clarifying-intent

Elicit intent and constraints from underspecified product requests into structured specs.

2|Updated Jan 28, 2026
One-click install
npx skills add https://github.com/chriswch/praxis --skill clarifying-intent
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: clarifying-intent
Source: https://github.com/chriswch/praxis/tree/main/skills/clarifying-intent
Command: npx skills add https://github.com/chriswch/praxis --skill clarifying-intent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Underspecified requests from stakeholders lead to misaligned scope and wasted effort; this skill guides teams to a shared understanding by eliciting intent, constraints, unknowns, risks, and success criteria to produce precise artifacts.

Core Features & Use Cases

  • Elicit targeted clarifying questions to extract goals, constraints, risks, and acceptance criteria.
  • Generate a Feature Brief when the input is large, or a Story-Level Behavioral Spec for smaller work.
  • Provide structured handoffs to downstream workflows (e.g., slicing-stories) with clear acceptance criteria and scope.
  • Use templates and conventions from repository references to ensure consistency across teams.

Quick Start

Provide a vague requirement and the skill will ask targeted questions and generate a Feature Brief or Story-Level Behavioral Spec.

Frequently Asked Questions about clarifying-intent

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

FAQPage Schema
How do I turn vague product requests into actionable specs with acceptance criteria?

To turn vague product requests into actionable specs, you provide the raw requirement to elicit intent, constraints, and risks, generating a structured Story-Level Behavioral Spec with Given/When/Then acceptance criteria for shared understanding.

What is the best way to clarify underspecified requirements for large feature ideas?

The best way to clarify underspecified requirements for large features is to triage the input and generate a Feature Brief, which captures goals, unknowns, and constraints to hand off to downstream slicing-stories workflows.

How do I write a story spec that reduces ambiguity for development teams?

Writing a story spec that reduces ambiguity involves eliciting success criteria and constraints from underspecified tasks, directly producing a Story-Level Behavioral Spec with structured Given/When/Then acceptance criteria and handoff notes.

Can I use this approach for both large feature briefs and single tasks?

Yes, you can use this approach for both large feature briefs and single tasks, as it triages input scale to output either a Feature Brief for slicing-stories or a direct Story-Level Behavioral Spec with acceptance criteria.

When should I generate a Feature Brief instead of a Story-Level Behavioral Spec?

You should generate a Feature Brief instead of a Story-Level Behavioral Spec when the input is a large feature idea requiring slicing, whereas single smaller tasks directly produce a Story-Level Behavioral Spec with Given/When/Then criteria.