🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk based on the analysis, with no indications of malicious activity or obfuscation. However, the low engagement on its Git repository and sparse maintainer information slightly increase the risk score.

No network calls or shell executions detected
Low engagement and sparse maintainer info

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network functionality.
Shell: No shell execution patterns detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has low engagement on its Git repository and the maintainer's information is sparse, suggesting potential unreliability.

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 ParUtils

Develop a fully-functional mini-application named 'AutoTestRunner' using Python that leverages the functionalities provided by the 'ParUtils' package. This application will serve as a tool for IT engineers to automate testing processes for various software components. The goal is to streamline the testing phase by providing an easy-to-use interface and robust backend support for running tests on different environments and configurations.

Step 1: Define the Core Features
- Test Case Management: Allow users to create, edit, and delete test cases. Each test case should contain details such as name, description, expected outcome, and test steps.
- Environment Setup: Provide options to configure test environments, including software versions, hardware requirements, and network settings.
- Test Execution: Implement functionality to execute test cases against specified environments. This includes scheduling tests at specific times and running them concurrently if needed.
- Reporting: Generate detailed reports after each test run, highlighting pass/fail status, execution time, and any errors encountered during the test.

Step 2: Utilizing ParUtils Package
The 'ParUtils' package will be instrumental in supporting several aspects of 'AutoTestRunner':
- Use ParUtils' support utilities to manage configuration files and settings related to test environments.
- Leverage its automation tools for automating repetitive tasks like setting up test environments and executing tests.
- Employ ParUtils' test utilities for validating the outcomes of individual test cases and generating comprehensive reports.

Step 3: Implementation Plan
1. Set up the project structure and dependencies, including installing ParUtils via pip.
2. Develop the user interface, which could be command-line based or a simple GUI, depending on the complexity and intended use.
3. Integrate ParUtils functionalities into each module of the application as per the outlined requirements.
4. Conduct thorough testing to ensure all features work as expected and generate meaningful reports.
5. Document the application, explaining how to install it, use its features, and troubleshoot common issues.

Suggested Features:
- Support for multiple test frameworks (e.g., pytest, unittest)
- Integration with CI/CD pipelines for automated testing
- Ability to export test results to CSV or JSON formats for further analysis
- User authentication and role-based access control for managing different levels of permissions