aptapy

v0.19.2 suspicious
4.0
Medium Risk

Statistical tools for online monitoring and analysis

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse due to shell execution for Git operations and an incomplete maintainer profile, though there's no strong evidence of malicious activity.

  • Shell risk due to Git operations
  • Incomplete maintainer profile
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution appears to be used for local Git operations and might not indicate malicious intent, but further investigation is needed.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has an incomplete profile and may be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • Test runner config found: conftest.py
  • 9 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • 20 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (947 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 168 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in lucabaldini/aptapy
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • short", "HEAD"] sha = subprocess.check_output(args, **kwargs).decode().strip() suffix = f"+g{sha}"
  • "diff", "--quiet"] if subprocess.call(args, stdout=subprocess.DEVNULL, **kwargs) != 0:
  • in(args)}\"...") result = subprocess.run(args, capture_output=True, text=True, check=True) print(
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: unipi.it>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository lucabaldini/aptapy 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 aptapy 
Your task is to create a real-time data analysis dashboard using the 'aptapy' Python package. This dashboard will be designed for a manufacturing company that wants to monitor its production line metrics in real time. The goal is to detect anomalies and trends that could indicate potential issues or inefficiencies in the production process. Here’s a detailed breakdown of what your application should include:

1. **Real-Time Data Ingestion**: Implement a feature that allows for continuous data ingestion from a simulated or actual production line. This data will include key metrics such as machine uptime, defect rates, production volume, etc.
2. **Data Visualization**: Utilize 'aptapy' to perform statistical analyses on the incoming data streams. Create visualizations (e.g., graphs, charts) that display these metrics in real-time, allowing operators to quickly understand the current state of the production line.
3. **Anomaly Detection**: Using 'aptapy', implement an anomaly detection system that flags unusual patterns or spikes in the data. These alerts should be displayed prominently within the dashboard and also sent via email/SMS to relevant personnel.
4. **Trend Analysis**: Provide a feature that shows historical trends over different time periods (hourly, daily, weekly). Use 'aptapy' to highlight significant changes or trends that could indicate long-term issues or improvements in the production process.
5. **User Interface**: Design an intuitive user interface that includes interactive elements like dropdown menus to select different data views, sliders to adjust time ranges, and buttons to trigger manual data refreshes.
6. **Customizable Alerts**: Allow users to customize alert thresholds based on their specific needs and preferences. For example, they might want to be notified only when certain metrics exceed or fall below particular values.
7. **Documentation and Testing**: Ensure that you provide comprehensive documentation for setting up and using the application, along with thorough testing procedures to verify the functionality and accuracy of the statistical analyses performed by 'aptapy'.

The 'aptapy' package will be crucial for performing the necessary statistical analyses on the real-time data streams. It offers robust tools for online monitoring and analysis which are perfect for detecting anomalies and trends in the data. Your application should leverage these capabilities to offer valuable insights into the performance of the production line.

💬 Discussion Feed

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