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Data format specialist covering TOON encoding, JSON/YAML optimization, serialization patterns, and data validation. Use when optimizing data for LLM transmission or implementing serialization.
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基于 SOC 职业分类
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| name | moai-formats-data |
| description | Data format specialist covering TOON encoding, JSON/YAML optimization, serialization patterns, and data validation. Use when optimizing data for LLM transmission or implementing serialization. |
| license | Apache-2.0 |
| compatibility | Designed for Claude Code |
| allowed-tools | Read, Write, Edit, Grep, Glob, mcp__context7__resolve-library-id, mcp__context7__get-library-docs |
| user-invocable | false |
| metadata | {"version":"2.0.0","category":"library","status":"active","updated":"2026-01-11","modularized":"true","tags":"formats, data, toon, serialization, validation, optimization","author":"MoAI-ADK Team"} |
| triggers | {"keywords":["serialization","data format","json","yaml","toon","validation","schema","optimization"]} |
Advanced Data Format Management - Comprehensive data handling covering TOON encoding, JSON/YAML optimization, serialization patterns, and data validation for performance-critical applications.
Core Capabilities:
When to Use:
Quick Start:
Create a TOONEncoder instance and call encode with a dictionary containing user and age fields to compress the data. The encoded result achieves 40-60% token reduction. Call decode to restore the original data structure.
Create a JSONOptimizer instance and call serialize_fast with a large dataset to achieve ultra-fast JSON processing.
Create a DataValidator instance and call create_schema with a dictionary defining name as a required string type. Call validate with the data and schema to check validity.
TOON (Token-Optimized Object Notation):
Performance Optimization:
Data Validation:
TOON Encoding for LLM Optimization:
Create a TOONEncoder instance. Define data with user object containing id, name, active boolean, and created datetime, plus permissions array. Call encode to compress and decode to restore. Compare sizes to verify reduction.
Fast JSON Processing:
Create a JSONOptimizer instance. Call serialize_fast to get bytes and deserialize_fast to parse. Use compress_schema with a type object and properties definition to optimize repeated validation.
Data Validation:
Create a DataValidator instance. Define user_schema with username requiring string type, minimum length 3, email requiring email type, and age as optional integer with minimum value 13. Call validate with user_data and schema, then check result for valid status, sanitized_data, or errors list.
API Response Optimization:
Create a function to optimize API responses for LLM consumption by encoding data with TOONEncoder. Create a corresponding function to parse optimized responses by decoding TOON data back to dictionary.
Configuration Management:
Create a YAMLOptimizer instance and call load_fast with a config file path. Call merge_configs with base_config, env_config, and user_config for multi-file merging.
Large Dataset Processing:
Create a StreamProcessor with chunk_size of 8192. Define a process_item function that handles each item. Call process_json_stream with the file path and callback to process large JSON files without loading into memory.
See modules/toon-encoding.md for custom type handlers (UUID, Decimal), streaming TOON processing, batch TOON encoding, and performance characteristics with benchmarks.
See modules/data-validation.md for cross-field validation, schema evolution and migration, custom validation rules, and batch validation optimization.
See modules/caching-performance.md for intelligent caching strategies, cache warming and invalidation, memory management, and performance monitoring.
See modules/json-optimization.md for streaming JSON processing, memory-efficient parsing, schema compression, and format conversion utilities.
Core Implementation Modules:
Supporting Files:
Core Libraries:
Performance Tools:
Validation Libraries:
For working code examples, see examples.md.
Status: Production Ready Last Updated: 2026-01-11 Maintained by: MoAI-ADK Data Team
| Rationalization | Reality |
|---|---|
| "JSON is universal, I do not need to optimize the format" | JSON verbosity wastes tokens in LLM contexts. TOON encoding reduces token usage by 30-50% for structured data. |
| "YAML is more readable than JSON for configuration" | YAML readability comes with parsing ambiguity (Norway problem, indentation errors). Use JSON for machine-read config, YAML for human-edited. |
| "Schema validation is unnecessary for internal data" | Internal data formats drift silently. Schema validation catches format changes before they cause runtime errors. |
| "I will handle serialization edge cases as they appear" | Date formats, null handling, and unicode escaping are known edge cases. Handle them in the serializer, not in each consumer. |
| "The data payload is small, optimization is unnecessary" | Small payloads multiply by request count. Optimizing a 1KB payload saves 1MB per 1000 requests. |
