LLM Cost Optimizer
高级development最低上下文：32K
Analyzes LLM usage and reduces inference cost without sacrificing quality. Covers prompt compression, context trimming, caching (prompt and semantic), model routing by task difficulty, batching, structured output to cut retries, and token accounting. Produces a concrete plan with estimated savings and quality guardrails.
使用场景
- Cutting token spend on a high-volume LLM feature
- Introducing prompt and semantic caching
- Routing easy requests to cheaper models
- Reducing retries with structured outputs
- Building a token cost dashboard and budget alerts
示例提示词
Here is our current LLM pipeline and monthly token usage: [paste details]. Produce a cost-optimization plan that: 1. Identifies the biggest cost drivers 2. Recommends prompt/context reductions with examples 3. Proposes a caching strategy (prompt + semantic) 4. Designs a model-routing policy by task difficulty 5. Estimates savings and defines quality guardrails
推荐模型
兼容工具
claude-codekirocursorany
模态
输入: text, code
→输出: text
相关 Skills
作者
OpenModels Community