asta-autodiscovery

v0.1.5 suspicious
4.0
Medium Risk

Open-ended Scientific Discovery via Bayesian Surprise

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to potential shell execution vulnerabilities and low maintainer activity, but lacks clear indicators of malicious intent.

  • Shell execution without proper error handling and validation
  • Low maintainer activity and poor metadata quality
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution is used to install dependencies but lacks proper error handling and validation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (3422 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 126 type-annotated function signatures detected in source
○ 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)

  • ss def install(package): subprocess.check_call(["install-package", package])\n\n\n""" else: ins
  • ys def install(package): subprocess.check_call([sys.executable, "-m", "pip", "install", "--quiet", package]
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ 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

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with asta-autodiscovery 
Create a Python-based mini-application that leverages the 'asta-autodiscovery' package to enable users to explore scientific datasets through the lens of Bayesian Surprise. This application should allow users to upload their own datasets or select from predefined ones, and then use the Bayesian Surprise algorithm to identify the most surprising elements within the data. The goal is to uncover hidden patterns or anomalies that traditional statistical methods might miss.

### Key Features:
1. **User Interface**: Develop a simple yet intuitive web interface using Flask or Django. Users should be able to upload CSV files or choose from a set of example datasets.
2. **Data Exploration**: Implement functionality to visualize the uploaded data using libraries such as Matplotlib or Seaborn. Provide basic statistical summaries.
3. **Bayesian Surprise Analysis**: Utilize 'asta-autodiscovery' to perform Bayesian Surprise analysis on the dataset. Display the results highlighting the most surprising elements identified by the algorithm.
4. **Interactive Dashboard**: Allow users to interactively adjust parameters related to the Bayesian Surprise calculation, such as the prior distribution settings or the significance threshold, to refine the discovery process.
5. **Results Visualization**: Present the findings in a visually appealing manner, perhaps using heatmaps or scatter plots to show where the most significant surprises lie within the data.
6. **Documentation and Help**: Include comprehensive documentation and tooltips within the application to guide users through the process and help them understand the concept of Bayesian Surprise.

### Implementation Steps:
1. Set up your development environment with Python, Flask/Django, and necessary visualization libraries.
2. Install 'asta-autodiscovery' and ensure it integrates seamlessly with your chosen framework.
3. Design and develop the user interface, ensuring it is user-friendly and responsive.
4. Implement data handling functionalities, including file uploads and dataset selection.
5. Integrate 'asta-autodiscovery' into your application, focusing on how to input data and interpret the output effectively.
6. Develop the interactive dashboard and visualization components, making sure they are dynamic and informative.
7. Test the application thoroughly, fixing any bugs and refining the user experience based on feedback.
8. Deploy the application to a cloud service like Heroku or AWS for public access.

By following these steps and utilizing the power of 'asta-autodiscovery', you'll create a powerful tool for anyone interested in exploring the unexpected in their data.

💬 Discussion Feed

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