speckit-learn-clarify

Scan feature specifications for ambiguities and record clarifications inline.

Updated Apr 12, 2026

Search Tags:#clarification #ambiguity #engineering #spec #process

Ready to run

npx skills add https://github.com/datamonsterr/mycoai_projects --skill speckit-learn-clarify

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: speckit-learn-clarify
Source: https://github.com/datamonsterr/mycoai_projects/tree/main/.agents/skills/speckit-learn-clarify
Command: npx skills add https://github.com/datamonsterr/mycoai_projects --skill speckit-learn-clarify

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ambiguities in active feature specifications slow down delivery and risk misaligned implementations. This Skill runs a structured ambiguity & coverage scan and records clarifications directly in the spec to align teams early.

Core Features & Use Cases

Ambiguity detection across functional scope, data model, UX flows, non-functional targets, integration points, and edge cases.
Inline clarifications stored under a dedicated "Clarifications" section in the spec, with session tagging for traceability.
Priority-driven questions: generates up to five high-impact questions to minimize downstream rework.

Quick Start

Initiate the clarifications workflow against the current spec to begin the interactive session.

Frequently Asked Questions about speckit-learn-clarify

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

FAQPage Schema

How do I clarify ambiguities in a feature specification?▼

Clarifying ambiguities in a feature specification involves running a structured scan across functional, data, UX, non-functional, integration, and edge case scopes, then recording clarifications directly in the spec for traceable decisions.

What does a structured spec ambiguity scan cover?▼

A structured spec ambiguity scan covers functional scope, data models, UX flows, non-functional targets, integration points, edge cases, and terminology to detect gaps and align teams early before implementation begins.

How do I generate high-impact questions to reduce downstream rework on specs?▼

You can generate high-impact questions to reduce downstream rework by initiating an interactive clarifications workflow that analyzes spec coverage gaps and produces up to five priority-driven questions.

Can I store clarification decisions inline within my engineering spec?▼

Yes, you can store clarification decisions inline within your engineering spec under a dedicated Clarifications section, using session tagging to maintain traceable alignment across active feature development.

Does the spec clarification process require specific frontmatter formatting?▼

Yes, the spec clarification process requires the SKILL.md file to include frontmatter with a defined name and description, and defines concrete acceptance criteria for questions to be answerable and impact-driven.