ralph-setup_skill

Overview

This skill sets up automated agent-driven development using Ralph. It orchestrates a loop of AI agents that implement user stories, verify acceptance criteria, and log progress for subsequent agents. The setup integrates with a recipe-driven resource to bootstrap the loop and configuration. It is designed for TypeScript full-stack projects and production-ready patterns.

How this skill works

The setup fetches the Ralph Agent Loop recipe from a central recipe resource to configure the agent orchestration and runtime. Once configured, agents run in iterative cycles: one or more agents implement features from a user story, dedicated verification agents check acceptance criteria, and a logging agent records outcomes and next steps. The loop repeats until acceptance is achieved or a stopping condition is met, producing structured logs and artifacts for each iteration.

When to use it

• You want to automate feature implementation from user stories end-to-end.
• You need a reproducible loop of implementation, verification, and handoff between agents.
• You are building a TypeScript full-stack AI-assisted application and want production-ready patterns.
• You want continuous, logged progress for auditability and incremental delivery.
• You need a recipe-driven bootstrap for consistent agent configuration across environments.

Best practices

• Start by reviewing and customizing the recipe to match your project conventions and test suites.
• Configure clear acceptance criteria and deterministic tests to guide verification agents.
• Limit each agent's scope per iteration to avoid large, hard-to-review changes.
• Persist logs and artifacts from each loop cycle for debugging and rollback.
• Run the loop in isolated environments first (feature branch or staging) before production.

Example use cases

• Automating small feature tickets: implement, test, and log progress until acceptance.
• Continuous refinement of UI components using Shadcn patterns and recipes.
• Scaling a team’s throughput by letting agents produce repeatable implementation artifacts.
• Creating audit trails for AI-driven changes in a full-stack TypeScript app.
• Prototyping new integrations quickly by iterating until acceptance criteria are met.