AI Analysis
The package has low technical risks but raises concerns due to its placeholder nature and lack of detailed metadata.
- Low effort in package creation
- Missing maintainer history and author details
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package is expected to communicate with external services.
- 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 of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- 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)
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: apploom.ai>
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
Create a simple task management application using the Python package 'apploom-sdk'. This application will allow users to create, update, delete, and view tasks. Each task will have a title, description, due date, and status (e.g., pending, completed). The apploom-sdk package provides essential functions for managing these tasks efficiently. Steps to build the application: 1. Set up your development environment with Python installed. 2. Install the apploom-sdk package via pip. 3. Create a main module where you will define classes and functions to manage tasks. 4. Implement a class named Task which holds information about a task such as title, description, due_date, and status. 5. Develop a TaskManager class that utilizes the apploom-sdk package to perform CRUD operations on tasks. This class should include methods like add_task(), get_tasks(), update_task(), and delete_task(). 6. Write a command-line interface (CLI) that allows users to interact with the TaskManager class. Users should be able to create new tasks, view all tasks, update existing tasks, and delete tasks. 7. Enhance the CLI with options to filter tasks based on their status or due dates. 8. Test the application thoroughly to ensure all functionalities work as expected. 9. Document your code and provide instructions on how to run the application. Suggested Features: - Allow users to mark tasks as completed. - Implement sorting of tasks by due date or status. - Add error handling to gracefully manage exceptions. - Include a feature to export task data to a CSV file. How 'apploom-sdk' is Utilized: - Use the apploom-sdk package to handle the backend logic of storing and retrieving tasks. This includes connecting to a database, performing CRUD operations, and ensuring data integrity. - Leverage any specific features provided by apploom-sdk that might simplify task management, such as automatic validation or indexing.
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