aretro

v0.0.1 suspicious
4.0
Medium Risk

Claimed package name

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network usage, shell execution, obfuscation, and credential handling. However, the metadata quality is poor, suggesting a lack of transparency or effort from the developer.

  • Low metadata quality
  • Potential lack of transparency
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution detected, indicating the package does not execute system commands without explicit user input.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
  • Metadata: The package shows signs of low effort and potential lack of transparency, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

No shell execution patterns detected

✓ 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 8.0

4 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)
  • 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 aretro 
Imagine you're developing a time-traveling book recommendation engine named 'AretroReader'. This application will use the fictional 'aretro' Python package to analyze user preferences based on historical reading habits and recommend books from different eras. Here’s a detailed plan on how to build it:

1. **Project Setup**: Start by setting up your Python environment and installing the 'aretro' package. Ensure you have a virtual environment set up for this project.
2. **Data Collection**: Collect data on various books from different historical periods. This could include titles, authors, publication years, genres, and brief descriptions.
3. **User Profile Creation**: Allow users to create profiles where they can input their favorite books, authors, and genres. Use the 'aretro' package to analyze these inputs and determine which historical era they might prefer.
4. **Recommendation Engine**: Implement the core functionality of AretroReader using the 'aretro' package. Based on the user's profile, generate a list of recommended books from the selected era(s).
5. **Interactive Features**: Add interactive elements such as quizzes to further refine user preferences. For example, users could answer questions about their reading habits to get more personalized recommendations.
6. **Visualization**: Utilize the 'aretro' package to visualize trends in user preferences over different historical periods. This could help users understand their reading patterns better.
7. **Feedback Loop**: Incorporate a feedback mechanism where users can rate the recommended books. This data can then be fed back into the 'aretro' package to improve future recommendations.
8. **Deployment**: Once developed, deploy the application either as a web app or a desktop application, ensuring it's accessible and user-friendly.

By following these steps, you'll create a unique and engaging application that leverages the capabilities of the 'aretro' package to offer personalized book recommendations based on historical reading trends.

💬 Discussion Feed

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