AI Analysis
Final verdict: SAFE
The package exhibits minimal risks across network, shell, obfuscation, and credential fronts. While there is some suspicion regarding metadata and the absence of a GitHub repository, these alone do not strongly suggest malicious intent.
- Low risk scores across multiple categories
- Suspicion due to lack of GitHub repository
Per-check LLM notes
- Network: The use of aiohttp.ClientSession with a connection limit is expected for making asynchronous HTTP requests and does not indicate malicious activity.
- Shell: No shell execution patterns detected, indicating no risk of executing arbitrary commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some low-effort indicators and lacks a GitHub repository, raising some suspicion but not strong evidence of malice.
Package Quality Overall: Low (3.6/10)
✦ High
Test Suite
9.0
Test suite present — 9 test file(s) found
9 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
31 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-labtracks-service-async-client
Your task is to create a mini-application named 'LabTracks Data Visualizer' using Python, which leverages the 'aind-labtracks-service-async-client' package to fetch and visualize data from LabTracks service asynchronously. This application will serve as a tool for researchers to monitor and analyze their experimental data in real-time. Here’s a detailed guide on how to proceed: 1. **Setup**: Begin by setting up your development environment. Ensure you have Python installed and create a virtual environment for your project. Install the necessary packages including 'aind-labtracks-service-async-client', 'matplotlib', and 'pandas'. 2. **Authentication**: Implement a secure way to handle authentication tokens required to access the LabTracks API. Store these tokens securely and manage them efficiently. 3. **Data Fetching**: Utilize the 'aind-labtracks-service-async-client' package to asynchronously fetch data from the LabTracks service. Design your code to support fetching different types of data such as experiment logs, sensor readings, etc. 4. **Data Processing**: Once the data is fetched, process it to prepare for visualization. Use 'pandas' to manipulate and clean the data if necessary. 5. **Visualization**: Develop a user-friendly interface to display the processed data. Use 'matplotlib' to create interactive plots and charts. Consider implementing real-time updates to reflect new data as it becomes available. 6. **Additional Features**: Suggest and implement additional features like exporting data to CSV files, setting up alerts for specific conditions in the data, and allowing users to filter and search through historical data. 7. **Testing & Documentation**: Thoroughly test your application to ensure it functions correctly and efficiently. Document your code and provide clear instructions on how to run and use the application. 8. **Deployment**: Finally, consider deploying your application on a platform like Heroku or AWS, making it accessible to other researchers. By following these steps, you'll develop a robust, efficient, and user-friendly tool that enhances the accessibility and usability of LabTracks data.
💬 Discussion Feed
No discussion yet. Be the first to share your thoughts!
Report Abuse / Security Issue