angry-ralph

Converts a feature spec into reviewed, tested codebase via six-phase pipeline with resume capability.

5|Updated Feb 26, 2026
One-click install
npx skills add https://github.com/Custos/angry-ralph --skill angry-ralph
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: angry-ralph
Source: https://github.com/Custos/angry-ralph/tree/main/skills/angry-ralph
Command: npx skills add https://github.com/Custos/angry-ralph --skill angry-ralph

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

angry-ralph is a unified orchestration pipeline that converts a user-provided feature spec into a fully implemented, tested codebase through six sequential phases, combining decomposition, planning, adversarial review, and TDD-driven execution.

Core Features & Use Cases

  • End-to-end planning: decompose specs, plan implementations, and coordinate multi-LLM adversarial review to surface issues early.
  • Deterministic execution with gating: drive a Ralph Loop that enforces TDD red-green cycles and gating on tests.
  • Resumable sessions: persist state and artifacts so sessions can resume after interruptions.
  • Adversarial quality control: leverage external reviewers to improve plans and code before delivery.
  • Use Case: Plan and implement a feature from a high-level spec and obtain a reviewed, tested codebase ready for integration.

Quick Start

Load a feature spec and start the six-phase pipeline from decomposition through final review.

Frequently Asked Questions about angry-ralph

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate a feature spec into a tested codebase with TDD?▼

The pipeline uses a six-phase process that decomposes your feature spec, creates a detailed plan, applies adversarial multi-LLM review, and drives TDD red-green cycles to produce reviewed, tested code.

What is adversarial multi-LLM review in a code generation pipeline?▼

Adversarial multi-LLM review is a structured quality control gate where external LLMs critique implementation plans and code to surface issues early, ensuring robust output before final integration.

How do I resume an interrupted TDD pipeline session?▼

You resume an interrupted TDD pipeline using built-in state persistence that saves artifacts and progress, allowing the orchestration pipeline to recover and continue execution seamlessly after interruptions.

Can I use adversarial review gates for large feature decomposition?▼

Yes, adversarial review gates support large feature decomposition by splitting detailed plans into sections and enforcing structured review gates across each section before final integration.

What are the limitations of using a structured TDD pipeline for feature implementation?▼

The pipeline enforces strong prerequisites and structured review gates, meaning it requires a complete feature spec upfront and may halt execution if strict TDD gating tests fail during the Ralph Loop cycles.