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ModelRouterSkill

Routes LLM inference requests to task-appropriate wrapper skills using heuristic analysis.

Updated Feb 25, 2026
Search Tags:#ai#llm#dispatch#router#model selection#task routing
One-click install
npx skills add https://github.com/PixnBits/SeedClaw --skill modelrouterskill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ModelRouterSkill
Source: https://github.com/PixnBits/SeedClaw/tree/main/src/skills/models/model-router
Command: npx skills add https://github.com/PixnBits/SeedClaw --skill modelrouterskill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill intelligently selects the most appropriate LLM wrapper for incoming requests, ensuring efficient and safe AI model utilization.

Core Features & Use Cases

  • Dynamic Model Selection: Analyzes task type and content to choose the best LLM (e.g., coder, general reasoning).
  • Heuristic-Based Routing: Employs hard-coded rules to prioritize specialized models for specific tasks.
  • Safety Enforcement: Never routes to wrappers with broad network policies and flags high-risk tasks.
  • Use Case: When a request to "generate a Python script for data analysis" arrives, ModelRouterSkill identifies it as a code task and routes it to a coder-optimized LLM like qwen2.5-coder:32b.

Quick Start

Route the following LLM request to the most suitable model.

Frequently Asked Questions about ModelRouterSkill

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

FAQPage Schema
How does intelligent LLM routing work for task execution?▼

Intelligent LLM routing works by analyzing incoming inference requests and applying hard-coded safety and performance heuristics to dispatch tasks to the most appropriate LLM wrapper. It prioritizes coder models for code tasks and general reasoning models for analytical tasks.

Can I route code generation requests to a specialized coder model?▼

Yes, you can route code generation requests to a specialized coder model. The router analyzes the task content, identifies it as code-related, and dispatches the inference request to a coder-optimized LLM wrapper, ensuring optimal task execution.

How do I ensure safety when dispatching AI inference requests?▼

To ensure safety when dispatching AI inference requests, the router never calls LLMs directly and avoids wrappers with broad network policies. It flags high-risk tasks and ensures all routing decisions are auditable via metadata.

What is the best way to select an LLM for analytical tasks?▼

The best way to select an LLM for analytical tasks is to use heuristic-based routing. The router evaluates the task type and dynamically selects a general reasoning model wrapper, optimizing both performance and safety for analytical execution.

Do I need a direct LLM connection to use model selection heuristics?▼

No, you do not need a direct LLM connection to use model selection heuristics. The router operates independently and never calls LLMs directly, relying entirely on metadata and task content analysis to route requests to existing LLM wrappers.

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