AI Analysis
Final verdict: SAFE
The package exhibits low risk across multiple categories, with no indications of malicious activity. However, the metadata suggests it may be under-maintained.
- Low network, shell, obfuscation, and credential risks
- Metadata suggests potential low maintenance
Per-check LLM notes
- Network: The detection of network call patterns using aiohttp is expected for an asynchronous client library, indicating legitimate network interactions.
- Shell: No shell execution patterns were detected, which is normal and expected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows low signs of malicious intent but could indicate low maintenance or effort.
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
27 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
score 1.5
Found 1 network call pattern(s)
self.pool_manager = aiohttp.ClientSession( connector=aiohttp.TCPConnector(limit=self.m
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-async-client
Your task is to develop a Python-based mini-application that leverages the 'aind-dataverse-service-async-client' package to interact with the Dataverse service asynchronously. This application will serve as a powerful tool for researchers and data scientists who need to manage datasets stored in a Dataverse repository. The application should be designed to perform several key functions, including uploading new datasets, listing existing datasets, downloading datasets, and deleting datasets. Additionally, it should provide a user-friendly interface for these operations, possibly through a command-line interface (CLI) or a simple web interface. Step-by-step instructions: 1. Set up your development environment with Python 3.x and install the 'aind-dataverse-service-async-client' package using pip. 2. Create a configuration file to store API keys and other sensitive information securely. 3. Implement a CLI interface using argparse or click to allow users to input commands such as 'upload', 'list', 'download', and 'delete'. 4. Use the 'aind-dataverse-service-async-client' package to write asynchronous functions for each of these commands. Ensure that these functions handle errors gracefully and provide informative feedback to the user. 5. For the upload function, ensure that the application supports various file formats and provides progress indicators during uploads. 6. For the download function, implement a feature that allows users to specify the path where the dataset should be saved locally. 7. Test the application thoroughly to ensure all functionalities work as expected, and consider adding unit tests for additional reliability. 8. Document your code and provide clear usage instructions for the application. 9. Optionally, extend the application by adding features like dataset versioning, search functionality based on metadata, or integration with other services. By following these steps, you'll create a valuable tool that simplifies interaction with Dataverse repositories and enhances the efficiency of data management tasks.
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