🤖 AI Analysis

Final verdict: SUSPICIOUS

The package DQRbuild v0.3.0 has a moderate risk score due to its shell execution risks and metadata inconsistencies.

Subprocess calls within the package may pose legitimate but unverified execution risks.
Lack of maintainer history and author information suggests potential metadata tampering.

Per-check LLM notes

Network: No network calls detected, minimal risk.
Shell: Subprocess calls to Python modules within the package may be legitimate, but require further investigation into the purpose and necessity of these executions.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is suspicious due to lack of maintainer history and a missing author name, indicating potential risk.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

temp("e2e_dag") result = subprocess.run( [ sys.executable, "-m", "dqr_config.dqr
p("dag_output") result = subprocess.run( [ sys.executable, "-m", "dqr_config.dqr

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ligo.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with DQRbuild

Create a Python-based mini-application named 'DataQualityChecker' that leverages the DQRbuild package to perform comprehensive data quality checks on input datasets. This application should serve as a robust tool for data analysts and engineers who need to ensure their datasets meet specific criteria before further analysis or processing.

Step-by-Step Requirements:
1. **Setup Environment**: Ensure the environment is set up with the latest version of DQRbuild installed via pip.
2. **User Interface**: Develop a simple command-line interface (CLI) where users can interact with the DataQualityChecker app. The CLI should accept inputs such as file paths and parameters for different data quality checks.
3. **Data Quality Checks**:
- Implement at least five types of data quality checks using DQRbuild functionalities. These could include completeness, uniqueness, consistency, validity, and accuracy checks.
4. **Report Generation**: After performing the checks, generate a detailed report summarizing the findings. The report should be saved as a PDF and/or CSV file, providing insights into which records passed and failed each check.
5. **Integration with External Tools**: Allow for the integration of the DataQualityChecker results with popular data visualization tools like Tableau or PowerBI, enabling users to visualize the data quality metrics.
6. **Error Handling and Logging**: Incorporate error handling mechanisms and logging to track any issues during execution and provide meaningful feedback to the user.
7. **Documentation**: Write comprehensive documentation for the application, including setup instructions, usage guidelines, and examples of how to use the CLI effectively.

How DQRbuild is Utilized:
- Use DQRbuild to manage dependencies and ensure all required libraries are correctly pinned to compatible versions. This will help maintain stability and reliability in the application.
- Leverage DQRbuild's toolkit for executing various data quality checks efficiently and accurately. Each type of check should utilize appropriate functions or modules from DQRbuild to process and analyze the dataset.