🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious behavior such as network calls, shell execution, or obfuscation. However, the metadata risk score is high due to the lack of detailed information and the recent creation date, raising suspicion.

Metadata risk is high due to limited details provided.
Package is newly created with minimal history.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
Shell: No shell execution detected, indicating no immediate risk of command injection or unauthorized system access.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting the package is not attempting to steal secrets.
Metadata: The repository and package are newly created with minimal information provided by the author, raising concerns about potential malicious intent.

📦 Package Quality Overall: Low (3.8/10)

○ 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 (27738 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

14 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 16 commits in hankimis/ai-design-tells
Two distinct contributors found

🔬 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: iovstudio.kr>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository created very recently: 5 day(s) ago (2026-06-01T16:56:33Z)

Repository created very recently: 5 day(s) ago (2026-06-01T16:56:33Z)

⚠ Maintainer History score 6.0

3 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)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ai-design-tells

Create a web-based mini-app that evaluates the 'AI-generated design look' of images uploaded by users. The app should use the 'ai-design-tells' Python package to analyze images and provide feedback on their similarity to AI-generated designs. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Install Python and necessary libraries including Flask for the web framework and 'ai-design-tells' for AI design analysis.
2. **Design Layout**: Develop a simple yet user-friendly interface where users can upload an image file.
3. **Image Processing**: Integrate the 'ai-design-tells' package to process uploaded images and detect the presence of AI-generated design characteristics using its built-in 'Tell Score detector'.
4. **Feedback Generation**: Based on the detected characteristics, generate a report that includes a summary of which 'tells' were found in the image and the overall 'Tell Score'. This score should reflect how closely the image resembles AI-generated designs.
5. **Interactive Features**: Add interactive elements such as tooltips or additional information about each detected 'tell', enhancing user understanding.
6. **Server Integration**: Utilize the MCP server component of 'ai-design-tells' to handle requests more efficiently if needed, especially during high traffic periods.
7. **Testing & Optimization**: Test the application thoroughly, focusing on performance and accuracy of detection. Optimize the code for better efficiency and user experience.
8. **Deployment**: Deploy the application using a cloud service provider like AWS or Heroku, ensuring it’s accessible to a wide audience.

By following these steps, you will create a valuable tool that helps designers understand and identify AI-generated designs in their work.