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toon-format

Convert JSON data to and from Token-Oriented Object Notation format.

Updated Mar 11, 2026
Search Tags:#llm#json#compression#token optimization#toon#data format
One-click install
npx skills add https://github.com/elcoosp/elcoosp-skills --skill toon-format-elcoosp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: toon-format
Source: https://github.com/elcoosp/elcoosp-skills/tree/main/toon-format
Command: npx skills add https://github.com/elcoosp/elcoosp-skills --skill toon-format-elcoosp

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of high token consumption when sending structured JSON data to Large Language Models (LLMs), enabling more efficient and cost-effective prompt engineering.

Core Features & Use Cases

  • Token Optimization: Significantly reduces token count for uniform arrays in JSON data.
  • Format Conversion: Seamlessly converts between JSON and the compact TOON format.
  • LLM Prompting: Ideal for embedding structured data within LLM prompts for better context and reduced cost.
  • Use Case: You need to send a list of 100 user profiles (each with id, name, role) to an LLM for analysis. Using TOON instead of JSON can cut the token count by up to 60%, making the prompt cheaper and faster to process.

Quick Start

Use the toon-format skill to convert the provided JSON data into TOON format.

Frequently Asked Questions about toon-format

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

FAQPage Schema
How do I compress JSON data to reduce LLM token usage?▼

To reduce LLM token usage, convert your JSON data into Token-Oriented Object Notation (TOON) format. TOON compresses uniform arrays into a compact representation, significantly reducing token count and prompt costs for LLM processing.

What is TOON format and how does it optimize LLM prompts?▼

TOON is a Token-Oriented Object Notation format that optimizes LLM prompts by encoding JSON into a token-efficient structure. It compresses uniform arrays to significantly reduce token count while maintaining data accuracy for LLM input.

When do I need to convert structured data to a token-efficient format for LLMs?▼

You need a token-efficient format when sending large structured JSON arrays, such as user profiles, to an LLM. Converting this data into a compact format reduces prompt size and processing costs while maintaining data accuracy for analysis.

Can I decode TOON data back to standard JSON after LLM processing?▼

Yes, you can decode TOON data back to standard JSON. The conversion process supports bidirectional translation, allowing you to encode JSON into the compact TOON format and decode it back to the original structured JSON data.

What is the best way to handle large uniform arrays in LLM prompts?▼

The best way to handle large uniform arrays in LLM prompts is to convert them into a compact TOON format. This token optimization technique reduces token count by up to 60%, making prompt engineering cheaper and faster.

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