AI Analysis
Final verdict: SUSPICIOUS
The package is flagged as suspicious due to low maintainer effort and lack of community involvement, despite showing no signs of obfuscation or credential harvesting.
- Low maintainer effort
- Lack of community involvement
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer effort and lack of community involvement, raising suspicion but not conclusive evidence of malice.
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 (1344 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
Email domain looks legitimate: amesa.com>
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 amesa
Create a distributed reinforcement learning (RL) training application using the 'amesa' package. This application will allow users to train RL agents on multiple machines simultaneously, optimizing the training process and reducing time-to-solution. Here are the steps and features you need to implement: 1. **Setup**: Begin by setting up your environment with the necessary dependencies, including the 'amesa' package. 2. **Agent Design**: Design a simple RL agent that can navigate through a basic maze environment. The agent should learn to find the shortest path from a start point to an end point. 3. **Distributed Training**: Utilize the 'amesa' package to distribute the training of the agent across multiple machines. Each machine will handle a part of the training process, updating the model parameters periodically. 4. **Monitoring and Visualization**: Implement a feature that allows real-time monitoring of the training progress. This could include visualizing the agent's performance over time, the number of iterations completed, and the current best path found. 5. **User Interface**: Develop a simple web-based UI where users can interact with the application. They should be able to start, stop, and monitor the training process. 6. **Documentation**: Provide comprehensive documentation explaining how to set up the application, how it works internally, and how to use it effectively. Features: - Support for multiple environments (e.g., different mazes) - Ability to scale the number of machines used for training dynamically - Detailed logging and error handling - Option to save and load trained models - Integration with popular RL libraries like OpenAI Gym or RLLib for more complex environments Utilization of 'amesa': The 'amesa' package will be crucial for distributing the training process. It will handle the communication between different machines, synchronization of model parameters, and overall coordination of the training. Make sure to leverage its capabilities to ensure efficient and effective distributed training.
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