AI Analysis
Final verdict: SUSPICIOUS
The package has a moderate risk score due to its low-effort metadata and lack of maintainer history, which may indicate potential issues with its provenance.
- Low-effort metadata with missing author details
- Lack of maintainer history
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
- Metadata: The package shows signs of low effort and could be suspicious due to the lack of maintainer history and missing author details.
Package Quality Overall: Low (1.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ 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
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ 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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 8.0
4 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor 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)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with aspn23
Create a mini-application called 'ASPN-23 Explorer' that leverages the 'aspen23' Python package to provide users with an interactive exploration of data represented in the ASPN-23 format. This application will allow users to visualize, analyze, and manipulate datasets that conform to the ASPN-23 standard. Here’s a step-by-step guide on how to develop this application: 1. **Setup Environment**: Begin by setting up your Python environment and installing the 'aspen23' package. Ensure you have the latest version available. 2. **Data Loading**: Implement functionality to load datasets in ASPN-23 format. Users should be able to upload their own files or choose from a selection of preloaded datasets included with the application. 3. **Visualization Tools**: Develop visual tools that allow users to graphically represent the data in various ways (e.g., line charts, bar graphs, heat maps). These visualizations should dynamically update based on user interactions with the dataset. 4. **Analysis Features**: Incorporate analytical tools such as filtering, sorting, and statistical analysis functions (mean, median, mode, etc.). Allow users to apply these analyses to specific subsets of the data they select. 5. **Manipulation Capabilities**: Enable users to modify the dataset directly within the application, including adding, removing, or editing data entries. Ensure changes made are reflected in real-time across all visual representations. 6. **Export Options**: Provide options for users to export their modified datasets back into ASPN-23 format or other common formats like CSV or JSON. 7. **User Interface**: Design a clean, intuitive UI using frameworks like PyQt or Tkinter that makes navigating through the application easy and enjoyable. 8. **Documentation & Support**: Write comprehensive documentation detailing how to use the application effectively, including tutorials and FAQs. Consider adding support for community contributions to the documentation. Throughout development, utilize the 'aspen23' package's core functionalities to ensure seamless integration of data handling and manipulation processes. Focus on making the application accessible and user-friendly while providing robust data management capabilities.
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