RouterBase Model Routing

Overview

Use routerbase to plan model selection behind one OpenAI-compatible integration surface. This skill turns workload requirements into a practical model shortlist, fallback chain, cost and latency policy, and validation checklist.

When to Use

Use this skill when the user asks for:

• Choosing RouterBase models for chat, coding, reasoning, vision, or multimodal workloads
• Designing fallback chains for provider outages or degraded quality
• Balancing cost, latency, context length, output quality, and availability
• Documenting model routing rules for production applications or agent workflows
• Creating a validation plan for model behavior before rollout

Do not use

Do not use this skill for:

• Claiming exact pricing, availability, or model performance without checking current source data
• Recommending a single model without stating assumptions and fallback behavior
• Hiding routing changes from the application owner or production operator

Instructions

1. Clarify workload type, quality threshold, latency budget, context size, expected traffic, and failure tolerance.
2. Group candidate models by capability and role: primary, economical fallback, high-quality fallback, and specialized fallback.
3. State routing assumptions clearly and mark any values that require current catalog or pricing verification.
4. Recommend small rollout steps: test prompts, golden cases, logging, retry policy, rate limit handling, and rollback criteria.
5. Keep examples OpenAI-compatible where possible so the application integration stays portable.