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inquire

Generate targeted clarifying questions for ambiguous user requests.

4|1|Updated Jan 24, 2026
Search Tags:#clarification#ambiguity#provenance#question-generation#inquire
One-click install
npx skills add https://github.com/synaptiai/agent-capability-standard --skill inquire
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: inquire
Source: https://github.com/synaptiai/agent-capability-standard/tree/main/skills/inquire
Command: npx skills add https://github.com/synaptiai/agent-capability-standard --skill inquire

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Ambiguity in user requests leads to ineffective or unsafe actions. This Skill generates targeted clarifying questions to resolve missing parameters, conflicting interpretations, or insufficient constraints before proceeding.

Core Features & Use Cases

  • Generate concise, bounded clarifying questions that elicit actionable answers.
  • Attach evidence anchors to the ambiguity analysis to support reasoning.
  • Integrate with downstream planning/execution steps to ensure reliable outcomes.
  • Use Case: In a chat assistant, when a user request is underspecified, ask about required fields and preferred constraints.

Quick Start

Ask targeted clarifying questions to resolve missing parameters and ambiguities before acting.

Frequently Asked Questions about inquire

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

FAQPage Schema
How do I clarify ambiguous user requests in a chat assistant?▼

To clarify ambiguous user requests, generate targeted clarifying questions that resolve missing parameters, conflicting interpretations, or unclear constraints before proceeding with any action.

When do I need to ask clarifying questions for a conversation?▼

Ask clarifying questions when required parameters are missing, interpretations conflict across a conversation, or constraints are unclear, ensuring downstream actions remain reliable and safe.

What is the best way to resolve missing parameters before executing tasks?▼

The best way to resolve missing parameters is generating concise, bounded clarifying questions that elicit actionable answers while attaching evidence anchors to support the ambiguity analysis.

How does targeted question generation handle conflicting interpretations?▼

Targeted question generation handles conflicting interpretations by analyzing the ambiguity, providing evidence anchors for reasoning, and returning a structured output with a confidence score.

Can I integrate ambiguity analysis into downstream planning steps?▼

Yes, you can integrate ambiguity analysis with downstream planning and execution steps by using the structured output of questions, evidence anchors, and confidence scores to ensure reliable outcomes.

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