AI Analysis
Final verdict: SUSPICIOUS
The package has minimal direct risks but shows signs of low maintainer activity and poor metadata quality, which raises concerns about its long-term viability and potential for becoming a supply-chain attack vector.
- Low maintainer activity
- Poor metadata quality
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting secure handling of secrets.
- Metadata: The package shows signs of low maintainer activity and poor metadata quality, which may indicate it is not well-maintained or could be abandoned.
Package Quality Overall: Low (2.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1142 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 6.0
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)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with algomancy-cli
Create a Python-based command-line tool that leverages the 'algomancy-cli' package to interact with a backend service designed for data analysis and algorithmic modeling. This tool will serve as a lightweight alternative to the full-featured graphical user interface provided by Algomancy, allowing users to perform complex operations such as model training, prediction, and evaluation directly from their terminal. The project should include the following features: 1. **User Authentication**: Implement a simple login system where users can authenticate themselves using their credentials (username and password). These credentials should be securely handled, and the 'algomancy-cli' package will handle the backend communication for authentication. 2. **Model Management**: Users should be able to list all available models, upload new models, and delete existing ones. The 'algomancy-cli' package will facilitate the interaction with the backend API for these operations. 3. **Data Operations**: Allow users to import datasets into the system and manage them (list, delete). Additionally, implement functionality to preprocess data before feeding it into models. Use the 'algomancy-cli' package to communicate with the backend for handling these tasks. 4. **Model Training and Evaluation**: Provide options to train models on uploaded datasets and evaluate their performance. Include metrics such as accuracy, precision, recall, etc., based on the type of problem (classification/regression). 5. **Prediction Service**: Enable users to make predictions using trained models. Ensure that the predictions are returned in a readable format and that the process is seamless thanks to the 'algomancy-cli' package. 6. **Logging and Error Handling**: Implement comprehensive logging to record all actions performed through the CLI. Also, ensure robust error handling to gracefully manage any issues that arise during execution. The application should be well-documented, with clear instructions on how to install dependencies, set up the environment, and run the tool. Additionally, provide examples of how each feature can be utilized in different scenarios to showcase the flexibility and power of the 'algomancy-cli' package.