routing

Classifies inputs and dispatches them to specialized prompts, tools, or models.

Updated Apr 29, 2026
One-click install
npx skills add https://github.com/dev-khoi/AURA-conHack-2026 --skill routing-dev-khoi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: routing
Source: https://github.com/dev-khoi/AURA-conHack-2026/tree/main/.opencode/skills/routing
Command: npx skills add https://github.com/dev-khoi/AURA-conHack-2026 --skill routing-dev-khoi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Different request types often need different handling, but a single general-purpose prompt or model treats everything the same, leading to inconsistent quality, wasted cost, and poor latency control. ## Core Features & Use Cases - Input Classification: Categorize incoming requests by intent, complexity, domain, or safety level before processing. - Specialist Dispatch: Send each request class to a dedicated prompt, model, tool chain, or agent tuned for that category. - Fallback and Confidence Handling: Route low-confidence classifications to a safe default path instead of forcing a bad match. - Use Case: A support assistant receives billing questions, bug reports, and feature requests; the router classifies each message and sends it to the specialist prompt with the right tools and tone. ## Quick Start Classify this incoming user request and route it to the most appropriate specialist prompt or tool chain.

Frequently Asked Questions about routing

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

FAQPage Schema
How do I route user requests to different prompts or models?▼

Define a small set of routes with clear criteria, then use a classifier step that outputs a label plus confidence. Send each labeled request to its specialist prompt, model, or tool chain, and include a fallback path for low-confidence cases.

When should I use a routing pattern instead of one general prompt?▼

Use routing when requests fall into clear categories that need different prompts, tools, or models, and a cheap classifier can pick the path reliably. Skip it when categories overlap heavily or a generalist already performs well.

What are good criteria for classifying requests in an LLM router?▼

Common routing axes include intent type, task complexity, safety level, domain, required tool access, and response format. Choose axes where categories have low overlap so the classifier can decide confidently.

Why does my LLM router misclassify requests?▼

Misroutes usually come from overlapping route definitions, too many routes, or no fallback for ambiguous inputs. Save misroute examples, refine route criteria, and track per-route accuracy metrics to find weak spots.

What are the limitations of the routing agent pattern?▼

Routing fails when categories are fuzzy, misroute costs are high, or there are too few examples to define routes. It also adds maintenance burden since each route needs its own tested prompt and periodic review.