🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of risk with no evidence of malicious intent. It has a new and possibly less established metadata profile but does not exhibit any dangerous behaviors.

Low network, shell, obfuscation, and credential risks
Minimal metadata information and no associated GitHub repository

Per-check LLM notes

Network: The network call patterns indicate the use of HTTP client libraries for testing purposes, which is common but should be reviewed to ensure proper usage.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package appears to be newly created with minimal information and no associated GitHub repository, indicating potential low effort or inactivity.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

Test runner config found: pyproject.toml
7 test file(s) detected (e.g. test_analytics_client.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1678 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

42 type-annotated function signatures detected in source

○ 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 score 7.5

Found 5 network call pattern(s)

self._client = http_client or httpx.Client(timeout=timeout) def close(self) -> None: if se
vk-test", http_client=httpx.Client(transport=httpx.MockTransport(handler)), ) assert c
ontrol_characters(): with httpx.Client(transport=httpx.MockTransport(lambda _: httpx.Response(200))
/api/v1", http_client=httpx.Client(transport=httpx.MockTransport(handler)), ) assert c
/api/v1", http_client=httpx.Client(transport=httpx.MockTransport(handler)), ) result =

✓ 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 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Alephant AI" 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 alephantai

Create a real-time analytics dashboard using the 'alephantai' Python package. This dashboard will allow users to monitor and analyze data from various sources in real time, providing insights into user behavior, system performance, and more. Your task is to develop a mini-application that integrates the Alephant Gateway to fetch and display analytics data dynamically. Here’s a step-by-step guide on how to approach this project:

1. **Set Up Your Environment**: Begin by setting up your development environment. Install Python and the necessary packages including 'alephantai'. Ensure you have access to an Alephant Gateway instance.
2. **Initialize the Project**: Create a new Python project and initialize it with a virtual environment.
3. **Fetch Data**: Use 'alephantai' to connect to the Alephant Gateway and fetch analytics data. This could include metrics such as user engagement, page views, error rates, etc.
4. **Real-Time Updates**: Implement functionality within your application to periodically fetch updates from the Alephant Gateway and refresh the displayed data in real time.
5. **Visualize Data**: Utilize a library like Matplotlib or Plotly to visualize the fetched data in meaningful charts and graphs. Ensure the visualizations are interactive and easy to understand.
6. **User Interface**: Develop a simple but intuitive user interface using a web framework like Flask or Django. This UI should allow users to select which data they want to view and customize their dashboard layout.
7. **Security Considerations**: Since your application will be interacting with sensitive data, ensure proper authentication and authorization mechanisms are in place. Use secure methods to handle API keys and other credentials.
8. **Testing**: Thoroughly test your application to ensure all components work seamlessly together. Check for any potential bugs or issues that might arise during real-time data fetching and visualization.
9. **Documentation**: Write clear documentation explaining how to set up and use your application. Include instructions for deploying the application to a server or cloud service.

Some suggested features to enhance your application could include advanced filtering options, historical data comparison, customizable alert notifications based on specific conditions, and integration with external services for exporting data.

By following these steps and incorporating these features, you'll create a powerful yet accessible tool for monitoring and analyzing real-time data using the 'alephantai' package.