autoagent-action

🥈Silver

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.

1500Updated 1mo ago

Intermediate30min to implementautomation

Saves ~90 min per use

Quick InstallView Source

git clone https://github.com/erans/autoagent-action.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/erans/autoagent-action

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

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.

Example Output

## 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`).