Generate validated MessyData YAML configurations for synthetic dirty data pipelines.
npx skills add https://github.com/sodadata/messydata --skill messydata
Please help me install this Agent Skill. Skill: messydata Source: https://github.com/sodadata/messydata/tree/main Command: npx skills add https://github.com/sodadata/messydata --skill messydata
MessyData enables you to generate synthetic, realistic dirty data for testing data pipelines, data quality tooling, and ML workflows without writing custom data generators.
Create a MessyData YAML config, then run the validate command before generating.
High-intent search queries and answers about installing and using this skill.
You can generate synthetic dirty data by defining a declarative YAML configuration that specifies datasets, distributions, and anomalies. This approach replaces custom procedural data generators and supports date-aware generation via CLI or Python API.
Synthetic data anomaly injection is the process of deliberately introducing realistic errors into generated datasets. You need it when validating data quality tools or stress-testing anomaly detection algorithms against imperfect real-world data conditions.
To validate a YAML config before generating synthetic datasets, run the validate command against your configuration file. This ensures your declarative setup satisfies the required YAML schema and orchestration rules before executing data generation pipelines.
Yes, you can use Python APIs for end-to-end synthetic data generation workflows. The framework provides both CLI and Python API interfaces, allowing you to programmatically control date-aware generation and anomaly injection within your testing scripts.
Yes, declarative YAML data generation supports date-aware modes. This feature allows you to generate synthetic dirty data with realistic temporal distributions, which is critical for accurately testing time-series pipelines and ML workflows.
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