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moai-formats-data

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

Updated Dec 14, 2025
Search Tags:#performance#validation#LLM optimization#JSON#data formats#serialization#TOON
One-click install
npx skills add https://github.com/seo-jinseok/regulation_manager --skill moai-formats-data-seo-jinseok
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-formats-data
Source: https://github.com/seo-jinseok/regulation_manager/tree/main/.claude/skills/moai-formats-data
Command: npx skills add https://github.com/seo-jinseok/regulation_manager --skill moai-formats-data-seo-jinseok

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires orjson, PyYAML, ijson, python-dateutil, regex, jsonschema, cerberus, pydantic, typing, dataclasses, enum, re, datetime, functools, pickle, time, pathlib, fastapi, redis, 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 efficiency, ensuring high-performance serialization, and implementing robust data validation.

Core Features & Use Cases

  • Token Optimization: Reduce LLM token usage with TOON encoding.
  • High-Performance Serialization: Achieve faster data processing with optimized JSON/YAML.
  • Data Validation: Ensure data integrity with schema validation and custom rules.
  • Use Case: When sending complex data structures to an LLM, use TOON encoding to fit more information within token limits, improving context and reducing costs.

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 LLM token usage when sending complex JSON data structures?▼

You can reduce LLM token usage by applying TOON encoding to compress complex data structures. This technique fits more information within token limits, improving context and lowering costs.

What is the best way to achieve high-performance JSON serialization in Python?▼

High-performance JSON serialization is achieved using the orjson library. This approach enables rapid data processing and efficient data transmission for modern applications compared to standard methods.

How does schema validation work with Pydantic and Cerberus for data integrity?▼

Schema validation with Pydantic and Cerberus ensures data integrity by applying custom rules and schema management. These libraries validate incoming data structures against defined schemas to prevent processing errors.

Can I use FastAPI and Redis with optimized data formats for rapid data processing?▼

FastAPI and Redis integrate with optimized data formats to handle rapid data processing and efficient data transmission. This combination supports high-performance serialization and robust validation in modern applications.

Does orjson support parsing large YAML files that do not fit into memory?▼

orjson focuses on high-performance JSON serialization rather than YAML parsing. For large files, the ijson library enables iterative parsing of JSON streams, preventing memory overload during data processing.

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