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clarify:vague

Converts vague requests into actionable specifications using structured, hypothesis-driven questioning and summaries.

7|6|Updated Feb 25, 2026
Search Tags:#specification#requirements#clarification#ambiguity#scope#questioning
One-click install
npx skills add https://github.com/team-attention/workshop-upstage --skill clarify-vague
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clarify:vague
Source: https://github.com/team-attention/workshop-upstage/tree/main/.claude/skills/vague
Command: npx skills add https://github.com/team-attention/workshop-upstage --skill clarify-vague

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps when a request is too vague, underspecified, or ambiguous to act on confidently. It turns unclear ideas into a concrete plan by asking structured follow-up questions and capturing the decisions made.

Core Features & Use Cases

  • Ambiguity diagnosis: Identifies what is missing, unclear, or open to interpretation in a request.
  • Hypothesis-driven clarification: Presents plausible options instead of open-ended questions to reduce user effort.
  • Requirement summary: Produces a before-and-after clarification summary with scope, constraints, and success criteria.
  • Use case: Use it when someone says “make the app better” or “build a login flow” and you need to refine the request into something implementable.

Quick Start

Use the clarify:vague skill to turn this ambiguous request into a precise specification and summarize the clarified requirements.

Frequently Asked Questions about clarify:vague

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

FAQPage Schema
How do I turn vague feature requests into actionable specifications?▼

Clarifying ambiguous requirements involves diagnosing what is missing and asking hypothesis-driven questions. It converts vague requests into actionable specifications by presenting plausible options instead of open-ended questions to reduce effort and capture decisions.

What is the best way to clarify incomplete bug reports and unclear task descriptions?▼

The best way to clarify incomplete bug reports is through iterative hypothesis-based question design. This identifies what is unclear or open to interpretation, resolving scope and constraints to produce implementation-ready details.

Can I refine underspecified requirements without writing open-ended follow-up questions?▼

Yes, you can refine underspecified requirements using hypothesis-driven clarification. Instead of open-ended questions, this approach presents plausible options to the requester, significantly reducing their effort while effectively resolving ambiguities and scope gaps.

Does requirement clarification work for requests that lack defined success criteria and scope?▼

Yes, requirement clarification works for requests lacking defined success criteria by diagnosing ambiguity and resolving missing scope. It produces a final clarified summary capturing constraints, behavior, and implementation-ready details to guide development.

What are the limitations of using structured questioning for ambiguous requirements?▼

The limitation of using structured questioning for ambiguous requirements is that it requires iterative hypothesis-based question design and decision tracking. If the requester cannot confirm plausible options, resolving scope and constraint ambiguity becomes difficult.

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