What is the Ralph Method?

The Ralph Method (based on Ralph Wiggum methodology) is a multi-phase workflow for AI coding that prioritizes deep planning and requirement definition before any implementation code is written.

Does this skill write implementation code?

No, this specific skill focuses on Phase 1 (Requirements) and Phase 2 (Planning). Implementation is handled in Phase 3 using the generated plans and specialized runner scripts like ralph.sh.

What documentation does it generate?

It creates a dedicated specs folder containing a Product Requirements Document (PRD.md), an Implementation Plan, Backpressure configurations for quality control, and Agent Context files.

How do I start the coding phase after using this skill?

Once this skill completes the planning phase, it provides instructions to run ralph.sh or ralph-one.sh, which use the generated specs to execute the implementation tasks autonomously.

Why is 'Backpressure' important in this method?

Backpressure configurations define the automated tests and linting checks that must pass before an AI agent is allowed to commit code, ensuring high quality throughout the building loop.

Ralph Method Planning & Specs

Name: Ralph Method Planning & Specs
Author: AlteredCraft

byAlteredCraft

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生产力与工作流

Guides users through the Ralph Wiggum methodology to define requirements and create implementation plans before starting code generation.

关于

The Ralph Method skill implements the first two phases of the Ralph Wiggum methodology, a rigorous approach to AI-assisted software development. It acts as a requirements engineer, conducting structured interviews to identify Jobs to Be Done (JTBD) and decomposing vague tasks into granular, machine-verifiable specifications. By generating a namespaced suite of documentation—including PRDs, backpressure configurations, and detailed implementation plans—this skill ensures that autonomous coding agents have a clear 'Definition of Done' and a structured path to success, significantly reducing the risk of loop divergence and technical debt.

主要功能

Automated namespaced specification generation (PRD, Backpressure, Agent Context)
Git branch management integration for feature-specific development
Detailed implementation planning with task ordering and dependencies
Deployment of autonomous loop runner scripts (ralph.sh and ralph-one.sh)
Structured requirements interviewing based on JTBD and Topic decomposition
10 GitHub stars

使用场景

Standardizing 'Definition of Done' and quality checks for complex software projects
Preparing structured context and guardrails for autonomous AI coding agents
Transforming high-level, vague feature requests into actionable technical specifications

关于

主要功能

Automated namespaced specification generation (PRD, Backpressure, Agent Context)
Git branch management integration for feature-specific development
Detailed implementation planning with task ordering and dependencies
Deployment of autonomous loop runner scripts (ralph.sh and ralph-one.sh)
Structured requirements interviewing based on JTBD and Topic decomposition
10 GitHub stars

使用场景

Standardizing 'Definition of Done' and quality checks for complex software projects
Preparing structured context and guardrails for autonomous AI coding agents
Transforming high-level, vague feature requests into actionable technical specifications