flow-spec

Translate multi-AI research into a structured NLSpec with frontmatter metadata.

4.0k|369|Updated Jan 15, 2026
One-click install
npx skills add https://github.com/nyldn/claude-octopus --skill flow-spec
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: flow-spec
Source: https://github.com/nyldn/claude-octopus/tree/main/skills/flow-spec
Command: npx skills add https://github.com/nyldn/claude-octopus --skill flow-spec

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

NLSpec authoring from multi-AI research is tedious, brittle, and error-prone. This skill automates the generation of a structured specification from disparate research outputs, ensuring consistency and traceability across teams.

Core Features & Use Cases

  • Structured NLSpec with meta, actors, behaviors, constraints, dependencies, and acceptance criteria.
  • Multi-AI research synthesis: aggregates artifacts from Codex, Gemini, and Claude into a single spec.
  • Versioned, reproducible outputs suitable for handoff to development and governance.

Quick Start

Describe your project, key actors, and expected outcomes to generate an NLSpec.

Frequently Asked Questions about flow-spec

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

FAQPage Schema
How do I generate a structured software specification from multi-AI research outputs?▼

Generate a structured software specification from multi-AI research by automating the synthesis of artifacts from tools like Codex, Gemini, and Claude into a single versioned NLSpec document, ensuring consistency and traceability across teams.

What is an NLSpec and when do I need it for documentation pipelines?▼

An NLSpec is a structured natural language specification containing meta, purpose, actors, behaviors, constraints, dependencies, and acceptance criteria, needed for documentation pipelines requiring clear, repeatable spec artifacts for development handoff.

How do I aggregate artifacts from multiple AI models into a single specification?▼

Aggregate artifacts from multiple AI models into a single specification by translating disparate research outputs into a reproducible NLSpec structure, applying frontmatter metadata to maintain versioned and context-rich specification workflows.

Does this specification workflow support frontmatter metadata and versioning for governance?▼

Yes, this specification workflow supports versioned, reproducible outputs with frontmatter metadata, satisfying requirements for governance and development handoff by ensuring clear, repeatable spec artifacts with traceable context.

What do I need to provide to start authoring an NLSpec for research-driven development?▼

To start authoring an NLSpec for research-driven development, describe your project, key actors, and expected outcomes, allowing the automated workflow to generate the structured specification.