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.
Best use case
ralph-loop is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using ralph-loop should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ralph-loop/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ralph-loop Compares
| Feature / Agent | ralph-loop | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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. ## Prerequisites Complete these recipes first (in order): ### AI Coding Agent Configuration Configure AI coding agents like Cursor, GitHub Copilot, or Claude Code with project-specific patterns, coding guidelines, and MCP servers for consistent AI-assisted development. ```bash curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/agent-setup ``` ## Cookbook - Complete These Recipes in Order ### User Stories Setup Create a structured format for documenting feature requirements as user stories. JSON files with testable acceptance criteria that AI agents can verify and track. ```bash curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/user-stories-setup ``` ### Working with User Stories Document and track feature implementation with user stories. Workflow for authoring stories, building features, and marking acceptance criteria as passing. ```bash curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/using-user-stories ``` ### Ralph Agent Loop Set up automated agent-driven development with Ralph. Run AI agents in a loop to implement features from user stories, verify acceptance criteria, and log progress for the next agent. ```bash curl -H "Accept: text/markdown" https://fullstackrecipes.com/api/recipes/ralph-setup ```
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