OpenRouter Performance Tuning
Optimizes OpenRouter request latency and throughput for high-performance AI applications.
This skill provides a comprehensive suite of tools and patterns to minimize latency and maximize throughput when using the OpenRouter API with Claude Code. It empowers developers to implement automated benchmarking, low-latency streaming for reduced Time-To-First-Token (TTFT), and advanced asyncio-based parallel request processing. By leveraging model speed tiers, connection pooling, and optimized provider routing, this skill ensures your AI integrations are responsive enough for real-time production environments.
Key Features
01Automated latency benchmarking (p50/p95) across multiple models
02Streaming implementations for immediate UI feedback and lower TTFT
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04Asyncio-based parallel request processing with semaphore concurrency control
05Strategic provider routing and :nitro variant selection guidance
06Connection pooling and keep-alive optimization to eliminate handshake overhead
Use Cases
01Scaling high-throughput data processing tasks with concurrent model requests
02Accelerating user-facing chat interfaces by reducing perceived response time
03Benchmarking and selecting the fastest model/provider combination for specific regions