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LLM Cost Optimizer

Audit LLM prompts and call patterns to identify token waste and cost-saving opportunities.

9|1|Updated Mar 15, 2026
Search Tags:#llm#batching#cost-optimization#prompt-caching#cost-savings#model-routing#token-reduction
One-click install
npx skills add https://github.com/Notysoty/openagentskills --skill llm-cost-optimizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: LLM Cost Optimizer
Source: https://github.com/Notysoty/openagentskills/tree/main/skills/llm-cost-optimizer
Command: npx skills add https://github.com/Notysoty/openagentskills --skill llm-cost-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Audits AI applications for unnecessary token spend and recommends prompt caching, model routing, and token reduction techniques to cut costs.

Core Features & Use Cases

  • Prompt caching, model routing, token reduction, batching, and output-length control to lower costs across Claude Code, Cline, Cursor, Codex, and other LLM workflows.
  • Real-world scenarios include reducing monthly spend for customer-support chatbots and research assistants without compromising response quality.
  • The skill provides actionable recommendations and safe defaults to implement cost-saving changes in production.

Quick Start

Provide the system prompts, estimated daily calls, and current model usage, then ask the skill to identify cost-saving opportunities.

Frequently Asked Questions about LLM Cost Optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce LLM token costs without degrading response quality?▼

You can cut LLM token costs by applying prompt caching, model routing, token reduction, and batching based on an audit of your call patterns. This skill specifies actionable configurations and guardrails to implement these cost-saving changes safely in production.

How does prompt caching work to lower AI API spend?▼

Prompt caching lowers API spend by reusing processed prompt prefixes across repeated calls to minimize token billing. This skill audits your system prompts and call patterns to identify exactly where caching configurations can be applied for maximum cost savings.

Can I use model routing to optimize costs across Claude Code, Cline, and Cursor?▼

Yes, model routing optimizes costs across Claude Code, Cline, Cursor, and Codex by directing requests to cheaper models for simpler tasks. This skill audits your current model usage to specify actionable routing configurations that reduce spend across these platforms.

What is the best way to audit AI applications for unnecessary token spend?▼

The best way to audit unnecessary token spend is to analyze system prompts, estimated daily calls, and current model usage to identify waste. This skill performs that audit and recommends prompt caching, model routing, and batching configurations to cut costs.

What do I need to provide to identify cost-saving opportunities in my LLM workflows?▼

You need to provide your system prompts, estimated daily calls, and current model usage to identify cost-saving opportunities. The skill audits this input data to output actionable configurations and safe defaults for implementing prompt caching and token reduction.

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