AI Analysis
Final verdict: SUSPICIOUS
The package shows low risk in terms of network, shell, and obfuscation activities but raises some concerns due to its low activity level and the limited history of its author.
- Low package activity
- Author with limited history
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 detected, indicating no immediate risk of command injection or system manipulation.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package has low activity and an author with limited history, raising some suspicion.
Package Quality Overall: Low (4.0/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
1 test file(s) detected (e.g. test.py)
○ 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
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
7 type-annotated function signatures (partial)
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 25 commits in nekit508/another-dependency-injectorTwo distinct contributors found
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
Email domain looks legitimate: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with another-dependency-injector
Your task is to develop a simple yet powerful command-line tool for managing a personal library of books. This tool will allow users to add new books, remove existing ones, update book information, and display all books in their collection. To make your application more modular and easier to maintain, you'll use the 'another-dependency-injector' package for dependency injection. Here are the key features your application should have: 1. Users should be able to add new books by specifying title, author, publication year, and genre. 2. Implement a feature to remove a book from the collection by its unique identifier. 3. Provide functionality to update any of the book's details (title, author, publication year, genre). 4. Display all books in the collection in a formatted manner. 5. Add a search function to find books by title or author. 6. Use classes for Books and the Library Manager to encapsulate the logic. 7. Ensure that the Library Manager class can be easily tested by injecting mock objects using 'another-dependency-injector'. The 'another-dependency-injector' package will be crucial for setting up the dependency injection framework. It will help in decoupling the BookManager class from the data storage mechanism (e.g., file system or database), allowing for easy swapping of these dependencies without changing the core logic of the BookManager. Additionally, it will facilitate unit testing by enabling the injection of mock data sources during test runs. Start by installing the package and setting up the basic structure of your application. Then, implement each feature step-by-step, ensuring that you utilize dependency injection effectively to enhance the modularity and testability of your code.
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