aind-dataverse-service-client

v0.3.1 safe
4.0
Medium Risk

aind-dataverse-service

🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk across all categories with no indications of malicious activities. However, its low activity and incomplete metadata suggest it may be newly maintained or less frequently updated.

  • No network calls detected
  • Lacks standard metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low activity and lacks standard metadata, indicating potential low effort or new maintainer status.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • 7 test file(s) detected (e.g. test_default_api.py)
○ 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
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 33 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

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: openapitools.org

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "OpenAPI Generator community" 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 aind-dataverse-service-client 
Your task is to develop a Python-based mini-application named 'Dataverse Explorer' which will serve as a user-friendly interface to explore datasets hosted on a Dataverse server using the 'aind-dataverse-service-client' package. This application should allow users to search for datasets based on various criteria such as keywords, authors, publication dates, and categories. Additionally, it should provide functionality to view dataset metadata, download specific files from a dataset, and manage local copies of datasets (e.g., delete, rename). Here are the steps and features you need to implement:

1. **Setup**: Begin by installing the 'aind-dataverse-service-client' package along with other necessary dependencies. Ensure your environment is set up correctly to interact with a Dataverse server.
2. **Authentication**: Implement a login system where users can authenticate themselves to access private datasets. Use OAuth2 or similar protocols supported by the Dataverse service.
3. **Search Functionality**: Create a robust search feature allowing users to filter datasets by keywords, authors, publication dates, and categories. The search should return results with relevant metadata like title, author names, publication date, and abstract.
4. **Dataset Viewer**: Develop a viewer component that displays detailed information about a selected dataset including all its files, descriptions, and any additional metadata provided by the Dataverse server.
5. **Download Manager**: Integrate a file downloader that allows users to selectively download files from a dataset directly into their local storage. Ensure that progress bars and error handling are implemented for a smooth user experience.
6. **Local Dataset Management**: Add functionalities to manage local copies of downloaded datasets such as renaming, deleting, and updating them when new versions become available.
7. **User Interface**: Design a clean, intuitive UI using frameworks like PyQt or Tkinter to make the application accessible and easy to use.
8. **Documentation & Testing**: Provide comprehensive documentation detailing how to install, configure, and use the application. Conduct thorough testing to ensure reliability and usability.

In each of these steps, utilize the core features of the 'aind-dataverse-service-client' package effectively to connect with the Dataverse server, retrieve data, and manage interactions with datasets. Your goal is to create a versatile tool that enhances the accessibility and usability of datasets stored in a Dataverse repository.

💬 Discussion Feed

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