🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some concerning characteristics such as missing repository details, a single version release, and poor metadata quality, which could suggest potential malicious intent. However, there are no direct indicators of malicious activity within the code.

missing repository
single version release
low-quality metadata

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The package shows several red flags including a missing repository, a single version release, and low-quality metadata which may indicate potential malicious intent.

🔬 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 GroqAuto

Develop a real-time sentiment analysis web application using the Python package 'GroqAuto'. This application will allow users to input text and receive immediate feedback on the overall sentiment of the text (positive, negative, neutral). The app should be designed with the following requirements and features:

1. User Interface: Create a simple, clean, and responsive web interface where users can type or paste their text.
2. Sentiment Analysis: Use the GroqAuto package to asynchronously send the user's text to a sentiment analysis model hosted on Groq's platform. Ensure that the application handles API key rotation automatically when rate limits are hit.
3. Real-Time Feedback: Display the sentiment result immediately after the user submits their text, highlighting the confidence level of the sentiment prediction.
4. Error Handling: Implement error handling to manage any issues that arise from API requests, such as connection errors or invalid inputs.
5. Logging: Include logging functionality to record all API calls and their outcomes for monitoring and debugging purposes.
6. Security: Ensure that API keys and other sensitive information are stored securely and not exposed in the code or logs.
7. Documentation: Provide comprehensive documentation explaining how to run the application, including setting up the environment and installing dependencies.
8. Deployment: Outline steps for deploying the application to a cloud service provider like AWS or Heroku.

Utilize GroqAuto's core features to streamline the development process, focusing on its ability to handle asynchronous requests and automatically rotate API keys upon hitting rate limits. This will ensure that your application remains robust and scalable even under high usage scenarios.