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moai-formats-data

Encode Python dictionaries into TOON format for LLM token optimization.

Updated Nov 28, 2024
Search Tags:#validation#optimization#json#yaml#data formats#serialization#toon
One-click install
npx skills add https://github.com/desafin/Resource-Monitor-pyQT --skill moai-formats-data-desafin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-formats-data
Source: https://github.com/desafin/Resource-Monitor-pyQT/tree/main/.claude/skills/moai-formats-data
Command: npx skills add https://github.com/desafin/Resource-Monitor-pyQT --skill moai-formats-data-desafin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires orjson, PyYAML, ijson, python-dateutil, regex, jsonschema, cerberus, marshmallow, pydantic, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines data handling by providing specialized tools for optimizing data formats, ensuring data integrity through validation, and enabling efficient serialization and deserialization.

Core Features & Use Cases

  • TOON Encoding: Achieve significant token reduction for LLM communication.
  • High-Performance JSON/YAML: Utilize ultra-fast serialization and streaming for large datasets.
  • Robust Data Validation: Implement schema validation, type checking, and custom rules.
  • Use Case: Optimize API responses for LLM consumption by encoding data with TOON, ensuring it stays within token limits while maintaining data structure.

Quick Start

Use the moai-formats-data skill to encode the provided Python 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 JSON data to an LLM?▼

TOON encoding reduces token usage for LLM communication by optimizing data formats while maintaining structure. This ensures API responses stay within token limits during serialization.

What is the best way to parse large YAML or JSON files in Python?▼

High-performance JSON and YAML processing uses libraries like orjson and ijson to enable ultra-fast serialization and streaming for large datasets, preventing memory overload.

Can I use pydantic and marshmallow together for data validation?▼

Yes, the skill supports robust data validation using both pydantic and marshmallow, along with jsonschema and cerberus, allowing flexible schema definitions and type checking.

Does TOON encoding work with existing JSON serialization workflows?▼

TOON encoding integrates with existing workflows to optimize data transmission for LLM consumption, taking Python dictionaries and encoding them to reduce token counts efficiently.

What are the limitations of streaming JSON with ijson for large-scale data?▼

While ijson enables streaming JSON processing for large datasets, limitations depend on complex nested structures requiring careful schema validation to maintain data integrity.

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