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AMGrobelnik
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json

Validate JSON files against predefined schemas and generate full, mini, and preview outputs.

1|Updated Feb 27, 2026
Search Tags:#validation#schema#formatting#json#pipeline#data processing
One-click install
npx skills add https://github.com/AMGrobelnik/ai-inventor-old3 --skill json-amgrobelnik
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: json
Source: https://github.com/AMGrobelnik/ai-inventor-old3/tree/main/.claude/skills/aii_json
Command: npx skills add https://github.com/AMGrobelnik/ai-inventor-old3 --skill json-amgrobelnik

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jsonschema, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of working with JSON data by providing robust validation against predefined schemas and generating optimized versions for various use cases.

Core Features & Use Cases

  • JSON Schema Validation: Ensure your JSON files conform to expected structures for experiment pipelines, hypothesis selection, and solution generation.
  • JSON Formatting: Create 'full', 'mini' (first 3 items), and 'preview' (truncated strings) versions of JSON datasets for efficient development and inspection.
  • Use Case: Validate the output of your experiment pipeline to ensure it matches the exp_eval_sol_out.json schema, or generate a quick preview of a large JSON dataset to inspect its contents without loading the entire file.

Quick Start

Validate the JSON file /path/to/eval_out.json against the exp_eval_sol_out schema.

Frequently Asked Questions about json

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate JSON files against a schema for data pipelines?▼

JSON schema validation checks your files against predefined structures using the `jsonschema` library. This Skill validates experiment pipeline outputs to ensure data integrity and conformance to expected formats like `exp_eval_sol_out.json`.

What is the best way to format large JSON datasets for quick inspection?▼

The best way to format large JSON datasets is by generating optimized versions. This Skill creates 'mini' outputs with the first 3 items and 'preview' outputs with truncated strings for quick inspection without loading the entire file.

Do I need the jsonschema package to validate JSON data structures?▼

Yes, the `jsonschema` package is required. This Skill utilizes the `jsonschema` dependency to validate JSON files against predefined schemas and ensure data integrity for experiment pipelines.

Can I generate a preview of a large JSON file without loading the entire dataset?▼

Yes, you can generate a preview of large JSON files. The Skill creates a 'preview' output with truncated strings and a 'mini' version containing the first 3 items, enabling efficient inspection without loading the entire dataset.

What are the limitations of using custom Python scripts for JSON formatting?▼

Using custom Python scripts for JSON formatting limits outputs to full, mini, and preview versions. The Skill relies on predefined schemas and the `jsonschema` library for validation, so it does not dynamically generate schemas.

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