What problem does it solve? Teams running LLM-powered applications face runaway API costs, unvalidated model upgrades, and outages when a single provider fails. This Skill governs autonomous model optimization by shadow-testing new models against production baselines while enforcing hard financial and security guardrails. ## Core Features & Use Cases - Shadow Traffic A/B Testing: Route a percentage of live traffic asynchronously to experimental models and grade them with explicit mathematical scoring criteria (accuracy, latency, hallucination penalties). - Autonomous Routing with Circuit Breakers: Rank providers by historical performance and automatically trip failovers on cost overruns, retry-limit breaches, or anomalous traffic spikes. - AI FinOps Guardrails: Enforce maximum cost per execution, retry caps, and timeouts on every external API call, with cheaper fallback providers mapped in advance. - Use Case: When a new cheaper model is released, shadow-test it on 5% of production traffic, verify it scores within tolerance of the baseline, then autonomously update router weights to cut costs by 80%. ## Quick Start Ask the agent to design a multi-provider LLM router with shadow testing, cost limits per execution, and circuit-breaker failover for your current API workload.