Encode JSON structures into compact TOON format for LLM prompts.
npx skills add https://github.com/Consiliency/treesitter-chunker --skill toon-format
Please help me install this Agent Skill. Skill: toon-format Source: https://github.com/Consiliency/treesitter-chunker/tree/main/.ai-dev-kit/skills/toon-format Command: npx skills add https://github.com/Consiliency/treesitter-chunker --skill toon-format
TOON provides a compact, human-readable encoding for JSON data to reduce token usage and improve LLM prompt efficiency.
Encode a simple object to TOON: name: Alice, role: Engineer
High-intent search queries and answers about installing and using this skill.
TOON encodes JSON into a compact, human-readable format that reduces token consumption while preserving data structure. It's designed for structured data like tables, configurations, and nested objects in AI workflows.
TOON applies formatting conventions and tabular data rules to convert JSON-like structures into concise representations. This encoding maintains semantic meaning while minimizing tokens needed to transmit the data.
Yes. TOON handles nested objects through explicit array representations and formatting conventions. It supports tables, configurations, and complex nested structures commonly used in prompts and data pipelines.
Use TOON when passing structured data through LLM prompts where token limits matter or cost is a concern. It's particularly effective for tabular data, API responses, and configuration objects that repeat across multiple prompts.
TOON preserves structure and semantics through explicit formatting rules and validation conventions. The compact representation remains machine-friendly and human-readable while maintaining the original data relationships.
gountharFormat structured data into TOON v2.0 with configurable array types and delimiters.
GoosLabReduce JSON payloads and parse TOON-encoded data for LLM workflows.
mattjoyceConvert large JSON datasets into a compact TOON format for LLM prompts.
AGIBuildGuide .NET serialization format selection and implementation for AOT compatibility.
