Orchestrate a greenfield reverse-specification workflow into SDD artifacts with CONTINUAR checkpoints.
npx skills add https://github.com/sandeco/reversa --skill reversa-new-sandeco
Please help me install this Agent Skill. Skill: reversa-new Source: https://github.com/sandeco/reversa/tree/main/agents/reversa-new Command: npx skills add https://github.com/sandeco/reversa --skill reversa-new-sandeco
Reversa-new turns a natural-language product idea into complete, traceable specification documents that an AI coding agent can safely use, instead of relying on vague intent or “vibe coding”.
.reversa/state.json and requiring explicit CONTINUAR between agents.newproject-brief.md, ideation.md, personas.md, prd.md, and sdd/<componente>.md to support a later /reversa-forward evolution cycle.Tell the skill what you want to build by running: /reversa-new "Describe your product idea in one or two paragraphs".
High-intent search queries and answers about installing and using this skill.
To generate AI-ready SDD artifacts from a product idea, use a greenfield reverse-specification workflow. It orchestrates named sub-agents to draft ideation, personas, PRDs, and component specs, preventing vague intent in forward engineering.
Drafting a PRD for greenfield planning without vibe coding requires transforming your initial idea into structured, traceable documents. A checkpointed orchestration pipeline synthesizes research and ideation into an operational contract for AI agents.
You orchestrate AI agents to produce operational contracts by coordinating named sub-agents in a fixed sequence. This pipeline tracks stages via state.json checkpoints, requiring explicit CONTINUAR confirmations to prevent accidental reruns during synthesis.
Yes, this greenfield specification workflow requires explicit CONTINUAR confirmations between stages. It tracks the pipeline stage in a state.json checkpoint file and never auto-advances, ensuring user-controlled execution and preventing accidental reruns.
Yes, you can use this orchestration pipeline to synthesize user personas and research data. The workflow processes ideation and persona synthesis, outputting dedicated markdown files to support the later forward engineering evolution cycle.
A limitation of using checkpointed pipelines for product requirements is the strict manual intervention required. The workflow never auto-advances without explicit CONTINUAR confirmations, meaning you must actively manage transitions between the orchestrated sub-agents.
afsjrOrchestrates greenfield product planning pipeline from natural-language idea to SDD-ready documentation.
simonheimlicherDecompose PRDs into capability specs with BSP ordering and templates.
fcouryGenerate structured PRDs with markdown and prd.json output.
RdindaOrchestrate a greenfield product pipeline from natural-language ideas to PRD and SDD outputs.