🤖 AI Analysis

Final verdict: SAFE

The package appears to be a legitimate utility library for database access, S3 interactions, and data processing. There is no evidence of malicious activity or supply-chain attack based on the provided information.

Package provides clear functionality descriptions
No specific issues or warnings from previous checks

🔬 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: inilab.dk>

✓ 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 3phi-framework

Imagine you are tasked with developing a real-time sentiment analysis tool using the '3phi-framework' Python package. This tool will analyze social media posts in real time to determine the sentiment of the content, categorizing it as positive, negative, or neutral. The application will be named 'SentimentSentry'. Here’s a detailed breakdown of what the app should achieve and how you can utilize the '3phi-framework' package:

1. **Real-Time Data Fetching:** SentimentSentry will connect to popular social media APIs (Twitter, Facebook, etc.) to fetch live data streams. Use the '3phi-framework' to manage these connections efficiently.
2. **Text Preprocessing:** Before sentiment analysis, preprocess the fetched text data to remove noise such as URLs, special characters, and unnecessary words. Utilize the preprocessing capabilities provided by '3phi-framework' for this purpose.
3. **Sentiment Analysis:** Implement a machine learning model within the '3phi-framework' to classify the preprocessed text into positive, negative, or neutral sentiments. Ensure the model is trained on a large dataset to ensure accuracy.
4. **Visualization:** Display the sentiment analysis results in real-time using charts and graphs. Integrate visualization libraries supported by '3phi-framework' to create dynamic visualizations that update as new data comes in.
5. **Alert System:** If the sentiment analysis indicates a significant shift towards negativity, trigger an alert via email or SMS. Use the alert functionalities provided by '3phi-framework' to set up these notifications.
6. **User Interface:** Develop a simple but effective web-based UI where users can see the live sentiment trends and interact with the application. Use the web development tools available in '3phi-framework' to build this interface.
7. **Security Measures:** Ensure all API keys and sensitive information are securely stored and accessed using '3phi-framework' security modules.
8. **Documentation & Deployment:** Provide comprehensive documentation for setting up the environment and deploying the application. Use the deployment guides and tools within '3phi-framework' to facilitate easy deployment.

By following these steps and utilizing the robust features of '3phi-framework', you'll be able to develop a powerful and user-friendly sentiment analysis tool that provides valuable insights from social media data.