When should I use TOON instead of JSON?

You should use TOON for arrays with 5 or more similar objects, large API responses, or database results. JSON remains better for small arrays or deeply nested, non-uniform data.

How much token savings can I expect?

Most users see between 30% and 60% savings. User lists, log entries, and database rows typically see the highest efficiency gains.

Token-Oriented Object Notation (TOON) is a specialized format designed to minimize token overhead in structured data by eliminating redundant keys and formatting in uniform arrays.

How do I convert my files to TOON using this skill?

You can use the built-in commands like /toon-encode to convert a JSON file, or use /analyze-tokens to compare the size difference between formats.

TOON Data Formatter

Name: TOON Data Formatter
Author: raintree-technology

byraintree-technology

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Herramientas para Desarrolladores

Optimizes structured data for LLM processing by converting JSON and arrays into Token-Oriented Object Notation to reduce token usage by up to 60%.

Acerca de

The TOON Formatter skill empowers Claude to handle large datasets, logs, and API responses with extreme efficiency by utilizing Token-Oriented Object Notation (TOON). By stripping away repetitive JSON keys and structural overhead in uniform arrays, it significantly extends Claude's effective context window and reduces operational costs. This skill is essential for developers working with RAG pipelines, database results, or extensive log files where standard JSON formats would otherwise consume excessive tokens and limit model performance.

Características Principales

Reduces token consumption by 30-60% for structured data like logs and API responses.
Features custom delimiter support (comma, tab, pipe) to handle complex data values safely.
Supports multiple layout modes including tabular, inline, and expanded formats.
Includes a high-performance Zig-based encoder for rapid local data processing and conversion.
39 GitHub stars
Automatically detects and converts JSON arrays of 5+ items into high-efficiency TOON format.

Casos de Uso

Optimizing RAG pipelines by shrinking retrieved data payloads before they enter the context window.
Reducing overhead in tool call responses and high-volume API data exchanges during development.
Analyzing massive server logs or database query results within a single Claude conversation.

Acerca de

Características Principales

Reduces token consumption by 30-60% for structured data like logs and API responses.
Features custom delimiter support (comma, tab, pipe) to handle complex data values safely.
Supports multiple layout modes including tabular, inline, and expanded formats.
Includes a high-performance Zig-based encoder for rapid local data processing and conversion.
39 GitHub stars
Automatically detects and converts JSON arrays of 5+ items into high-efficiency TOON format.

Casos de Uso

Optimizing RAG pipelines by shrinking retrieved data payloads before they enter the context window.
Reducing overhead in tool call responses and high-volume API data exchanges during development.
Analyzing massive server logs or database query results within a single Claude conversation.