token-usage-optimization

Estimate and optimize LLM token usage, cost, and latency for prompts and workflows.

3|Updated Jun 23, 2022

Search Tags:#optimization #llm #latency #tokenization #token #cost

Ready to run

npx skills add https://github.com/fbosch/dotfiles --skill token-usage-optimization

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: token-usage-optimization
Source: https://github.com/fbosch/dotfiles/tree/main/.agents/skills/token-usage-optimization
Command: npx skills add https://github.com/fbosch/dotfiles --skill token-usage-optimization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tokenx, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps teams estimate and reduce token usage, cost, and latency for prompts, agents, and tool workflows, enabling faster iterations and more predictable performance.

Core Features & Use Cases

Token estimation for prompt components such as system, developer, tool lists, history, retrieval, and user input.
An optimization playbook that trims inputs, reduces retrieval size, enables prompt caching, and enforces output length caps.
Suitable for budgeting, model selection, and troubleshooting context length issues across AI tasks, agents, and tool-based conversations.

Quick Start

Provide your target prompt and budget, then run the estimator to obtain token counts and a prioritized optimization plan.

Frequently Asked Questions about token-usage-optimization

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

FAQPage Schema

How do I estimate LLM token usage and cost for complex agent workflows?▼

You can estimate LLM token usage and cost by sizing individual prompt components like system instructions, tool lists, history, and retrieval inputs. This approach breaks down context length to forecast budgets and identify specific areas for reduction.

What's the best way to reduce LLM latency and token cost for prompts?▼

The best way to reduce LLM latency and token cost is applying an optimization playbook that trims inputs, reduces retrieval size, enables prompt caching, and enforces output length caps. This prioritizes high-impact reductions across your AI tasks.

Can I use token optimization heuristics for model selection and budgeting?▼

Yes, token optimization heuristics support model selection and budgeting by forecasting token counts and latency across various models. You can compare estimated costs against your target budget to select cheaper models for your tool workflows.

Does token estimation work with retrieval contexts and tool-based conversations?▼

Token estimation works with retrieval contexts and tool-based conversations by sizing each component individually. It measures tokens for tool lists, conversation history, and retrieved documents to troubleshoot context length issues and optimize agent performance.

Why does my LLM prompt exceed the context length limit during agent tasks?▼

Your LLM prompt likely exceeds context length limits due to accumulated tokens from system instructions, tool definitions, history, and retrieval inputs. Per-component token sizing identifies which specific inputs are consuming the most context.

Do I need the tokenx dependency to run token usage optimization?▼

Yes, you need the tokenx dependency to run token usage optimization as it provides the foundational scripts and references for estimating token counts and applying the optimization playbook to your AI tasks.