🤖 AI Analysis

Final verdict: SAFE

The ai-tooling package poses minimal risk based on the analysis, with no detected network calls, shell executions, or obfuscation. The metadata risk is slightly elevated due to the maintainer having only one package.

Low network and shell risk
No signs of obfuscation or credential harvesting
Maintainer has only one package

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 no direct system command execution from the package.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags were identified.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (1143 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

5 type-annotated function signatures (partial)

○ 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 2.0

1 maintainer concern(s) found

Author "Evin Dunn" 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-tooling

Create a fully-functional mini-app named 'AI-FileClassifier' using the Python package 'ai-tooling'. This app will be designed to classify files into different categories based on their content, such as identifying emails, documents, images, etc., and then organizing them accordingly. Here’s a detailed breakdown of what your app should do and how you can use 'ai-tooling' to achieve these functionalities:

1. **Setup**: Begin by setting up a virtual environment for your project and installing the 'ai-tooling' package along with any other necessary dependencies like Flask for the web interface.
2. **User Interface**: Develop a simple yet intuitive user interface where users can upload files. Ensure that the UI supports multiple file uploads at once.
3. **File Classification**: Utilize 'ai-tooling' to train a model that can classify uploaded files into predefined categories such as 'Text Documents', 'Images', 'Spreadsheets', 'Presentations', and 'Emails'. The package should offer tools for training models on custom datasets which you can prepare from various file types.
4. **Model Training**: Use 'ai-tooling' to train your model on a dataset containing examples of each file type. This dataset should include a variety of file contents to ensure accurate classification.
5. **Classification Process**: When a user uploads files, the app should automatically process these files through the trained model to determine their category.
6. **Organizing Files**: After classifying the files, the app should organize them into separate folders corresponding to their identified categories.
7. **Feedback Loop**: Implement a feedback mechanism where users can correct the classification if the app misclassifies a file. This feedback should be used to improve the model over time.
8. **Reporting**: Provide a report feature that shows statistics about the classified files, such as the number of files in each category and the accuracy of the classifications.

By following these steps and utilizing the capabilities provided by 'ai-tooling', you'll create a powerful and user-friendly tool for file management and organization.