create-specification

Create machine-readable specification files with YAML frontmatter in a /spec/ directory.

4|1|Updated Feb 19, 2026

Search Tags:#templates #documentation #markdown #specification #ai #frontmatter

Ready to run

npx skills add https://github.com/billlzzz26/visual-story-extension --skill create-specification-billlzzz26

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: create-specification
Source: https://github.com/billlzzz26/visual-story-extension/tree/main/.agents/skills/creat-specifications
Command: npx skills add https://github.com/billlzzz26/visual-story-extension --skill create-specification-billlzzz26

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps teams create precise, machine-readable specifications for AI-enabled solutions, reducing ambiguity and accelerating handoff between humans and generative models.

Core Features & Use Cases

Structuring requirements, constraints, and interfaces in a single, reusable template.
Enforcing a standardized frontmatter and a /spec/ directory for easy discovery and loading by AI agents.
Supporting audits, versioning, and collaboration across product, design, and engineering teams.

Quick Start

Generate a complete specification file for the given AI project scope and save it under the /spec/ directory.

Frequently Asked Questions about create-specification

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

FAQPage Schema

How do I create machine-readable AI specification files for project documentation?▼

To create machine-readable AI specification files, use a structured markdown template with YAML frontmatter for name and description. This standardizes project requirements and constraints, reducing ambiguity and accelerating handoff between human teams and generative models.

What is the required directory structure for AI-ready project documentation?▼

The required directory structure for AI-ready project documentation uses a defined /spec/ directory. This enforces easy discovery and loading of specification files by AI agents, ensuring consistent project governance and engineering workflows.

Does YAML frontmatter need specific fields for engineering specification workflows?▼

Yes, YAML frontmatter requires specific fields including name and description for engineering specification workflows. This standardized metadata ensures the generated files are machine-readable and properly structured for automated AI agent discovery.

What is the best way to structure design governance constraints for AI agents?▼

The best way to structure design governance constraints for AI agents is applying a consistent markdown template within a /spec/ directory. This enforces standardized YAML frontmatter, ensuring precise machine-readable requirements and supporting audits and versioning across product and engineering teams.

Can I use markdown templates for versioning and collaboration across product and engineering teams?▼

Yes, you can use markdown templates for versioning and collaboration across product and engineering teams. The reusable specification template structures requirements and interfaces in a single file, supporting audits and collaboration while accelerating handoff to generative models.