Delegate independent tasks to specialized agents working concurrently with isolated context.
Data Type Converter
data-type-converter
Convert between data formats (JSON, CSV, XML, YAML, TOML). Handles nested structures, arrays, and preserves data types where possible.
SKILL.md
Full skill instructions
Data Type Converter
Convert data between JSON, CSV, XML, YAML, and TOML formats. Handles nested structures, arrays, and complex data with intelligent flattening options.
Quick Start
from scripts.data_converter import DataTypeConverter
# JSON to CSV
converter = DataTypeConverter()
converter.convert("data.json", "data.csv")
# YAML to JSON
converter.convert("config.yaml", "config.json")
# With options
converter.convert("data.json", "data.csv", flatten=True)
Features
- 5 Formats: JSON, CSV, XML, YAML, TOML
- Nested Data: Flatten or preserve nested structures
- Arrays: Handle array data intelligently
- Type Preservation: Maintain data types where possible
- Pretty Output: Formatted, human-readable output
- Batch Processing: Convert multiple files
API Reference
Basic Conversion
converter = DataTypeConverter()
# Auto-detect format from extension
converter.convert("input.json", "output.csv")
converter.convert("input.xml", "output.json")
converter.convert("input.yaml", "output.toml")
With Options
# Flatten nested structures for CSV
converter.convert("nested.json", "flat.csv", flatten=True)
# Pretty print output
converter.convert("data.json", "pretty.json", indent=4)
# Specify root element for XML
converter.convert("data.json", "data.xml", root="records")
Programmatic Access
# Load and convert in memory
data = converter.load("data.json")
converter.save(data, "data.yaml")
# String conversion
json_str = '{"name": "John", "age": 30}'
yaml_str = converter.convert_string(json_str, "json", "yaml")
Batch Processing
# Convert all JSON files to CSV
converter.batch_convert(
input_dir="./json_files",
output_dir="./csv_files",
output_format="csv"
)
CLI Usage
# Basic conversion
python data_converter.py --input data.json --output data.csv
# With flattening
python data_converter.py --input nested.json --output flat.csv --flatten
# Batch convert
python data_converter.py --input-dir ./json --output-dir ./csv --format csv
# Pretty print
python data_converter.py --input data.json --output pretty.json --indent 4
CLI Arguments
| Argument | Description | Default |
|---|---|---|
--input | Input file | Required |
--output | Output file | Required |
--input-dir | Input directory for batch | - |
--output-dir | Output directory | - |
--format | Output format | From extension |
--flatten | Flatten nested data | False |
--indent | Indentation spaces | 2 |
--root | XML root element | root |
Conversion Matrix
| From/To | JSON | CSV | XML | YAML | TOML |
|---|---|---|---|---|---|
| JSON | - | Yes | Yes | Yes | Yes |
| CSV | Yes | - | Yes | Yes | Yes |
| XML | Yes | Yes | - | Yes | Yes |
| YAML | Yes | Yes | Yes | - | Yes |
| TOML | Yes | Yes | Yes | Yes | - |
Examples
JSON to CSV (Flat)
converter = DataTypeConverter()
# Input: data.json
# [{"name": "John", "age": 30}, {"name": "Jane", "age": 25}]
converter.convert("data.json", "data.csv")
# Output: data.csv
# name,age
# John,30
# Jane,25
Nested JSON to Flat CSV
# Input: nested.json
# [{"user": {"name": "John", "email": "[email protected]"}, "orders": 5}]
converter.convert("nested.json", "flat.csv", flatten=True)
# Output: flat.csv
# user.name,user.email,orders
# John,[email protected],5
YAML Config to JSON
# Input: config.yaml
# database:
# host: localhost
# port: 5432
# debug: true
converter.convert("config.yaml", "config.json")
# Output: config.json
# {"database": {"host": "localhost", "port": 5432}, "debug": true}
XML to JSON
# Input: data.xml
# <users>
# <user><name>John</name><age>30</age></user>
# </users>
converter.convert("data.xml", "data.json")
# Output: data.json
# {"users": {"user": {"name": "John", "age": "30"}}}
Dependencies
pyyaml>=6.0
toml>=0.10.0
xmltodict>=0.13.0
pandas>=2.0.0
Limitations
- CSV doesn't support nested data (requires flattening)
- XML attribute handling is basic
- TOML doesn't support null values
- Very deep nesting may cause issues with some formats
- Array handling varies by format
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