AI Analysis
The package has low direct execution risks but raises concerns due to incomplete metadata and lack of maintainer activity.
- Minimal repository engagement
- Incomplete author profile
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communication.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized system access.
- 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 a lack of maintainer history, minimal repository engagement, and an incomplete author profile.
Package Quality Overall: Low (4.4/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Documentation URL: "Documentation" -> https://jamespetersen.ca/apnds/docs/Detailed PyPI description (1134 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
Classifier: Typing :: Typed54 type-annotated function signatures detected in source
Limited contributor diversity
1 unique contributor(s) across 37 commits in ljtpetersen/apndsSingle author but highly active (37 commits)
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: jamespetersen.ca>
All external links appear legitimate
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
3 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)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Your task is to develop a command-line utility named 'DSFileAnalyzer' using the Python package 'apnds'. This tool will serve as a comprehensive analyzer for Nintendo DS ROM files, providing users with detailed information about their contents. Here are the steps and features you need to implement: 1. **Setup**: Start by installing the 'apnds' package via pip. Ensure your environment supports Python 3. 2. **Main Functionality**: - Implement a function to read and parse the header of a given Nintendo DS ROM file. - Provide another function to extract specific sections of the ROM based on user input (e.g., extracting game data, tilemaps). 3. **Feature Suggestions**: - Include a feature to compare two different ROM files for similarities or differences. - Add functionality to repair minor issues within the ROM files if possible (e.g., fixing checksum errors). - Offer an option to convert between different supported Nintendo DS formats. 4. **User Interface**: - Design a simple yet effective command-line interface that allows users to select which actions they want to perform. - Include help documentation accessible via command-line arguments to guide users through the available options. 5. **Testing**: - Write unit tests for each major function to ensure reliability. - Test the utility with a variety of Nintendo DS ROMs to verify compatibility and correctness. 6. **Documentation**: - Create a README file that explains how to install and use 'DSFileAnalyzer', including examples of commands and expected outputs. 7. **Deployment**: - Prepare the project for deployment on platforms like GitHub, ensuring it's easily accessible and understandable for other developers. By following these guidelines, you'll create a robust tool that leverages the 'apnds' package's capabilities to offer valuable insights into Nintendo DS ROM files.
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