aimo-registry

v0.1.3 suspicious
4.0
Medium Risk

Smart AI model registry with hierarchical access and rich metadata

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low immediate risks but has high metadata risk due to its suspiciously active status with minimal community engagement. This could indicate potential issues such as a lack of long-term support or other hidden risks.

  • High metadata risk
  • Minimal community engagement
  • No clear long-term maintenance plan
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Metadata: Suspiciously new and active with minimal community engagement.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_aimo.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2590 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 38 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 5 commits in MaximeRivest/aimo
  • Single author with few commits — possibly a personal or throwaway project

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • All 5 commits happened within 24 hours
⚠ 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 aimo-registry 
Develop a mini-application called 'ModelManager' using the Python package 'aimo-registry'. This application will serve as a comprehensive tool for managing and tracking machine learning models within a team environment. Here’s a step-by-step guide on how to create it:

1. **Project Setup**: Start by setting up your Python environment. Ensure you have the latest version of 'aimo-registry' installed. Use virtual environments to manage dependencies.

2. **Application Structure**: Design a clean and modular structure for your application. It should include modules for user authentication, model registration, retrieval, and deletion.

3. **User Authentication**: Implement basic user authentication mechanisms to ensure that only authorized users can access and modify the model registry. This could involve creating user roles and permissions based on their level of access.

4. **Model Registration**: Allow users to register new models in the registry. Users should be able to provide rich metadata about each model, such as the model name, version, description, training dataset details, performance metrics, and more. Utilize 'aimo-registry' to store these models hierarchically and manage them efficiently.

5. **Model Retrieval & Search**: Enable users to search for specific models based on various criteria like model name, version, or performance metrics. Provide options to filter and sort the results to make searching more intuitive.

6. **Version Control**: Implement version control so that users can track different versions of the same model. This allows for easy rollback to previous versions if needed.

7. **Model Deletion**: Allow authorized users to delete outdated or irrelevant models from the registry. Ensure that this process is safe and that accidental deletions are prevented through confirmation prompts.

8. **Integration with External Tools**: Integrate 'ModelManager' with other tools commonly used in the data science workflow, such as Jupyter Notebooks or Git repositories, to enhance usability.

9. **Documentation & Testing**: Write clear documentation explaining how to use 'ModelManager' and its features. Include unit tests and integration tests to ensure the application works as expected under different scenarios.

10. **Deployment**: Finally, deploy your application either locally or on a cloud platform like AWS or Google Cloud, making sure it’s accessible to your team members.

Utilize 'aimo-registry' throughout the development process to leverage its hierarchical access and rich metadata capabilities, ensuring that your application is robust and scalable.

💬 Discussion Feed

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