AutoAgent Action is a GitHub Action that integrates AI agents like Cursor CLI, Claude Code, and Gemini CLI into Pull Request workflows. It allows developers to run AI prompts on repositories during CI/CD pipelines, automating code reviews, suggestions, and analysis. This tool connects to GitHub repositories and AI agent CLIs, benefiting development and operations teams.
git clone https://github.com/erans/autoagent-action.gitAutoAgent Action is a composable GitHub Action that integrates multiple AI coding agents into Pull Request workflows. It runs predefined rules like OWASP security checks and code reviews, plus custom prompts, on repository code during CI/CD pipelines. The action automatically installs supported agents (Cursor CLI, Claude Code, Gemini CLI, Codex, Amp Code, OpenCode) and posts structured analysis results as GitHub PR comments. Teams can analyze only changed files for speed or the entire codebase for comprehensive coverage, with configurable logging for troubleshooting.
Add the GitHub Action to your workflow YAML file, specify which AI agent to use (cursor, claude, gemini, codex, amp, or opencode), provide the corresponding API key as a secret, and configure predefined rules or custom prompts. The action automatically installs the agent and posts results as PR comments.
Automated OWASP security vulnerability scanning on pull requests
Continuous code review and refactoring suggestions during PR workflows
Custom team-specific code analysis rules in CI/CD pipelines
SQL query optimization detection and performance analysis
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/erans/autoagent-actionCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Analyze the code changes in this pull request for [REPOSITORY] and suggest improvements. Focus on [SPECIFIC_ASPECT] such as performance, security, or readability. Provide actionable feedback in bullet points. Use [AI_TOOL] for the analysis.
## Code Review Analysis for [REPOSITORY] Pull Request #123 ### Performance Improvements - The `data_processing.py` script can be optimized by using list comprehensions instead of for loops, reducing execution time by ~20%. - Consider implementing memoization for the `calculate_stats()` function to avoid redundant computations. ### Security Recommendations - Add input validation for the `user_input` parameter in `api_endpoint.py` to prevent potential injection attacks. - Ensure sensitive environment variables are properly masked in the deployment logs. ### Readability Enhancements - Break down the lengthy `generate_report()` function into smaller, modular functions with clear docstrings. - Use consistent naming conventions for variables across the codebase (e.g., `user_data` vs. `userData`).
AI assistant built for thoughtful, nuanced conversation
Google's multimodal AI model and assistant
Let’s build from here, together
The AI Code Editor for productive developers
Serverless CI/CD for Google Cloud
Cloud computing services and AI infrastructure by Google
Take a free 3-minute scan and get personalized AI skill recommendations.
Take free scan