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speckit-clarify

Identify underspecified areas in feature specs and encode answers.

Updated Jul 6, 2026
Search Tags:#feature development#clarification#feature specification#specification analysis#question-answering
One-click install
npx skills add https://github.com/Tzuhuanhsu/AI_MCP_Slot_Demo --skill speckit-clarify-tzuhuanhsu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: speckit-clarify
Source: https://github.com/Tzuhuanhsu/AI_MCP_Slot_Demo/tree/main/.claude/skills/speckit-clarify
Command: npx skills add https://github.com/Tzuhuanhsu/AI_MCP_Slot_Demo --skill speckit-clarify-tzuhuanhsu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps to identify and address underspecified areas in feature specifications by posing targeted clarification questions and encoding the answers back into the spec.

Core Features & Use Cases

  • Clarification Questions: Ask up to 5 highly targeted questions to pinpoint underspecified areas.
  • Answer Encoding: Encode the answers back into the spec file for reference.
  • Use Case: When reviewing a feature spec, this Skill can help clarify ambiguities and missing decision points, reducing the risk of rework and misalignment.

Quick Start

Use the speckit-clarify skill to generate clarification questions for the current feature spec.

Frequently Asked Questions about speckit-clarify

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

FAQPage Schema
How do I clarify ambiguities in a feature specification?▼

This Skill clarifies feature specifications by asking up to 5 targeted questions to pinpoint underspecified areas. It encodes your answers directly back into the spec file to reduce rework and misalignment.

What is the best way to identify missing decision points in a feature spec?▼

The best way to identify missing decision points in a feature spec is by generating targeted clarification questions. This approach highlights ambiguities and encodes the resolved answers back into the document for reference.

How do I encode clarification answers into a YAML or Markdown spec file?▼

You encode clarification answers into a YAML or Markdown spec file by running an automated question-answering process. It updates the existing feature specification with the resolved decisions directly within the project structure.

Does feature specification analysis work within a spec-kit directory structure?▼

Feature specification analysis works within a project using a spec-kit structure and a .specify/ directory. It operates by reading the existing YAML and Markdown formats to generate targeted clarification questions.

What are the limitations of automated question generation for feature specs?▼

A limitation of automated question generation is that it asks a maximum of 5 highly targeted questions per session. This constrains the scope of ambiguities addressed, requiring multiple runs for highly underspecified feature specifications.

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