🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk score due to insufficient maintainer effort and lack of author details raises some concerns, warranting further investigation.

Low maintainer effort indicated by metadata
Lack of author details

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, reducing risk of command injection or system compromise.
Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating no immediate risk of secret theft.
Metadata: The package shows signs of low maintainer effort and lacks a proper author description, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 22 test file(s) found

22 test file(s) detected (e.g. builders.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (6880 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

110 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

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: users.noreply.github.com>

✓ 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 aioads

Develop a real-time monitoring tool using Python's 'aioads' library for communication with Beckhoff TwinCAT PLCs. This tool will allow users to monitor various process variables in a manufacturing plant in real-time. The application should include the following features:

1. **Real-Time Data Retrieval**: Implement functionality to connect to one or more Beckhoff TwinCAT PLCs and retrieve data from specific process variables at regular intervals.
2. **Data Visualization**: Integrate a simple yet effective visualization component (such as Matplotlib or Plotly) to display the retrieved data graphically. Users should be able to select which variables to visualize.
3. **Alert System**: Develop an alert system that triggers notifications (via email or SMS) when certain variables exceed predefined thresholds.
4. **User Interface**: Create a user-friendly web interface using Flask or another lightweight framework to interact with the tool. The UI should allow users to start/stop data retrieval, configure thresholds for alerts, and select variables for visualization.
5. **Logging**: Implement logging to record all activities, including connection attempts, data retrieval times, and alert triggers, for auditing purposes.

The 'aioads' package will be utilized to establish asynchronous connections with the PLCs, allowing for efficient data retrieval without blocking other parts of the application. Ensure your code is well-documented and modular to facilitate future enhancements or modifications.