aegis-dq

v0.7.0 suspicious
4.0
Medium Risk

Open, audit-grade agentic data quality framework with portable industry packs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks primarily due to its network and credential handling practices, which warrant closer scrutiny.

  • moderate network risk
  • credential handling requiring further investigation
Per-check LLM notes
  • Network: The network calls indicate the package is likely making HTTP requests, possibly for reporting or updating purposes, which is not inherently suspicious but should be reviewed for context.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No signs of obfuscation detected in the provided code snippet.
  • Credentials: The code is likely accessing AWS credentials through standard methods but requires further investigation to ensure proper handling and usage.
  • Metadata: The maintainer has a new or inactive account and lacks a full author name, raising some suspicion but not definitive proof of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • } async with httpx.AsyncClient(timeout=self._timeout) as client: resp = await c
  • yload(report) async with httpx.AsyncClient(timeout=timeout) as client: resp = await client.post
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 4 credential access pattern(s)

  • ), "region_name": lambda: os.environ.get("AWS_REGION") or os.environ.get("AWS_DEFAULT_REGION"), "schem
  • .environ.get("AWS_REGION") or os.environ.get("AWS_DEFAULT_REGION"), "schema_name": lambda: os.environ.get(
  • le: AWS profile name (reads ~/.aws/credentials). region: AWS region (default us-east-1). """
  • * environment variables - ~/.aws/credentials / instance profile Table references use the schema_name co
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository aegis-dq/aegis-dq appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 aegis-dq 
Create a data quality monitoring tool using the 'aegis-dq' package. This tool will serve as a dashboard for users to input datasets and receive comprehensive data quality reports. Here are the steps and features to include:

1. **User Interface**: Develop a simple web-based interface where users can upload their datasets. The interface should allow users to select files from their local machine.
2. **Data Quality Checks**: Utilize 'aegis-dq' to perform various data quality checks such as completeness, consistency, accuracy, validity, uniqueness, and conformity. Display these checks in a tabular format within the dashboard.
3. **Visualization**: Implement visualizations (charts/graphs) to represent the results of the data quality checks. For example, bar charts showing the percentage of missing values across different columns.
4. **Report Generation**: Allow users to generate PDF reports summarizing the data quality findings. These reports should include detailed descriptions of each check performed and the corresponding results.
5. **Customizable Checks**: Provide an option for advanced users to customize the data quality checks based on their specific needs. They should be able to choose which checks to run and set thresholds for acceptable data quality.
6. **Integration with Other Tools**: Ensure the tool can integrate with other common data analysis tools like Jupyter Notebooks or BI platforms, allowing seamless data transfer and further analysis.

In this project, the 'aegis-dq' package will be crucial for performing the data quality checks. Users will benefit from its audit-grade capabilities and portable industry packs, ensuring high standards of data quality are maintained.