Gemini AI Code Reviewer workflow (truongnh1992/gemini-ai-code-reviewer)

The Gemini AI Code Reviewer workflow from truongnh1992/gemini-ai-code-reviewer, explained and optimized by Latchkey.

What it does

This is the Gemini AI Code Reviewer workflow from the truongnh1992/gemini-ai-code-reviewer repository, a real project running GitHub Actions. It is shown here with attribution under its MIT license.

Below, Latchkey shows a faster, safer version produced by its optimization engine.

The workflow

name: Gemini AI Code Reviewer

on:
  issue_comment:
    types: [created]
permissions: write-all
jobs:
  gemini-code-review:
    runs-on: ubuntu-latest
    if: |
      github.event.issue.pull_request &&
      contains(github.event.comment.body, '/gemini-review')
    steps:
      - name: Checkout Repo
        uses: actions/checkout@v3
        with:
          fetch-depth: 0

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'

      - name: Install dependencies
        run: |
          pip install -r requirements.txt

      - name: Run Gemini AI Code Reviewer
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
        run: |
          python3.10 review_code_gemini.py

The same workflow, on Latchkey

Estimated ~20% faster on cache hits, plus fewer wasted runs and a safer supply chain. Added and changed lines are highlighted.

name: Gemini AI Code Reviewer
 
on:
  issue_comment:
    types: [created]
permissions: write-all
jobs:
  gemini-code-review:
    timeout-minutes: 30
    runs-on: latchkey-small
    if: |
      github.event.issue.pull_request &&
      contains(github.event.comment.body, '/gemini-review')
    steps:
      - name: Checkout Repo
        uses: actions/checkout@v3
        with:
          fetch-depth: 0
 
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          cache: 'pip'
          python-version: '3.10'
 
      - name: Install dependencies
        run: |
          pip install -r requirements.txt
 
      - name: Run Gemini AI Code Reviewer
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
          GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}
        run: |
          python3.10 review_code_gemini.py

What changed

• Run on Latchkey managed runners with one line (runs-on), which apply the fixes below automatically and self-heal transient failures. This example uses latchkey-small; pick the runner size that fits the job.
• Cache dependency installs on the setup step so they are served from cache.
• Add a job timeout so a hung step cannot burn hours of runner time.

What Latchkey heals here

This workflow has steps that commonly fail on transient issues (network, registries, flaky browsers). On Latchkey managed runners they are detected, retried, and self-healed instead of failing your build:

• Dependency installs

This workflow runs 1 job per trigger. On Latchkey the same minutes cost up to 58% less than GitHub-hosted, with zero queue time.

Actions used in this workflow