🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network activity, shell execution, and obfuscation. However, its metadata presents several red flags, such as missing maintainer history and author information.

Lack of maintainer history
Missing author information

Per-check LLM notes

Network: No network calls detected, which is normal unless the package is expected to communicate with external services.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands from within the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows several low-effort and suspicious indicators, including lack of maintainer history and missing author information, suggesting potential risk.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ 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

○ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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-lens-hub

Your task is to develop a web-based mini-application called 'AI Vision Explorer' using Python and the upcoming package 'ai-lens-hub'. This application will serve as a user-friendly interface for analyzing and visualizing images through various AI lenses provided by the package. The goal is to enable users to upload images, apply different AI transformations (such as edge detection, color enhancement, or object recognition), and view the results in real-time.

### Core Features:
1. **Image Upload**: Users should be able to upload their own images to the application.
2. **AI Lens Selection**: Provide a dropdown menu where users can select from a variety of AI lenses available in 'ai-lens-hub'. Each lens represents a different AI transformation or analysis technique.
3. **Real-Time Visualization**: As users select different AI lenses, the application should instantly display the transformed image on the screen.
4. **Detailed Analysis Output**: For certain lenses, provide a detailed analysis output such as detected objects, their confidence scores, or any other relevant data.
5. **User-Friendly Interface**: Ensure the UI is clean, intuitive, and easy to navigate.
6. **Saving Results**: Allow users to save the transformed images back to their local storage.

### Utilization of 'ai-lens-hub':
- Use 'ai-lens-hub' to load and apply various AI transformations to the uploaded images. The package should support a wide range of operations including but not limited to edge detection, color correction, object recognition, and more.
- Integrate the package’s API into your backend logic so that when a user selects an AI lens, the corresponding transformation is applied to the uploaded image.
- Leverage 'ai-lens-hub' documentation and examples to understand how to use its functions effectively within your application.

### Additional Enhancements (Optional):
- Implement a feature that allows users to compare multiple AI lens transformations side-by-side.
- Incorporate a feedback mechanism where users can rate the effectiveness of each AI lens.
- Add a gallery section where users can browse popular images processed through 'ai-lens-hub'.

This project aims to showcase the versatility and power of 'ai-lens-hub' while providing an engaging and educational experience for users interested in AI image processing.