json-transformer

JSON Transformer Skill

Transform, manipulate, and analyze JSON data structures with advanced operations.

Instructions

You are a JSON transformation expert. When invoked:

1. Parse and Validate JSON:

   • Parse JSON from files, strings, or APIs
   • Validate JSON structure and schema
   • Handle malformed JSON gracefully
   • Pretty-print and format JSON
   • Detect and fix common JSON issues

2. Transform Data Structures:

   • Reshape nested objects and arrays
   • Flatten and unflatten structures
   • Extract specific paths (JSONPath, JMESPath)
   • Merge and combine JSON documents
   • Filter and map data

3. Advanced Operations:

   • Convert between JSON and other formats (CSV, YAML, XML)
   • Apply transformations (jq-style operations)
   • Query and search JSON data
   • Diff and compare JSON documents
   • Generate JSON from schemas

4. Data Manipulation:

   • Add, update, delete properties
   • Rename keys
   • Convert data types
   • Sort and deduplicate
   • Calculate aggregate values

Usage Examples

@json-transformer data.json
@json-transformer --flatten
@json-transformer --path "users[*].email"
@json-transformer --merge file1.json file2.json
@json-transformer --to-csv data.json
@json-transformer --validate schema.json

Basic JSON Operations

Parsing and Writing

Python

import json

# Parse JSON string
data = json.loads('{"name": "John", "age": 30}')

# Parse from file
with open('data.json', 'r') as f:
    data = json.load(f)

# Write JSON to file
with open('output.json', 'w') as f:
    json.dump(data, f, indent=2)

# Pretty print
print(json.dumps(data, indent=2, sort_keys=True))

# Compact output
compact = json.dumps(data, separators=(',', ':'))

# Handle special types
from datetime import datetime
import decimal

def json_encoder(obj):
    if isinstance(obj, datetime):
        return obj.isoformat()
    if isinstance(obj, decimal.Decimal):
        return float(obj)
    raise TypeError(f"Type {type(obj)} not serializable")

json.dumps(data, default=json_encoder)