create-specification

Create a machine-readable specification file with YAML frontmatter and Markdown sections.

Updated Mar 18, 2026
One-click install
npx skills add https://github.com/angryconsultant/Copilot-CLI --skill create-specification-angryconsultant
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: create-specification
Source: https://github.com/angryconsultant/Copilot-CLI/tree/main/skills/create-specification
Command: npx skills add https://github.com/angryconsultant/Copilot-CLI --skill create-specification-angryconsultant

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The specification generation process is often error-prone and time-consuming; this skill creates a complete, machine-readable specification file that clearly defines requirements, constraints, interfaces, and data contracts to guide AI-driven implementation and review.

Core Features & Use Cases

  • Structured frontmatter and Markdown body to support AI consumption and machine parsing.
  • Clear guidance for organizing requirements, constraints, definitions, interfaces, and data contracts.
  • Consistent storage in the /spec/ directory with a standardized naming convention to enable reuse across projects.

Quick Start

Provide the specification's purpose to generate the spec file under /spec/ using the naming convention.

Frequently Asked Questions about create-specification

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

FAQPage Schema
How do I create AI-ready specification files from minimal requirements?

To create machine-readable specifications, provide the solution's purpose to generate a structured Markdown file with YAML frontmatter, defining requirements, interfaces, and data contracts optimized for AI consumption.

What is a machine-readable specification file for Generative AI?

A machine-readable specification file is a structured Markdown document with YAML frontmatter that defines requirements, constraints, interfaces, and data contracts to guide AI-driven implementation and automated review processes.

How do I document software interfaces and data contracts for AI consumption?

Document software interfaces and data contracts by generating a specification file under the /spec/ directory, using standardized Markdown templates and YAML frontmatter to ensure clear machine parsing and AI consumption.

Does this specification template work for infrastructure and architecture scopes?

Yes, the specification template works for schema, tool, data, infrastructure, process, design, and architecture scopes, providing structured guidance for organizing requirements, constraints, definitions, interfaces, and data contracts across software projects.

What is the best way to structure requirements and acceptance criteria for AI review?

The best way to structure requirements and acceptance criteria is using a standardized Markdown specification with YAML frontmatter, which clearly defines constraints and data contracts to guide AI-driven implementation and review.

Can I generate multiple specification files in the /spec/ directory without naming conflicts?

Yes, you can generate multiple specification files without conflicts by using the skill's standardized naming convention, which consistently stores all generated spec files under the /spec/ directory to enable reuse across projects.