anospp-analysis

v0.5.0 safe
4.0
Medium Risk

ANOSPP data analysis

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across most categories, with only shell execution and metadata indicating some level of concern. There's no strong evidence of malicious intent or supply-chain attack.

  • Shell execution could pose risks if not sanitized properly.
  • Maintainer has only one package, suggesting potential inactivity or newness.
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution is present and could be used for unintended operations if not properly sanitized.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, indicating potential new or inactive status which could be risky.

📦 Package Quality Overall: Low (3.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_functions_vae.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2668 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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)

  • ging.info(cmd) process = subprocess.run(cmd.split(), capture_output=True, text=True) process.che
  • """ try: result = subprocess.run( list(cmd), check=True,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: sanger.ac.uk

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Alex Makunin" 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 anospp-analysis 
Create a mini-application named 'AnosppInsight' using the Python package 'anospp-analysis'. This application will serve as a powerful tool for researchers and analysts working with ANOSPP data. AnosppInsight aims to simplify the process of loading, analyzing, and visualizing ANOSPP datasets. Here’s a step-by-step guide on what your application should achieve:

1. **Data Loading**: Develop a feature that allows users to upload ANOSPP data files (CSV or Excel formats). Ensure that the application supports both local file uploads and remote file URLs.
2. **Data Cleaning**: Implement basic data cleaning functionalities such as handling missing values, removing duplicates, and ensuring data consistency.
3. **Analysis Tools**: Utilize the core functionalities of 'anospp-analysis' to provide advanced analytical tools like statistical summaries, trend analysis, and anomaly detection specific to ANOSPP data.
4. **Visualization**: Integrate visualization capabilities using popular Python libraries like Matplotlib or Seaborn. Users should be able to generate plots such as line charts, bar graphs, and heatmaps based on their selected data and analysis parameters.
5. **Export Results**: Allow users to export the results of their analyses and visualizations in various formats including CSV, Excel, and image formats.
6. **User Interface**: Design a simple yet intuitive user interface where users can interact with all the above features without needing extensive programming knowledge. Consider using frameworks like Streamlit or Flask for building the UI.
7. **Documentation and Support**: Provide comprehensive documentation detailing how to use each feature within AnosppInsight. Additionally, include a FAQ section addressing common issues and a support email for further assistance.

Suggested Features:
- Integration with Jupyter Notebooks for more advanced users who prefer a coding environment.
- Real-time data updates from specified sources.
- Customizable alerts based on predefined conditions within the data.
- Multi-language support for broader accessibility.

By following these guidelines, AnosppInsight will not only streamline the process of working with ANOSPP data but also offer valuable insights through its robust analysis and visualization capabilities.

💬 Discussion Feed

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