prompt-interviewer

Interviews users to refine and complete prompts through structured analysis and iterative questioning.

6|Updated Jan 21, 2026

Search Tags:#refinement #prompt engineering #prompt optimization #llm #interview #ai assistant

Ready to run

npx skills add https://github.com/hubvue/skills --skill prompt-interviewer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-interviewer
Source: https://github.com/hubvue/skills/tree/main/context-engineering/prompt-interviewer
Command: npx skills add https://github.com/hubvue/skills --skill prompt-interviewer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps users refine their initial prompts by systematically identifying and addressing missing information, ambiguities, and unclear requirements, leading to more effective LLM interactions.

Core Features & Use Cases

Structured Analysis: Analyzes prompts based on goal clarity, context, constraints, audience, input/output, quality, and edge cases.
Iterative Questioning: Asks targeted, high-impact questions to gather necessary details.
Prompt Polishing: Finalizes prompts based on user feedback and ensures all criteria are met before execution.
Use Case: You have a prompt to generate marketing copy but it's too generic. The Prompt Interviewer will ask you about the target audience, desired tone, key selling points, and call to action to create a much more specific and effective prompt.

Quick Start

Use the prompt-interviewer skill to help refine my prompt about generating a blog post.

Frequently Asked Questions about prompt-interviewer

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

FAQPage Schema

How do I refine a prompt to get better LLM responses?▼

To refine a prompt, you must resolve goal clarity, context completeness, constraints, audience, input/output formats, and ambiguities. Structured analysis and iterative questioning identify missing details to guide your prompt to a polished state before execution.

What is the best way to optimize an LLM prompt for marketing copy?▼

Optimizing an LLM prompt for marketing copy requires specifying the target audience, desired tone, key selling points, and call to action. Iterative questioning gathers these necessary details to transform a generic request into a specific and effective prompt.

How does iterative questioning improve prompt engineering?▼

Iterative questioning improves prompt engineering by asking targeted, high-impact questions that systematically address missing information and unclear requirements. This structured analysis ensures all quality criteria and edge cases are met before finalizing the prompt.

Do I need a fully written prompt to start prompt optimization?▼

You do not need a fully written prompt to start prompt optimization. The process works by conducting structured analysis on your initial idea, asking targeted questions to gather context, constraints, and audience details to complete the prompt iteratively.

Can prompt refinement fix ambiguous requirements in my AI assistant queries?▼

Prompt refinement fixes ambiguous requirements by addressing them through structured analysis. Targeted questions resolve ambiguities surrounding input/output formats, quality criteria, and edge cases, ensuring your AI assistant receives a clear and complete instruction.

What are the limitations of using structured analysis for prompt refinement?▼

The limitation of using structured analysis for prompt refinement is that it requires active user participation to answer iterative questions. The process cannot guess missing context or constraints automatically, relying entirely on your feedback to finalize the prompt.