interview-me

Extract user intent through a one-question-at-a-time interview loop.

6|Updated Feb 17, 2026

Search Tags:#requirements #clarification #interview #ai-assistant #intent #conversational-design

Ready to run

npx skills add https://github.com/farzanmrz/oparax-chirp --skill interview-me-farzanmrz

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: interview-me
Source: https://github.com/farzanmrz/oparax-chirp/tree/main/.claude/skills/interview-me
Command: npx skills add https://github.com/farzanmrz/oparax-chirp --skill interview-me-farzanmrz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Extracts true user goals and requirements by conducting a one-question-at-a-time interview until high confidence is reached, preventing misinterpretation of underspecified asks.

Core Features & Use Cases

Iterative questioning: probes missing details with a focused hypothesis, updating confidence as you go.
One-question-at-a-time: avoids overwhelm and surface-hidden constraints.
Guardrails for clarity: ensures explicit user intent before moving to planning or execution.

Quick Start

Start by asking the user one focused question to surface the true intent before proposing any plan.

Frequently Asked Questions about interview-me

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

FAQPage Schema

How do I clarify ambiguous user intent through an AI conversational flow?▼

To clarify ambiguous user intent, apply a one-question-at-a-time interview loop that probes missing details with focused hypotheses. This disciplined conversational design prevents misinterpretation of underspecified requests by surfacing hidden constraints iteratively until high confidence is reached.

What is hypothesis-driven questioning in conversational design?▼

Hypothesis-driven questioning in conversational design is an iterative mechanism that probes missing details with a focused hypothesis, updating a confidence score as the user responds. It enforces one-question-at-a-time interactions to avoid overwhelming the user and accurately extract true requirements.

How do I extract true user requirements when requests are underspecified?▼

Extract true user requirements by triggering an interview flow that enforces one-question-at-a-time interactions. This method tracks a confidence score through iterative questioning, requiring an explicit yes from the user to finalize intent before moving to any planning or execution.

When should I use a one-question-at-a-time interview loop?▼

Use a one-question-at-a-time interview loop when user requests are underspecified or ambiguous, or when explicitly triggered. This approach avoids overwhelming the user, surfaces hidden constraints, and systematically tracks confidence until reaching approximately 95% certainty before proceeding.

Does this intent clarification approach require an explicit confirmation to finalize requirements?▼

Yes, this intent clarification approach requires an explicit yes to finalize requirements. The conversational design enforces guardrails for clarity by ensuring explicit user confirmation before moving to planning or execution, preventing misinterpretation of underspecified asks.

Can I use conversational design guardrails to prevent misinterpretation of underspecified asks?▼

Yes, you can use conversational design guardrails to prevent misinterpretation by enforcing one-question-at-a-time interactions and requiring explicit confirmation. These guardrails ensure true user goals are extracted through hypothesis-driven questioning before any planning or execution begins.