ClarificationAgent

Diagnose user questions by exposing logical gaps, hidden assumptions, and cognitive biases.

Updated Aug 23, 2026

Search Tags:#clarification #diagnosis #prompt-engineering #cognitive-bias #question-analysis #dialogue-design

Ready to run

npx skills add https://github.com/liushuang393/serverlessAIAgents --skill clarificationagent

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ClarificationAgent
Source: https://github.com/liushuang393/serverlessAIAgents/tree/main/skills/apps/decision_governance_engine/clarification
Command: npx skills add https://github.com/liushuang393/serverlessAIAgents --skill clarificationagent

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps teams diagnose user questions before answering by systematically exposing logical gaps, implicit assumptions, and cognitive biases.

Core Features & Use Cases

Structured question analysis: restates questions, identifies ambiguities, and surfaces hidden assumptions.
Bias and assumption awareness: detects cognitive biases and unspoken premises to improve decision quality.
Refined questioning: generates precise, answer-ready refined questions to guide subsequent interactions.
Use Case: In product support or research, a vague user query is transformed into a clear diagnostic report that guides the final answer.

Quick Start

Use the ClarificationAgent to analyze a user question and return a structured report containing restated_question, ambiguities, hidden_assumptions, cognitive_biases, refined_question, and diagnosis_confidence.

Frequently Asked Questions about ClarificationAgent

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

FAQPage Schema

How do I clarify ambiguous user questions before answering them?▼

To clarify ambiguous user questions, a diagnostic analysis restates the query, identifies logical gaps, and surfaces hidden assumptions to produce a structured clarification report before the final answer is generated.

What is question diagnosis in prompt engineering and dialogue design?▼

Question diagnosis in prompt engineering exposes cognitive biases and unspoken premises within a user query. This process transforms vague inputs into precise, answer-ready refined questions to guide subsequent interactions.

How do I detect hidden assumptions and cognitive biases in support conversations?▼

Detecting hidden assumptions and cognitive biases in support conversations requires systematically evaluating the query's logical structure. The output includes identified biases, unspoken premises, and a refined question for accuracy.

Can I use question analysis to improve research question quality?▼

Yes, you can use question analysis to improve research question quality by systematically exposing logical gaps and ambiguities. It generates a diagnostic report with a refined question and a diagnosis confidence score.

What is the best way to structure question analysis for product inquiries?▼

The best way to structure question analysis for product inquiries is outputting a diagnostic report containing the restated question, identified ambiguities, hidden assumptions, cognitive biases, and a refined question.