ralph-loop
Complete setup for automated agent-driven development. Define features as user stories with testable acceptance criteria, then run AI agents in a loop until all stories pass.
Security Assessment
About ralph-loop
The ralph-loop skill provides a comprehensive framework for automated, agent-driven development. It allows developers to define project features as structured user stories with testable acceptance criteria and then leverages AI agents to iteratively implement and verify these features. By running AI agents in a continuous loop, the skill ensures that each feature meets its acceptance criteria, reducing manual oversight and streamlining the development workflow. This approach addresses the common challenge of coordinating multiple AI-driven tools for consistent and reliable feature implementation.
Key features include the configuration of AI coding agents such as Cursor, GitHub Copilot, or Claude Code for project-specific patterns and coding guidelines. It supports structured user story creation and management, enabling AI agents to verify progress and track acceptance criteria systematically. The Ralph Agent Loop automates feature implementation by repeatedly executing AI agents, logging results, and ensuring all criteria pass before completion. These capabilities allow for a cohesive and repeatable AI-assisted development process that integrates coding, testing, and documentation seamlessly.
Ralph-loop is ideal for development teams and technical leads seeking to enhance productivity through AI-assisted automation. It is particularly useful for projects requiring strict adherence to feature specifications and testable outcomes. Use cases include iterative software development, automated testing, and continuous integration workflows. Target users range from individual developers experimenting with AI-driven coding to larger teams aiming to standardize their development process using automated agent loops, improving both efficiency and code quality.
FAQ
How do I start using ralph-loop?
Begin by configuring your AI coding agents according to project-specific guidelines, then create structured user stories with testable acceptance criteria. Once set up, run the Ralph Agent Loop to automate feature implementation.
Which AI coding agents are compatible with ralph-loop?
Ralph-loop supports agents like Cursor, GitHub Copilot, and Claude Code, as long as they can be configured for project-specific patterns and coding guidelines.
Do I need to complete any prerequisites before using ralph-loop?
Yes, you should first configure AI coding agents and set up user stories following the provided recipes to ensure proper automated development flow.
Can ralph-loop handle multiple features simultaneously?
Ralph-loop is designed to implement features iteratively based on user stories. While it can manage multiple stories, each story is processed through the agent loop until its criteria are met.
Are there any limitations or requirements for using ralph-loop?
You need a compatible AI coding agent and properly formatted user stories with testable acceptance criteria. The process relies on continuous agent execution to ensure all criteria pass.
Install ralph-loop
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
andrelandgraf/fullstackrecipes