🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks with no network calls, shell executions, or credential harvesting attempts. However, the newness of the maintainer slightly increases the metadata risk.

No network calls
No shell execution
New maintainer

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution detected, indicating the package does not perform system-level commands that could be exploited.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer seems to be new and has not released other packages, which could indicate a lack of established trust.

📦 Package Quality Overall: Low (3.4/10)

○ 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 (1338 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

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 54 commits in michel4j/ai-center
Small but multi-author team (3–4 contributors)

🔬 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: lightsource.ca

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository michel4j/ai-center appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Michel Fodje" 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 ai-center

Create a fully-functional mini-application named 'SampleAligner' that leverages the 'ai-center' Python package to automate the alignment of samples using a YOLO model. This application will be particularly useful in laboratories where precise sample placement is crucial for experiments. Here’s a step-by-step guide on what your application should achieve:

1. **User Interface**: Develop a simple yet intuitive GUI using Tkinter or a similar library. This interface should allow users to select an image or video file containing samples that need to be aligned.
2. **Image/Video Processing**: Implement functionality within the application that processes the selected image or video. Utilize the 'ai-center' package to apply the YOLO model for object detection, identifying the samples within the image or video frame.
3. **Alignment Algorithm**: Once the samples are detected, design an algorithm within the application that calculates the necessary adjustments to align the samples correctly. This could involve calculating the center point of each detected sample and comparing it against a predefined ideal position.
4. **Adjustment Recommendations**: Display recommendations to the user on how to adjust the samples based on the analysis performed by the YOLO model. These recommendations could include visual overlays on the image or video indicating the required movements for alignment.
5. **Export Results**: Allow users to export the processed images/videos along with alignment reports. The report should detail the detected samples, their initial positions, recommended adjustments, and final aligned positions if applicable.
6. **Configuration Settings**: Provide options within the application for users to customize parameters such as sensitivity thresholds for the YOLO model, alignment tolerances, and output formats.
7. **Documentation and Help**: Include comprehensive documentation and a help section within the application to guide users through setup, usage, and troubleshooting common issues.

By following these steps, you'll create a powerful tool that not only automates the tedious task of sample alignment but also enhances accuracy and efficiency in laboratory workflows.