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Skill Details

Runtime
(claude-5-opus)
Example user message
[userMessage]: I have three LLMs available: Model A costs $1 per million input tokens and $4 per million output tokens, Model B costs $3/$12, and Model C costs $8/$30. Route customer-support requests to minimize cost, but answers must pass our existing factual and policy validation. Simple FAQ requests should not use the expensive model unless validation fails.
Example agent response
Routing Decision Selected model: Model A Routing tier: Efficient Task complexity: Simple Primary reason: Model A satisfies the stated initial-routing requirement at the lowest cost. Fallback trigger: Validation failure, unsupported output, or policy violation. Routing Policy Simple FAQ → Model A → Validation → Escalate if validation fails Model C should not be the default because its higher unit price is not justified for this workload. The optimization target is not raw token price alone. Track cost per successfully validated response, including any fallback calls. Fallback Strategy If Model A fails validation: Escalate to Model B. Revalidate the response. Use Model C only when Model B also fails or the task requires capabilities unavailable in A/B. This creates a controlled cascade instead of sending every request to the highest-cost model. Measurement Track: Model A validation-pass rate Escalation rate Cost per successful response Average latency Policy-failure rate Do not claim savings until these metrics are measured against the current baseline.
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Llm Cost Model Routing Optimization Cores

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CLAUDE-5-OPUS
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Optimize LLM costs by routing each request to the most suitable model based on task complexity, quality requirements, latency, token usage, budget, and reliability. Classify workload demands, select cost-efficient models, enforce budget constraints, handle fallbacks, and track routing outcomes. Balance cost, performance, and execution risk without assuming the cheapest model is always sufficient—creating a measurable routing strategy for production AI systems.
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Added 2 weeks ago
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