development workflow.

The future of coding is increasingly collaborative between humans and AI. Tools like this give us a glimpse of what that partnership might look like—and honestly, it’s pretty exciting.

Have you tried the Gemini CLI GitHub Action? I’d love to hear about your experience and any creative ways you’ve customized it for your projects.