A zero-dependency Python tool to validate data integrity and schema compliance for CSV, JSON, and JSONL files.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-quality-checker
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install data-quality-checker using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Data Quality Checker?
The Data Quality Checker is a robust diagnostic utility designed to ensure data integrity across various file formats. As part of the Openclaw Skills ecosystem, it provides developers and data scientists with a high-performance, zero-dependency Python solution for identifying structural and content-based issues. It goes beyond simple parsing by calculating a comprehensive quality score (0-100) based on weighted severity, allowing teams to quantify the health of their datasets instantly.
This skill excels at identifying hidden data rot such as statistical outliers, whitespace inconsistencies, and schema drift. By providing clear, severity-ranked reports in both Markdown for human review and JSON for automated pipelines, it bridges the gap between manual data cleaning and automated data engineering workflows. Whether you are preparing datasets for machine learning or validating API responses, this tool ensures your data remains clean and compliant.
Data Quality Checker Use Cases
- Validating CSV, JSON, or JSONL files before importing them into a production database.
- Detecting duplicate records and potential ID conflicts in large datasets.
- Identifying statistical outliers using the Interquartile Range (IQR) method to clean training data.
- Enforcing schema compliance in CI/CD pipelines to prevent malformed data from breaking downstream services.
- Profiling data quality for automated reporting and stakeholder visibility.
How Data Quality Checker Works
- The user provides a data file path and optional parameters like format or specific check filters.
- The script parses the file (CSV, JSON, or JSONL) and optionally compares it against a provided or auto-generated schema.
- It executes a suite of diagnostic tests including type consistency, duplicate detection, and format validation.
- A quality score is calculated based on the quantity and severity of issues found (Critical, Warning, or Info).
- The tool generates a report in the requested format and returns an exit code based on the highest severity found.
Data Quality Checker Setup
To start using this Openclaw Skills utility, ensure you have Python 3 installed. No external dependencies are required.
# Clone your skill repository or navigate to the scripts directory
# Run a basic check on a CSV file
python3 scripts/check_data_quality.py your_data.csv
# To save a Markdown report for documentation
python3 scripts/check_data_quality.py your_data.json --format markdown --output quality_report.md
Data Quality Checker Data Schema & Taxonomy
The skill organizes its findings based on a severity-weighted scoring system. It supports the following data structures:
| Feature | Details |
|---|---|
| Input Formats | CSV, JSON, JSONL |
| Reporting Formats | Markdown (Human-readable), JSON (Machine-readable) |
| Validation Rules | Supports standard JSON schema properties including type, minimum, maximum, pattern, and enum |
| Scoring | 0-100 scale (90+ is Clean; <50 is Critical) |
Data Quality Checker Advanced Features
- Auto-Schema Generation: Automatically create a JSON validation schema based on an existing high-quality dataset.
- CI/CD Integration: Use specific exit codes (0, 1, or 2) to break builds if critical data quality issues are detected.
- Targeted Checks: Isolate specific issues by running only a subset of checks (e.g.,
--checks missing,duplicates). - Statistical Profiling: Built-in outlier detection using the IQR method to find data points that deviate significantly from the norm.
- Schema Drift Detection: Identify inconsistent keys or properties across different rows in JSON and JSONL files.
SKILL.md
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