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moai-formats-data

Encode data into TOON format for LLM token reduction.

Updated Mar 14, 2026
Search Tags:#validation#LLM optimization#JSON#YAML#data formats#serialization#TOON
One-click install
npx skills add https://github.com/jinwoonghong/MESAsim --skill moai-formats-data-jinwoonghong
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-formats-data
Source: https://github.com/jinwoonghong/MESAsim/tree/main/.claude/skills/moai-formats-data
Command: npx skills add https://github.com/jinwoonghong/MESAsim --skill moai-formats-data-jinwoonghong

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires orjson, PyYAML, ijson, python-dateutil, regex, jsonschema, cerberus, marshmallow, pydantic, functools, hashlib, pickle, typing, dataclasses, enum, re, datetime, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines data handling by optimizing formats for LLM transmission, enabling high-performance serialization, and ensuring data integrity through robust validation.

Core Features & Use Cases

  • TOON Encoding: Achieve 40-60% token reduction for LLM communication.
  • High-Performance Serialization: Utilize ultra-fast JSON/YAML processing.
  • Data Validation: Implement schema validation and error handling.
  • Use Case: When sending large datasets to an LLM, use TOON encoding to fit more information within token limits, reducing costs and improving context.

Quick Start

Use the moai-formats-data skill to encode the provided dictionary into TOON format.

Frequently Asked Questions about moai-formats-data

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

FAQPage Schema
How do I reduce token usage when sending large JSON datasets to an LLM?▼

TOON encoding reduces LLM token consumption by 40-60% compared to standard JSON. It transforms dictionary structures into an optimized format, allowing you to fit more information within context limits.

What is the best way to validate YAML schema integrity in Python?▼

YAML schema integrity is validated using libraries like jsonschema, cerberus, and marshmallow. This approach ensures data structure compliance and handles errors comprehensively during high-performance serialization.

Does this data serialization approach work with pydantic models?▼

Yes, pydantic is supported alongside dataclasses and typing for data validation. It integrates with ultra-fast JSON and YAML processing to ensure schema integrity across modern applications.

How do I convert JSON to TOON format for LLM optimization?▼

You can encode dictionaries into TOON format using the moai-formats-data skill. This conversion optimizes data transmission for LLMs, reducing costs and improving context by minimizing token count.

Can I use ijson for high-performance JSON serialization with large files?▼

Yes, ijson is included for high-performance JSON serialization. It supports advanced patterns for efficient data transmission, enabling ultra-fast processing for large datasets.

When should I use TOON encoding instead of standard JSON?▼

Use TOON encoding when sending large datasets to an LLM to overcome token limits. Standard JSON remains suitable for general high-performance serialization where token reduction is not required.

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