Agent skill · data analytics · aiskillstore
routerbase-model-routing
Choose RouterBase model IDs and routing strategies for chat, image, video, audio, and embeddings workloads. Use when comparing providers, selecting GPT, Claude, Gemini, image, video, or audio models on RouterBase, designing fallback paths, checking pricing or availability, planning cost/latency/quality tradeoffs, querying the RouterBase Models API, or documenting how an app should route model requests through RouterBase.
Why this skill is useful
Provides specific API commands and selection heuristics for optimizing model routing that the AI wouldn't reliably generate on its own.
What it needs
Requires routerbase account access. About 2k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
RouterBase Model Routing Overview Use routerbase to pick and document model choices behind one API key and one OpenAI-compatible integration surface. This skill helps agents turn workload requirements into a practical model shortlist, fallback plan, and validation checklist. Read references/routerbase-models.md when exact catalog API calls, model examples, or selection heuristics are needed. Routing Workflow 1. Classify the task modality: chat, image, video, audio, embeddings, or mixed. 2. Identify hard constraints: quality target, latency budget, price ceiling, context length, tool calling, vision, JSON mode, region/compliance needs, and fallback tolerance. 3. Query the live RouterBase catalog when possible: 4. Shortlist one primary model and one or two fallback models. Prefer fallbacks with the same modality and similar capability shape. 5. Check current pricing before final recommendations: 6. Document the decision as a table: use case, primary model, fallback model, reason, validation test, and known caveats. Selection Heuristics For general chat, prefer a balanced fast model first, then escalate only when reasoning quality or context length requires it. For high-stakes reasoning, choose a flagship model and require human review of outputs. For latency-sensitive UX, prefer smaller or flash-tier models and keep prompts compact. For tool-heavy agents, choose chat models documented to support tool calling and test the exact tool schema. For JSON outputs, use responseformat where the selected model supports JSON mode, and add schema validation in application code. For prompt caching benefits, place stable system prompts, policies, and tool definitions before variable user content. For media workflows, keep routing separate by modality; image, video, and audio endpoints have different sync/async behavior. Fallback Design Use explicit application-level fallback logic unless the user has configured RouterBase's smart routing in their account or upstream settings. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/routerbase-model-routing