sdd-riper-one

Implement spec-driven development workflows with RIPER stage gates for auditable AI projects.

Updated Apr 11, 2026
One-click install
npx skills add https://github.com/AngelWings1997/altas --skill sdd-riper-one
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sdd-riper-one
Source: https://github.com/AngelWings1997/altas/tree/main/altas-workflow/references/agents/sdd-riper-one
Command: npx skills add https://github.com/AngelWings1997/altas --skill sdd-riper-one

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Spec-driven development with RIPER stage gates enforces No Spec No Code, ensures explicit Plan approvals, and preserves auditable traces across AI projects.

Core Features & Use Cases

  • Spec-driven lifecycle: Maintains a rigorous Spec-first approach with live updates tied to CodeMap, context bundles, and tri-axis reviews.
  • RIPER workflow integration: Guides Research → RIPER → Plan → Execute → Review with explicit gatekeeping.
  • Use Case: Teams shipping AI features can rely on verifiable specs, controlled execution, and structured archiving to demonstrate compliance and reproducibility.

Quick Start

Operate the RIPER workflow by generating a CodeMap, bundling context, and bootstrapping RIPER to produce the initial Spec.

Frequently Asked Questions about sdd-riper-one

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

FAQPage Schema
How do I implement a spec-driven development workflow for AI projects?

A spec-driven development workflow enforces a rigorous Spec-first approach, generating a CodeMap and bundling context to maintain live updates tied to tri-axis reviews. You bootstrap the RIPER stages to produce the initial Spec before any code is written.

Can I use RIPER stage gates for mid-to-large AI initiatives across multiple teams?

Yes, RIPER stage gates apply to mid-to-large AI initiatives across multiple teams. The workflow enforces strict gating, explicit Plan approvals, and structured archiving to ensure controlled execution and compliance across distributed groups.

How do I enforce explicit plan approvals and spec-traceability in AI development?

You enforce explicit plan approvals and spec-traceability by applying the RIPER workflow gates. This process requires Research, Plan, Execute, and Review stage gatekeeping, ensuring every code execution step is verifiable against the initial Spec.

What is the best way to maintain spec-traceability and context bundling during AI feature development?

The best way to maintain spec-traceability is through a spec-driven lifecycle with CodeMap and context bundling. This approach ties live Spec updates directly to CodeMap changes and tri-axis reviews, preserving auditable traces throughout the workflow.

What are the limitations of a strict RIPER workflow for smaller AI projects?

A strict RIPER workflow with stage gates, explicit Plan approvals, and structured archiving is designed for mid-to-large AI initiatives. Smaller projects may find the rigid spec-driven gating and pre-research utilities too heavy for rapid execution.

Why do I need pre-research utilities and tri-axis reviews for AI feature shipping?

Pre-research utilities and tri-axis reviews are needed to demonstrate compliance and reproducibility when shipping AI features. They provide verifiable specs and structured archiving that ensure controlled execution across multiple teams.