AI Token Optimization for LLM Prompts

A Claude Code skill that optimizes prompt efficiency by reducing token usage by up to 65% through specialized communication patterns and prompt engineering. It helps teams reduce API costs and improve LLM response times without sacrificing quality. Target users are AI developers, startups using LLMs, and enterprises managing high-volume AI workloads.

Token costs are a genuine pain point right now as teams scale LLM usage beyond prototypes — Anthropic, OpenAI, and Google all price by token, and a 65% reduction in a high-volume workload translates directly to a line item someone's CFO notices. LLMGuard and PromptLayer touch adjacent territory around prompt management, but no clear incumbent owns the "token compression" niche specifically. The $5k–20k/mo revenue band is plausible if you land a handful of mid-sized teams burning $10k+/mo on API costs and price as a percentage of savings, though it gets harder if buyers insist on a one-time integration fee instead of recurring. The biggest risk is commoditization: model providers are actively working on more efficient tokenization and context caching natively, which could erode the core value proposition within 12–18 months.