vague

Generate structured questions and hypotheses to convert vague requests into concrete specifications.

Updated Apr 9, 2026

Search Tags:#workflow #specification #requirements #hypotheses #clarify #askuserquestion

Ready to run

npx skills add https://github.com/treejh/ai-agent-skills --skill vague-treejh

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vague
Source: https://github.com/treejh/ai-agent-skills/tree/main/plugins/clarify/skills/vague
Command: npx skills add https://github.com/treejh/ai-agent-skills --skill vague-treejh

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ambiguous requirements slow teams and derail projects. This skill automatically structures iterative clarification to transform unclear requests into precise, testable specifications.

Core Features & Use Cases

Hypothesis-driven options: Present plausible interpretations as selectable options instead of open questions.
Batch clarifications: Propose up to four related questions per interaction to minimize cognitive load.
Structured outputs: Produce a before/after summary and a decision log to document choices.
Safe prompting workflow: Always route questions through the AskUserQuestion protocol to preserve safety and clarity.

Quick Start

Initiate iterative clarification by asking up to four targeted questions to turn a vague request into a concrete specification.

Frequently Asked Questions about vague

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

FAQPage Schema

How do I clarify vague product requirements into actionable specifications?▼

To clarify vague product requirements into actionable specifications, the skill generates hypotheses and batches up to four targeted questions per interaction. It guides you through selecting plausible options to transform ambiguous requests into concrete specs.

What is the best way to handle underspecified workflow requests?▼

The best way to handle underspecified workflow requests is presenting plausible interpretations as selectable options rather than open questions. This hypothesis-driven approach minimizes cognitive load and routes clarifications safely through structured prompts.

How do I convert ambiguous user requests into testable specifications?▼

You convert ambiguous user requests into testable specifications by iteratively asking clarifying questions and documenting the choices. This process produces a before and after summary and a decision log to ensure the final requirements are precise and testable.

Can I batch clarifying questions to reduce cognitive load during requirements gathering?▼

Yes, you can batch clarifying questions to reduce cognitive load during requirements gathering. The workflow proposes up to four related questions per interaction, allowing you to efficiently resolve ambiguous requirements without overwhelming the user.

Why do ambiguous requirements derail projects and how to prevent it?▼

Ambiguous requirements derail projects by slowing teams down and causing misaligned execution. You prevent this by applying an iterative clarification process that converts vague requests into structured, decision-logged specifications before development begins.