aws-sagemaker-marimo

v0.2.0 suspicious
7.0
High Risk

Run marimo reactive notebooks on Amazon SageMaker Studio Lab and SageMaker Studio

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows unusually high metadata risk due to missing maintainer history and a non-existent git repository, raising concerns about its legitimacy despite low risks in other categories.

  • High metadata risk
  • Missing maintainer history
  • Non-existent git repository
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of being potentially malicious due to lack of maintainer history and a non-existent git repository.

📦 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 (12311 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

  • Type checker (mypy / pyright / pytype) referenced in project
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 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 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 6.0

3 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)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aws-sagemaker-marimo 
Create a data science collaboration tool using the 'aws-sagemaker-marimo' package that enables teams to work together on machine learning projects within Amazon SageMaker Studio. This tool should allow users to create and share interactive Jupyter-style notebooks directly from their SageMaker environments. Users should be able to collaborate in real-time, view changes made by others, and have version control for their notebooks. Additionally, the tool should integrate seamlessly with AWS services such as S3 for data storage and Lambda for serverless computing tasks.

Steps to implement:
1. Set up an AWS account and configure the necessary permissions for accessing SageMaker Studio and other required AWS services.
2. Install and configure the 'aws-sagemaker-marimo' package in your local development environment and within your SageMaker Studio instance.
3. Develop a user interface that allows users to create new marimo notebooks, open existing ones, and invite collaborators.
4. Implement real-time collaboration features allowing multiple users to edit the same notebook simultaneously while seeing each other’s changes.
5. Integrate version control into the application so that users can revert to previous versions of their notebooks if needed.
6. Ensure seamless integration with AWS S3 for storing data and notebooks, and with AWS Lambda for running serverless functions.
7. Test the application thoroughly to ensure all functionalities work as expected.
8. Document the setup process and usage instructions for other team members.

Features:
- Real-time collaboration on marimo notebooks.
- Version control for notebooks.
- Seamless integration with AWS S3 for data and notebook storage.
- Integration with AWS Lambda for serverless computing tasks.
- User-friendly interface for creating, opening, and managing notebooks.
- Invitation system for adding collaborators.

Utilizing 'aws-sagemaker-marimo':
- Use the 'aws-sagemaker-marimo' package to run and manage reactive notebooks on SageMaker Studio Lab and SageMaker Studio. This includes setting up the environment, creating and managing notebooks, and ensuring they are accessible via the web-based interface.

💬 Discussion Feed

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