AI Analysis
The package has minimal risk indicators with no network calls, shell executions, or obfuscations detected. While there are some concerns regarding low maintainer effort, there is no concrete evidence to suggest malicious intent.
- Low network and shell execution risks
- No obfuscation or credential harvesting
- Potential low maintainer effort
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 immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer effort and could be suspicious, but there's no direct evidence of malice.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (1842 chars)
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
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
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)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Develop a bridge design mini-app using the Python package 'allHandsOnDeck'. This app will serve as a practical tool for civil engineers to calculate and optimize the reinforcement requirements of beams and slabs in bridge structures based on the AASHTO flexure standards. The application should allow users to input various parameters such as span length, material properties, loading conditions, and desired safety factors. It should then compute the necessary reinforcement details including the size and spacing of rebars, and provide visual outputs like sectional diagrams of the optimized design. Additionally, include features for saving and exporting design reports in PDF format, and integrating a user-friendly GUI built with PyQt or Tkinter. Ensure that the 'allHandsOnDeck' package is utilized throughout the computation process to leverage its advanced algorithms for accurate and efficient calculations.
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