token-analyzer

Token estimation for LLM API calls applies specific rules to English text, code, and JSON formats. This analysis calculates token counts to help you understand context window consumption before execution.

To reduce LLM costs in long conversations, apply history pruning and prompt compression strategies. This skill optimizes token retention for important context while lowering per-call expenses by managing conversation history.

Context window budgeting for large-scale LLM deployments involves planning token allocation across prompts and conversations. This skill helps define token limits and route requests to appropriate models to manage context windows efficiently.

Yes, you can optimize prompts for code-generation tasks to minimize token costs. This skill analyzes code prompts, estimates their token footprint, and proposes practical optimizations like compression to reduce API expenditure.

Model routing for cost optimization should be used when managing diverse prompts within strict token budgets. This skill helps determine when to route requests to alternative models to minimize costs while preserving context.