Data Toolkit

Complete data processing utilities for OpenClaw agents.

Features

Converters

• JSON ↔ CSV - Bidirectional conversion with schema inference
• JSON ↔ YAML - Clean formatting, comment preservation
• JSON ↔ XML - Configurable root elements and attributes
• CSV ↔ YAML - Direct conversion without intermediate steps
• Multi-format batch conversion - Process entire directories

Validators

• JSON Schema validation - Validate against JSON Schema specs
• CSV structure validation - Check headers, columns, data types
• Data type inference - Automatic type detection and validation
• Custom rules - Define business logic validations

Cleaners

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• Duplicate removal - Smart deduplication with configurable keys
• Null/empty handling - Remove or replace null values
• Data normalization - Standardize formats (dates, numbers, strings)
• Whitespace cleanup - Trim, collapse multiple spaces
• Column operations - Remove, rename, reorder columns

Get Data Toolkit

🛒 Gumroad (€10): https://nexusatlas.gumroad.com/l/bsyacx
📦 ClawHub: https://clawhub.ai/skills/data-toolkit

MIT License — Python 3.8+, zero dependencies.

Usage

Convert Data

# JSON to CSV
./src/convert.py --input data.json --output data.csv --format csv

# CSV to JSON
./src/convert.py --input data.csv --output data.json --format json

# JSON to YAML
./src/convert.py --input data.json --output data.yaml --format yaml

# XML to JSON
./src/convert.py --input data.xml --output data.json --format json

# Batch conversion
./src/convert.py --input-dir ./raw --output-dir ./processed --format json

Validate Data

# Validate against JSON schema
./src/validate.py --input data.json --schema schema.json

# Validate CSV structure
./src/validate.py --input data.csv --check-headers --check-types

# Custom validation rules
./src/validate.py --input data.json --rules validation-rules.yaml

Clean Data

# Remove duplicates
./src/clean.py --input data.json --dedupe --key id

# Handle nulls
./src/clean.py --input data.csv --remove-nulls
./src/clean.py --input data.csv --replace-nulls "N/A"

# Normalize data
./src/clean.py --input data.json --normalize dates,numbers,strings

# Full cleanup pipeline
./src/clean.py --input messy.csv --dedupe --remove-nulls --normalize all --output clean.csv

API Usage (Python)

from data_toolkit import convert, validate, clean

# Convert
convert.json_to_csv('input.json', 'output.csv')
convert.csv_to_yaml('input.csv', 'output.yaml')

# Validate
is_valid = validate.json_schema('data.json', 'schema.json')
errors = validate.csv_structure('data.csv')

# Clean
clean.remove_duplicates('data.json', key='id')
clean.normalize_dates('data.csv', format='ISO8601')

Examples

See examples/ directory for complete workflows:

• examples/etl-pipeline.sh - Full ETL workflow
• examples/api-data-processing.py - API response processing
• examples/batch-conversion.sh - Bulk file conversion

Installation

Dependencies are minimal and common:

• Python 3.8+
• PyYAML
• pandas (optional, for advanced CSV operations)

pip install pyyaml pandas

Requirements

• Node.js (for JSON/YAML parsing)
• Python 3.8+
• 10MB disk space

License

MIT

Support

Issues: https://github.com/forge-agent/data-toolkit Docs: See docs/ directory

這個工具包功能覆蓋面廣，資料格式轉換、驗證和清理三合一，使用起來比較順手。程式碼質量中規中矩，結構清晰但文件不夠完善——官方說有的示例教程和文件其實並沒有附帶，導致上手需要花時間摸索。依賴管理也不夠友好，沒有自動安裝指令碼需要手動處理。對於日常簡單資料處理夠用，但如果需要更專業的功能可能會受限。總體而言是一個基礎可用的資料處理工具，還有完善空間。