speckit-specify

Name: speckit-specify
Availability: InStock
Author: dengnaichen

Generate structured feature specifications from natural language descriptions.

34|2|Updated Mar 16, 2026

Search Tags:#automation #workflow #documentation #specification #requirements #product-management

Ready to run

npx skills add https://github.com/DengNaichen/Stet --skill speckit-specify-dengnaichen

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: speckit-specify
Source: https://github.com/DengNaichen/Stet/tree/main/.agents/skills/speckit-specify
Command: npx skills add https://github.com/DengNaichen/Stet --skill speckit-specify-dengnaichen

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill eliminates the ambiguity and manual effort involved in drafting technical feature specifications, ensuring that requirements are clear, testable, and aligned with business goals.

Core Features & Use Cases

Automated Specification Generation: Converts natural language feature descriptions into structured, professional specification documents.
Quality Validation: Automatically generates and runs quality checklists to ensure requirements are testable and free of implementation details.
Use Case: When a product manager describes a new user authentication flow, this skill automatically creates a branch, drafts the functional requirements, defines success criteria, and validates the spec against quality standards.

Quick Start

Use the speckit-specify skill to create a new feature specification for a user authentication module.

Frequently Asked Questions about speckit-specify

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

FAQPage Schema

How do I generate structured feature specifications from natural language descriptions?▼

To generate structured feature specifications from natural language, you can use automation tools that parse text descriptions into standardized requirement documents. This approach coordinates branch creation and document generation to produce stakeholder-ready feature definitions within a spec-kit project structure.

What is the best way to automate technical requirement documentation for product management?▼

Automating technical requirement documentation involves converting natural language inputs into structured, testable specifications. By applying standardized templates and quality checklists, this process ensures that generated requirements remain free of implementation details and aligned with business goals.

Do I need a specific project structure to automate feature specification generation?▼

Yes, automating feature specification generation requires a spec-kit project structure. The automation executes shell scripts for branch management and template-based document creation, relying on this specific environment to generate and validate testable feature definitions.

How do quality validation checklists ensure feature requirements are testable?▼

Quality validation checklists ensure feature requirements are testable by automatically scanning generated specifications for ambiguity and implementation details. This automated validation step verifies that the documented functional requirements and success criteria meet predefined quality standards.

Can I automatically create a git branch when drafting a new feature specification?▼

Yes, you can automatically create a git branch when drafting a new feature specification. The specification automation process coordinates branch creation alongside requirement documentation, directly linking the generated functional requirements and success criteria to a dedicated development branch.

What are the limitations of using natural language to generate software feature specifications?▼

A limitation of using natural language for software feature specifications is the potential for initial ambiguity in the source text. While automation applies quality checklists to filter out implementation details and enforce testable requirements, the output quality heavily depends on the clarity of the original natural language input.