🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell execution, obfuscation, and credential handling. However, the metadata risk score is high due to the absence of maintainer history and a lack of an associated GitHub repository, raising concerns about its legitimacy.

High metadata risk due to missing maintainer history and no associated GitHub repository
Otherwise low risk in other categories

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity that involves executing system commands.
Obfuscation: No obfuscation patterns detected, suggesting low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is suspicious due to lack of maintainer history and no associated GitHub repository.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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 DQRAlert

Create a mini-application called 'DQRMonitor' that utilizes the Python package 'DQRAlert' to manage and monitor IGWN alerts related to Data Quality Reports (DQR). Your goal is to develop a tool that not only processes incoming alerts but also provides a user-friendly interface for tracking the status of various data quality checks over time.

Step-by-Step Instructions:
1. Set up a basic Flask web application as the framework for your mini-app.
2. Integrate 'DQRAlert' into your application to handle the processing of IGWN alerts related to DQRs.
3. Implement a feature to store alert history in a SQLite database for persistent storage.
4. Develop a simple API endpoint that allows users to query the status of specific alerts or retrieve historical data.
5. Create a dashboard within the web app where users can visualize alert statuses and trends over time using charts and graphs.
6. Ensure that the application can send notifications via email or SMS when critical alerts are received.
7. Add documentation and comments to your code to make it easy for others to understand and maintain.

Suggested Features:
- Real-time alert monitoring with live updates on the dashboard.
- Detailed alert logs with timestamps and descriptions.
- Customizable alert thresholds for different severity levels.
- User authentication to restrict access to sensitive alert information.
- Export options for alert data in CSV or JSON format.
- Integration with popular email services for notification delivery.

How to Utilize 'DQRAlert':
- Use 'DQRAlert' to parse incoming IGWN alerts and categorize them based on their severity and relevance to DQRs.
- Leverage the package's capabilities to filter and prioritize alerts based on predefined criteria.
- Store processed alerts in the SQLite database for historical analysis and reporting purposes.
- Utilize 'DQRAlert' functions to generate summary reports and statistics for display on the dashboard.
- Implement custom alert handling logic within your application to trigger notifications or automated responses.