OpenRouter Context Optimization

Overview

OpenRouter models have varying context windows (4K to 1M+ tokens). Since pricing is per-token, stuffing unnecessary context wastes money and can degrade output quality. This skill covers context window lookup, token estimation, conversation trimming, chunking strategies, and Anthropic prompt caching for large contexts.

Prerequisites

• An OpenRouter API key (sk-or-v1-...) exported as OPENROUTER_API_KEY — see the openrouter-install-auth skill for setup
• Python 3.8+ with the OpenAI SDK and requests for model-metadata lookup; tiktoken for exact token counting per the references
• curl and jq to query context windows and pricing from /api/v1/models
• Node.js 18+ if you use the TypeScript context-budget calculator in the references

Instructions