intent-router

What This Skill Does

The intent-router skill provides a robust NLU (Natural Language Understanding) engine designed to categorize unstructured text into predefined labels. Powered by the x402 architecture, it offers high-precision intent classification with built-in confidence scoring and entity extraction. This skill serves as the brain for intelligent routing systems, allowing OpenClaw agents to interpret user input and trigger appropriate workflows based on clearly identified intents. Whether you are building a customer service bot, a complex multi-agent orchestration layer, or a simple text categorization pipeline, intent-router ensures that inputs are mapped to the correct business logic with transparency.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/daisuke134/intent-router

Before running, ensure you have the required dependency installed globally:

npm install -g [email protected] awal auth login

This setup allows the skill to authenticate with the necessary infrastructure for seamless processing.

Use Cases

• Customer Support Automation: Automatically route tickets to 'complaint', 'billing', or 'technical_support' queues based on user tone and keywords.
• Multi-Agent Orchestration: Act as a gatekeeper that dispatches complex tasks to specialized agents (e.g., 'data-analysis' vs 'browser-automation').
• CRM Data Tagging: Automatically extract entities like dates, locations, and names from incoming emails to populate customer records.
• Content Filtering: Categorize incoming content streams into buckets for internal moderation or prioritization.

Example Prompts

1. "Classify this: 'My account was hacked and I need to reset my password immediately' using these intents: [security_alert, password_reset, feedback]"
2. "Extract location and date entities from 'I am planning a trip to London for next December' and categorize the intent."
3. "Route the following email to the correct department: 'I am unhappy with the service I received at the branch today' using these labels: [complaint, praise, general_inquiry]"

Tips & Limitations

To get the most out of the intent-router, ensure your intent list contains between 2 and 20 items for optimal classification accuracy. While the engine is highly performant, providing a 'context' string significantly improves entity extraction quality, especially with ambiguous pronouns or industry-specific jargon. Note that text input is capped at 2000 characters; for longer documents, consider summarizing before routing. Since the model relies on language hints, providing the correct 'language' parameter improves performance in multilingual environments. Keep in mind that confidence scores below 0.5 should ideally be routed to a human supervisor for verification.