clarify

Identifies ambiguities in specs and records clarifying answers back into the file.

52|3|Updated Feb 14, 2026
One-click install
npx skills add https://github.com/ZTE-AICloud/Co-OmniSpec --skill clarify-zte-aicloud
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: clarify
Source: https://github.com/ZTE-AICloud/Co-OmniSpec/tree/main/skills/clarify
Command: npx skills add https://github.com/ZTE-AICloud/Co-OmniSpec --skill clarify-zte-aicloud

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Clarifies and reduces ambiguities in active feature specifications by prompting targeted clarifying questions and recording the answers back into the SPEC file.

Core Features & Use Cases

  • Structured clarification workflow that identifies missing decision points in specs
  • Generates up to 5 targeted questions to resolve ambiguity
  • Records answers back into the SPEC file and updates related design notes
  • Helps ensure design, implementation, and testing align with user intent

Quick Start

Prompt the AI to start a clarification session on the current spec to resolve ambiguities and update the spec accordingly.

Frequently Asked Questions about clarify

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

FAQPage Schema
How do I identify and resolve ambiguities in a software feature specification?

To resolve ambiguities in a feature specification, you can use a structured clarification workflow that identifies missing decision points, generates targeted questions, and records answers directly back into the SPEC file.

What is the best way to lock requirements before starting software design and implementation?

Locking requirements before design involves prompting concise clarifying questions to resolve missing decisions, validating JSON-like payloads, and tracking acceptance criteria to ensure design and implementation align with user intent.

How do I document decision history and persona roles during specification clarification?

Documenting decision history during specification clarification requires managing session context and tracking persona roles, which captures the rationale behind resolved ambiguities and updates the active specification accordingly.

Can I validate JSON-like payload handling within a requirements clarification workflow?

Yes, validating JSON-like payload handling is supported within the requirements clarification workflow, ensuring structured data formats are checked while resolving specification ambiguities and updating design notes.

How many targeted questions can be generated to resolve ambiguity in a spec?

The clarification workflow generates up to five targeted questions per session to resolve ambiguity, strategically identifying missing decision points to prevent downstream rework across software features.