🤖 AI Analysis

Final verdict: SUSPICIOUS

While the package shows no immediate signs of malicious activity such as network calls, shell executions, or obfuscation, the low maintainer activity and poor metadata quality raise concerns about its long-term support and potential for supply-chain attacks.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell executions detected, indicating the package does not attempt to execute system commands.
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 low maintainer activity and poor metadata quality, raising some 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

Brief PyPI description (222 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 short
Author "" 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 ai-parade

Create a personalized AI-driven recommendation engine using the 'ai-parade' package. This application will serve as a versatile tool for suggesting items such as movies, books, or music based on user preferences and historical data. Here’s how you can structure your project and utilize the 'ai-parade' package effectively:

1. **Project Setup**:
- Initialize a new Python project and install necessary dependencies including 'ai-parade'.
- Set up a simple Flask or Django backend to handle API requests.

2. **Data Collection**:
- Gather a dataset containing user profiles, their past interactions (e.g., ratings, views), and item metadata.
- Preprocess the data to ensure it's clean and ready for model training.

3. **Model Integration**:
- Use 'ai-parade' to add a recommendation model into the parade. This involves selecting an appropriate algorithm (such as collaborative filtering or content-based filtering) and configuring it according to your needs.
- Train the model using the preprocessed dataset.

4. **User Interface**:
- Develop a frontend interface using React or Vue.js where users can input their preferences and view recommendations.
- Ensure the interface is user-friendly and visually appealing.

5. **Recommendation Engine**:
- Implement functionality within the app that queries the trained model from 'ai-parade' to generate personalized recommendations for each user.
- Allow users to rate recommended items, which can then be used to refine future recommendations.

6. **Feedback Loop**:
- Integrate a system where user feedback (ratings, likes/dislikes) is fed back into the model to continuously improve its accuracy.

7. **Testing and Deployment**:
- Thoroughly test the application to ensure all components work seamlessly together.
- Deploy the application to a cloud platform like AWS or Heroku.

8. **Documentation**:
- Write comprehensive documentation detailing how to set up, use, and contribute to the application.

By following these steps and utilizing the 'ai-parade' package for model integration and management, you'll create a robust recommendation engine capable of enhancing user experience through personalized suggestions.