🤖 AI Analysis

Final verdict: SAFE

The package appears safe with low risks across all categories. The only notable concern is the metadata risk due to low activity and a new maintainer, but there are no clear signs of malicious intent.

Low network, shell, obfuscation, and credential risks.
Metadata risk slightly elevated due to low activity and new maintainer.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires online services.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or system access abuse.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low activity and new maintainer suggest potential risk, but no clear malicious indicators.

🔬 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "BytePengwin" 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 ModelStash

Your task is to develop a comprehensive mini-application that leverages the 'ModelStash' Python package to create an interactive AI-driven chatbot with enhanced user experience features. This chatbot will not only provide quick and accurate responses but also track the costs associated with using different AI models, allowing users to monitor their usage efficiently. Here’s a detailed breakdown of the project requirements:

1. **Setup and Initialization**:
- Install necessary packages including 'ModelStash'. Ensure you have your API keys ready for integration.
- Initialize the chatbot with ModelStash, setting up the environment to support both synchronous and asynchronous requests.

2. **Core Functionality**:
- Develop the main chat interface where users can input queries.
- Utilize ModelStash to fetch responses from OpenAI-compatible APIs, ensuring that the chatbot can handle a variety of inputs and generate appropriate outputs.

3. **Cost Tracking Feature**:
- Implement a feature within the chatbot that tracks the costs associated with each model call.
- Display this information to the user, perhaps through a dashboard or a simple console output, detailing the total cost of interactions over time.

4. **Enhanced User Experience Features**:
- Integrate a history feature that allows users to review past conversations.
- Implement a feedback mechanism where users can rate the quality of responses received, helping to improve future interactions.

5. **Asynchronous Support**:
- Demonstrate the capability of handling multiple user interactions simultaneously by utilizing ModelStash’s async support.
- Show how the chatbot can manage these interactions efficiently without blocking other processes.

6. **Testing and Documentation**:
- Write comprehensive tests to ensure the reliability and efficiency of your chatbot.
- Document your code thoroughly, providing clear instructions on how to set up and run the application, as well as any additional notes on functionality or troubleshooting tips.

By completing this project, you’ll gain hands-on experience with the ModelStash package, understanding its capabilities in managing AI models and tracking costs, while also enhancing your skills in building user-friendly applications.