Does this work with nested data structures?

Yes, TOON supports key folding to flatten nested objects, though it is most effective with tabular or flat structures where fields are repeated.

Is the encoding process fast?

Yes, the skill utilizes a native Zig encoder that is up to 20x faster than standard interpretation, making it suitable for large-scale data processing.

TOON (Token-Oriented Object Notation) is a specialized format designed specifically to minimize token usage in LLMs while keeping data structured and human-readable.

How much token space can I save?

Depending on data uniformity and size, users typically see between 30% and 60% reduction in token consumption compared to standard JSON formatting.

When should I avoid using TOON?

Avoid using TOON for very small arrays (under 5 items), narrative text, or highly irregular data where the structure varies significantly between items.

TOON v2.0 Data Formatter

Name: TOON v2.0 Data Formatter
Author: Ven0m0

byVen0m0

Developer Tools

Optimizes structured data using TOON v2.0 to reduce token consumption by up to 60% while maintaining readability.

About

The TOON v2.0 Formatter is a specialized utility designed to maximize Claude's context window efficiency by aggressively compressing structured data like tables, logs, and API responses. By replacing bulky JSON with the Token-Oriented Object Notation (TOON), it preserves data integrity and readability while slashing token costs by 30-60%. It features intelligent detection of uniform data, key folding for nested structures, and a high-performance Zig-based encoder, making it an essential tool for developers handling large datasets, RAG pipelines, or complex database query results within their prompts.

Key Features

Aggressive token reduction (30-60% savings) for structured data
Support for multiple delimiters including comma, tab, and pipe
High-performance encoding via native Zig integration
0 GitHub stars
Automated data uniformity detection and optimal format selection
Advanced key folding for flattening nested object structures

Use Cases

Formatting dense system logs or transaction histories for analysis
Compressing large API responses or database query results for prompt inclusion
Optimizing RAG pipelines and agent-to-agent data transfers

About

Key Features

Aggressive token reduction (30-60% savings) for structured data
Support for multiple delimiters including comma, tab, and pipe
High-performance encoding via native Zig integration
0 GitHub stars
Automated data uniformity detection and optimal format selection
Advanced key folding for flattening nested object structures

Use Cases

Formatting dense system logs or transaction histories for analysis
Compressing large API responses or database query results for prompt inclusion
Optimizing RAG pipelines and agent-to-agent data transfers