AI Analysis
The package exhibits low risks in terms of network, shell execution, obfuscation, and credential harvesting, but the metadata risk score is notably high due to the absence of maintainer history and a GitHub repository. This raises suspicion about its legitimacy.
- High metadata risk due to missing maintainer history and GitHub repository.
- Low individual risk scores for network, shell, obfuscation, and credential harvesting.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- 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 several red flags including lack of maintainer history and a missing GitHub repository, indicating potential risk.
Package Quality Overall: Low (1.2/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: gmail.com>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
5 maintainer concern(s) found
Only one version has ever been released — brand new packagePackage is very new: uploaded 3 day(s) agoAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Your task is to develop a small, interactive data visualization tool using Python's 'arrowshader' package. This tool will allow users to visualize large datasets in the form of arrow tables, providing insights into complex data structures through dynamic, scalable visual representations. The application should have a user-friendly interface where users can upload their own arrow table files or use preloaded sample data. Upon loading the data, the app should automatically generate a visualization based on the structure of the arrow table. Additionally, include features such as filtering options, zooming capabilities, and the ability to highlight specific data points or rows within the visualization. The goal is to create an intuitive tool that makes it easy for users to explore and understand large datasets visually. Utilize 'arrowshader's core features to handle the rendering and manipulation of these large datasets efficiently, ensuring the application remains responsive even when dealing with extensive data volumes.
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