ai-routing

Route AI tasks to optimal provider-model pairs with governance and cost controls.

3|Updated May 28, 2026
One-click install
npx skills add https://github.com/mahg-es/araya --skill ai-routing
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ai-routing
Source: https://github.com/mahg-es/araya/tree/main/skills/ai-routing
Command: npx skills add https://github.com/mahg-es/araya --skill ai-routing

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Route tasks to the best provider and model while enforcing governance, cost controls, and explainability across a provider-agnostic AI landscape.

Core Features & Use Cases

  • Capability-based routing: verify required capabilities before assigning tasks to a model/provider.
  • Cost-aware routing: optimize for budget without sacrificing essential quality.
  • Explainable decisions: generate documented reasoning for every routing choice.
  • Use Case: route a latency-sensitive task to a fast model when it meets required capabilities, then log the rationale for auditability.

Quick Start

Route a provided task to the best provider-model pair that satisfies capabilities and governance constraints.

Frequently Asked Questions about ai-routing

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

FAQPage Schema
How do I route AI tasks to the best model while keeping costs under control?

AI task routing to optimal models enforces cost governance by verifying required capabilities and optimizing for budget constraints without sacrificing essential quality across provider-agnostic environments.

How does capability-based routing verify which provider can handle a specific task?

Capability-based routing verifies required model capabilities before assigning tasks, ensuring the selected provider-model pair satisfies latency, compliance, and functional constraints while generating documented reasoning for every choice.

Can I get explainable routing decisions for compliance and auditability?

Explainable routing decisions generate documented rationale for every task assignment, ensuring auditability and compliance by logging why a specific provider-model pair was selected based on capability and governance constraints.

What is the best way to route latency-sensitive tasks to a faster AI model?

The best way to route latency-sensitive tasks is capability-based routing, which directs tasks to fast models that meet required capabilities while enforcing cost controls and logging the routing rationale for auditability.

Does this AI routing approach work in provider-agnostic environments with multiple providers?

This AI routing approach applies to provider-agnostic environments with latency, cost, and compliance concerns, utilizing pluggable provider integration to direct tasks to the optimal available model.

Why do I need cost-aware routing when switching between different AI providers?

Cost-aware routing is needed to optimize budget allocation across providers, ensuring task assignments meet capability requirements and governance controls without overspending on unnecessary high-cost models.