speckit-specify

Convert natural-language feature descriptions into structured specification files with validation artifacts.

1|1|Updated Nov 14, 2025

Search Tags:#workflow #planning #documentation #specification #validation #nlp #feature

Ready to run

npx skills add https://github.com/erikalira/python-tts --skill speckit-specify-erikalira

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Converts informal, natural-language feature ideas into structured, production-ready specifications that teams can implement with confidence and traceability.

Core Features & Use Cases

Auto-generates a complete specification file (e.g., SPEC.md) with sections for problem, scope, user scenarios, functional requirements, acceptance criteria, and validation notes.
Performs quality checks and suggests clarifications to ensure the spec is actionable and testable.
Produces supporting artifacts like a structured checklist and traceability pointers for dependencies, extensions, and governance considerations.

Quick Start

Describe the feature you want, and Speckit will generate a complete specification ready for review.

Frequently Asked Questions about speckit-specify

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

FAQPage Schema

How do I convert a natural language feature description into a production-ready specification file?▼

To convert a natural language feature description into a production-ready specification file, you provide a prompt describing the feature. The system then generates a structured SPEC_FILE with functional requirements, acceptance criteria, and validation artifacts.

What is the best way to generate testable feature specifications from informal requirements?▼

Generating testable feature specifications from informal requirements is best achieved by applying pre-execution checks and validation outlines to the text. This process produces a branch-ready specification with traceability pointers and stakeholder-friendly language.

How does natural language processing work for creating structured software requirement specifications?▼

Natural language processing for creating structured software requirement specifications works by analyzing your input feature description and mapping it to structured sections. It outputs a searchable SPEC_FILE complete with user scenarios and quality validation checklists.

Can I use this specification workflow to generate acceptance criteria and traceability pointers for code repositories?▼

Yes, you can use this specification workflow to generate acceptance criteria and traceability pointers for code repositories. It applies the spec-kit workflow to produce branch-ready changes and structured artifacts designed specifically for repository implementation.

Does the generated specification file include validation notes and quality checks for feature planning?▼

Yes, the generated specification file includes validation notes and quality checks for feature planning. The system performs these checks automatically and suggests clarifications to ensure the final specification is actionable, testable, and ready for review.

What are the limitations of using automated specification generation for feature planning?▼

A limitation of using automated specification generation for feature planning is that it may require follow-up clarifications. While it produces structured outlines and validation artifacts, you must review the generated requirements to ensure complete accuracy for your specific context.