🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators, with no evidence of malicious activity. However, the metadata risk due to the maintainer's account status suggests some caution is warranted.

Low network, shell, obfuscation, and credential risks.
Maintainer's metadata raises slight concerns.

Per-check LLM notes

Network: Network calls are expected if the package is designed to interact with external services or APIs.
Shell: No shell execution patterns detected, indicating low risk.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate potential unreliability.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

.headers) response = requests.get(endpoint, params=params, headers=headers) response.r

✓ 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

Repository acryldata/datahub-actions appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

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 acryl-datahub-actions

Your task is to develop a real-time data monitoring tool using the 'acryl-datahub-actions' Python package. This tool will allow users to monitor and react to changes in their DataHub metadata in real-time. Here’s a step-by-step guide on how to create this tool:

1. **Project Setup**: Start by setting up a new Python environment. Install the required packages including 'acryl-datahub-actions'.
2. **Authentication & Configuration**: Configure your application to authenticate with DataHub and set up necessary configurations.
3. **Real-Time Change Detection**: Utilize 'acryl-datahub-actions' to listen for real-time changes in DataHub metadata. Implement logic to capture these changes and store them temporarily.
4. **Notification System**: Develop a notification system that alerts users via email or SMS when significant changes occur. For example, if a dataset is marked as deprecated or if a new dataset is added.
5. **Dashboard Creation**: Create a simple web-based dashboard where users can view recent changes and manage notifications. Use Flask or Django for backend and React or Vue.js for frontend.
6. **Testing & Deployment**: Thoroughly test your application to ensure it accurately captures and reacts to changes. Deploy the application on a cloud service like AWS or Heroku.

Suggested Features:
- User management and authentication
- Customizable notification preferences
- Historical change logs
- Integration with other services like Slack for notifications

The 'acryl-datahub-actions' package will be central to your project, enabling you to efficiently interact with DataHub's real-time change streams. Ensure you explore its documentation thoroughly to understand all available functionalities.