reversa-spec-sdd

Name: reversa-spec-sdd
Availability: InStock
Author: Sandeco

Generate component-level SDD specifications from a Reversa PRD.

1.5k|383|Updated Apr 26, 2026

Search Tags:#requirements #ai workflow #prd #quality scoring #sdv #agent readiness #spec sdd

Ready to run

npx skills add https://github.com/sandeco/reversa --skill reversa-spec-sdd-sandeco

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: reversa-spec-sdd
Source: https://github.com/sandeco/reversa/tree/main/agents/reversa-spec-sdd
Command: npx skills add https://github.com/sandeco/reversa --skill reversa-spec-sdd-sandeco

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Converts PRDs into complete, traceable Spec-Driven Development (SDD) specs for AI-assisted implementation, reducing ambiguity and misalignment when moving from product requirements to executable agent-ready contracts.

Core Features & Use Cases

PRD-to-SDD decomposition: Breaks prd.md into logical components, one spec per component, based on the PRD’s scope, personas, and acceptance criteria.
Spec drafting with RFC-Pragmatic structure: Produces LLM-ready SDD documents that define what should happen (not how), including functional requirements and acceptance criteria.
Automated quality scoring and iteration: Scores each generated spec (0–100) against completeness, testability, clarity, scope, and edge-case coverage, then iterates up to defined limits.
Output ready for the forward pipeline: Writes specs under _reversa_sdd/sdd/ and finishes with a handoff to /reversa-forward, enabling the next step of the Reversa workflow.

Quick Start

Run /reversa-spec-sdd after /reversa-new so the skill reads _reversa_sdd/prd.md and generates _reversa_sdd/sdd/<componente>.md with quality scores.

Frequently Asked Questions about reversa-spec-sdd

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

FAQPage Schema

How do I convert a PRD into testable SDD specs for AI agents?▼

To convert a PRD into testable SDD specs, you decompose product scope into logical components and generate one structured Spec-Driven Development markdown file per component with explicit acceptance criteria and quality scores.

What is spec-driven development decomposition for AI workflow?▼

Spec-driven development decomposition breaks product requirements into logical parts, drafting LLM-ready functional specs that define what should happen rather than how, reducing ambiguity for AI-assisted implementation.

How are SDD specs scored for quality and agent readiness?▼

SDD specs are scored 0–100 against completeness, testability, clarity, scope, and edge-case coverage, iterating automatically up to defined limits to ensure agent readiness before handoff.

Can I generate a single SDD spec from user-provided input without a full pipeline?▼

Yes, you can generate a single SDD spec from user-provided input for standalone evaluation, or apply the skill within pipeline execution by reading a standard prd.md file to output multiple component specs.

What is the best way to structure acceptance criteria in an SDD markdown file?▼

The best way to structure acceptance criteria in an SDD markdown file is using an RFC-Pragmatic structure with required RF IDs, explicit acceptance criteria, and 🟡 PLANJADO tagging for traceable agent-ready contracts.

What happens after generating SDD specs in the Reversa workflow?▼

After generating SDD specs, the workflow finishes with a handoff to /reversa-forward, enabling the next step of the Reversa pipeline by writing specs under the designated sdd directory for downstream execution.