🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity, with very low risks across all checked categories.

No network calls
No shell executions
No obfuscation
No credential harvesting

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell executions detected, indicating the package does not perform system-level operations.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

🔬 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: example.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

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)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ML-LAB-NIE-EXM

Create a command-line utility named 'CaseSwitcher' that leverages the 'ML-LAB-NIE-EXM' package to demonstrate the power and flexibility of switch-case logic in Python. This utility will serve as a versatile tool for developers looking to quickly test switch-case scenarios without needing to write boilerplate code.

Step 1: Set up the environment
- Ensure 'ML-LAB-NIE-EXM' is installed in your Python environment. If not, guide the user on how to install it via pip or from source.

Step 2: Design the CLI Interface
- Implement a clean and intuitive command-line interface that allows users to input a case value and a corresponding action.
- Users should be able to specify multiple cases and actions, which will be processed according to the switch-case logic provided by 'ML-LAB-NIE-EXM'.

Step 3: Utilize 'ML-LAB-NIE-EXM'
- Use the switch-case functionality within 'ML-LAB-NIE-EXM' to handle the logic for each case-action pair.
- For example, if a user inputs 'case1', 'case2', and 'case3' along with their respective actions, the utility should use the switch-case logic to execute the correct action based on the input case.

Step 4: Add Advanced Features
- Include options for default actions when no specific case matches the input.
- Allow users to define custom actions beyond just predefined ones, enabling dynamic behavior.
- Implement error handling to gracefully manage incorrect inputs or unexpected conditions.

Step 5: Documentation and User Guide
- Provide comprehensive documentation on how to use 'CaseSwitcher', including examples of various use cases.
- Create a user guide that explains the installation process, basic usage, and advanced configuration options.

The goal of this project is to showcase the simplicity and effectiveness of using 'ML-LAB-NIE-EXM' for implementing switch-case logic in Python applications, making it easier for developers to create flexible and robust command-line utilities.