Stream JSONL, JSON, CSV, and Pickle data with automatic format detection.
npx skills add https://github.com/anhvth/speedy_utils --skill io-utilities
Please help me install this Agent Skill. Skill: io-utilities Source: https://github.com/anhvth/speedy_utils/tree/main/.github/skills/io-utilities Command: npx skills add https://github.com/anhvth/speedy_utils --skill io-utilities
Efficiently handling IO tasks across formats (JSON, JSONL, CSV, TXT) and providing streaming, loading, and serialization utilities to reduce boilerplate and errors.
Stream a large compressed JSONL file with fast_load_jsonl to demonstrate memory-efficient processing.
High-intent search queries and answers about installing and using this skill.
Stream large JSONL files with low memory usage using fast_load_jsonl to process records sequentially. This skill handles automatic decompression, enabling memory-efficient processing of large compressed datasets directly from disk or network sources.
The best way to load multiple file formats automatically is using universal loading utilities that auto-detect formats by file extension. This skill natively supports JSON, JSONL, CSV, and TXT files, reducing boilerplate code for multi-format data sources.
Yes, you can serialize Pydantic models to JSON and Pickle safely using built-in serialization helpers. These utilities manage object serialization while automatically creating parent directories as needed to prevent file-not-found errors.
Yes, this IO utility handles automatic decompression for compressed JSONL streams. It streamlines data I/O tasks by detecting file extensions and applying fast JSONL streaming to minimize the memory footprint during extraction.
A limitation of streaming JSONL for multi-format data loading is that auto-detection relies strictly on file extensions for JSON, CSV, and Pickle. Files without recognized extensions or those requiring non-streaming access patterns may need manual format specification.
You need a specialized IO utility for JSONL streaming and serialization to eliminate boilerplate code and prevent common errors. It provides fast, flexible data handling with automatic directory creation and broad format support across diverse workflows.
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kimrejstromDefine and validate Pydantic v2 models, configs, and event types in Python projects.
aws-samplesProcess and analyze CSV, JSON, and text data files with automated cleaning and transformation.
