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adiytharpansa
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user-intent-reader

Analyze conversational messages to classify intent, emotion, urgency, and preferences.

Updated Apr 11, 2026
Search Tags:#conversation#emotion#prediction#preferences#intent#user-analysis
One-click install
npx skills add https://github.com/adiytharpansa/Openclaw-backup --skill user-intent-reader
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: user-intent-reader
Source: https://github.com/adiytharpansa/Openclaw-backup/tree/main/skills/user-intent-reader
Command: npx skills add https://github.com/adiytharpansa/Openclaw-backup --skill user-intent-reader

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps identify the underlying intent, emotions, urgency, and preferences behind user messages so interactions can be more relevant and responsive.

Core Features & Use Cases

  • Intent Classification: Analyze surface requests, goals, motivations, and expected outcomes from user messages.
  • Emotion and Preference Detection: Recognize emotional signals, communication styles, detail preferences, and decision patterns.
  • Conversation Prediction: Anticipate likely follow-up questions and suggest proactive next steps during user interactions.

Quick Start

Use the user-intent-reader skill to analyze this user message for intent, emotion, urgency, and likely next needs.

Frequently Asked Questions about user-intent-reader

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

FAQPage Schema
How do I analyze user intent and implicit needs from chat messages?▼

Conversation prediction anticipates likely follow-up questions by evaluating current conversational context and user goals. It suggests proactive next steps during user interactions by inferring anticipated needs from the ongoing chat messages.

Can I detect emotion and urgency from user messages automatically?▼

Yes, emotion and urgency detection is performed by recognizing emotional signals and assessing priority within user messages. This allows interaction systems to be more responsive to the user's current emotional state and immediate requirements.

What is the best way to infer communication preferences from conversational text?▼

Inferring communication preferences involves analyzing conversational messages to identify detail preferences, communication styles, and decision patterns. This contextual analysis helps tailor subsequent responses to match the user's specific interaction style.

How do I predict likely follow-up questions during user interactions?▼

Conversation prediction anticipates likely follow-up questions by evaluating current conversational context and user goals. It suggests proactive next steps during user interactions by inferring anticipated needs from the ongoing chat messages.

Does intent classification work for chat interactions without additional dependencies?▼

Yes, intent classification works for chat interactions by analyzing surface requests, goals, and expected outcomes directly from messages. It requires no additional dependencies to perform contextual analysis on conversational text.

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