Skill: atlasnexusops/data-toolkit (version 1.0.1)

--- name: data-toolkit description: Complete data conversion, validation, and cleaning toolkit. Convert between JSON/CSV/YAML/XML, validate schemas, clean duplicates and nulls. Essential utilities for data processing workflows. version: 1.0.0 author: Forge metadata: { "openclaw": { "requires": { "bins": ["node", "python3"] }, "install": [] } } --- # 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 - **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 ```bash # 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 ```bash # 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 ```bash # 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) ```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) ```bash 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)