AI Analysis
The package exhibits low risk in terms of network calls, shell execution, obfuscation, and credential handling. However, the metadata quality is poor, raising concerns about the developer's intentions or professionalism.
- Low metadata quality
- Potential lack of developer commitment
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
- Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- Metadata: The package shows low effort in maintaining metadata and author details, which raises some suspicion but does not conclusively indicate malice.
Package Quality Overall: Low (2.8/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (6808 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
22 type-annotated function signatures detected in source
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked β 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
3 maintainer concern(s) found
Author 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
Create a task management mini-app using the 'appm' package, which stands for APPN Phenomate Project Manager. This mini-app will allow users to manage their tasks efficiently by adding, deleting, updating, and listing tasks. Additionally, users should be able to categorize tasks into different projects and set deadlines for each task. Hereβs a detailed step-by-step guide on how to develop this mini-app: 1. **Project Setup**: Start by setting up your Python environment and installing the 'appm' package. Use pip to install it if itβs not already installed. 2. **User Interface Design**: Design a simple yet intuitive command-line interface (CLI) where users can interact with the app. Ensure that the CLI supports basic commands like 'add', 'delete', 'update', 'list', and 'set deadline'. 3. **Task Management Features**: Implement core functionalities such as adding new tasks with descriptions, deleting tasks, updating task details, and listing all tasks. Each task should have a unique identifier. 4. **Project Categorization**: Allow users to assign tasks to different projects. Projects should also be manageable through the CLI, enabling users to add, delete, and list projects. 5. **Deadline Management**: Integrate functionality to set deadlines for tasks. Users should be able to view upcoming deadlines and get notifications about tasks that are approaching their deadlines. 6. **Data Persistence**: Use 'appm' to handle data persistence. Ensure that tasks and projects are saved and can be retrieved even after the app is closed and reopened. 7. **Error Handling**: Implement robust error handling to manage invalid inputs gracefully and provide meaningful feedback to the user. 8. **Testing**: Write unit tests for all functionalities to ensure they work as expected. Focus on edge cases to improve the reliability of the app. 9. **Documentation**: Document your code thoroughly and provide a README file explaining how to use the app and its features. The 'appm' package will be primarily used for managing the storage and retrieval of tasks and projects. It simplifies the process of storing complex hierarchical data structures, making it easier to implement features like categorizing tasks under projects and managing deadlines. Utilize the packageβs documentation to understand how to integrate these features effectively into your mini-app.
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