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moai-formats-data

Optimize data formats with TOON encoding, orjson, and schema validation.

Updated Mar 11, 2026
Search Tags:#performance#validation#LLM optimization#JSON#data formats#serialization#TOON
One-click install
npx skills add https://github.com/thkim2015/pdf-tool --skill moai-formats-data-thkim2015
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-formats-data
Source: https://github.com/thkim2015/pdf-tool/tree/main/.claude/skills/moai-formats-data
Command: npx skills add https://github.com/thkim2015/pdf-tool --skill moai-formats-data-thkim2015

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires orjson, PyYAML, ijson, jsonschema, pydantic, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines data handling by optimizing formats for LLM transmission, ensuring high-performance serialization, and validating data integrity, reducing token costs and improving application efficiency.

Core Features & Use Cases

  • Token Optimization: Reduces data size for LLM prompts using TOON encoding.
  • High-Performance Serialization: Utilizes orjson for rapid JSON processing.
  • Data Validation: Enforces schema compliance and data integrity.
  • Use Case: When sending large datasets to an LLM, use this Skill to encode the data into TOON format, significantly cutting down token usage and associated costs while ensuring the LLM receives accurate information.

Quick Start

Use the moai-formats-data skill to encode the provided user data 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?▼

You can reduce token usage by encoding JSON datasets into TOON format, which significantly cuts down token counts for LLM prompts while ensuring accurate information transmission and lowering associated costs.

What is the best way to speed up JSON serialization in Python?▼

Speed up JSON serialization by utilizing the orjson library for rapid JSON processing, which ensures high-performance data handling and efficient transmission for modern applications.

How do I validate data integrity against a schema before serialization?▼

Validate data integrity by enforcing schema compliance using jsonschema and pydantic, which ensures data structure correctness before high-performance JSON or YAML serialization occurs.

Does this data format optimization approach work with YAML files?▼

Yes, this approach works with YAML files by utilizing PyYAML for high-performance YAML serialization alongside orjson for JSON, ensuring efficient data transmission across both formats.

Can I process large JSON files incrementally to avoid high memory usage?▼

Yes, you can process large JSON files incrementally using the ijson dependency, which allows robust parsing of large data structures without loading the entire file into memory.

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

Use TOON encoding instead of standard JSON when sending large datasets to an LLM, as TOON specifically optimizes data size to reduce token costs while maintaining the required information accuracy.

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