🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious behavior or unusual activities that would indicate a supply-chain attack. All checks point to a benign package.

No network calls detected
No shell execution detected

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution detected, indicating the package does not execute external commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

16 test file(s) detected (e.g. test_attr_promise.py)

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ 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

180 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

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 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 alizarin

Create a desktop application named 'TreeConverter' using Python that leverages the 'alizarin' package to convert between different tree data structures such as binary trees, ternary trees, and n-ary trees. This application will serve as a tool for developers and data scientists who work extensively with tree structures in their projects.

The application should have a simple yet intuitive graphical user interface (GUI) built with Tkinter. It should allow users to input a tree structure in various formats (e.g., adjacency lists, adjacency matrices, or serialized strings) and then select the type of tree they want to convert it into from a dropdown menu.

Key Features:
1. **Input Parsing**: Implement robust parsers to accept tree inputs in multiple formats.
2. **Conversion Engine**: Utilize the 'alizarin' package to handle the core logic of converting one tree structure to another.
3. **Visualization**: Provide a visual representation of both the original and converted trees within the GUI.
4. **Error Handling**: Include comprehensive error handling to manage invalid inputs and conversion errors gracefully.
5. **Save/Load Functionality**: Allow users to save their tree structures and conversions to files for later use.
6. **Help Documentation**: Integrate a help section within the application that explains common tree structures and provides examples.
7. **Testing Suite**: Develop a set of unit tests to ensure the correctness of the conversion logic implemented with 'alizarin'.

Detailed Steps:
1. **Setup Project Structure**: Create a new Python virtual environment and install necessary packages including 'alizarin', 'Tkinter', and any additional libraries you deem necessary.
2. **Design the GUI**: Sketch out the layout of the GUI components including input fields, dropdown menus, buttons, and visualization areas.
3. **Implement Input Parsers**: Write functions that can parse tree structures from different input formats and validate them.
4. **Integrate 'alizarin' Package**: Use 'alizarin' to implement the conversion logic between different types of tree structures.
5. **Develop Visualization Module**: Create a module that can render trees visually based on their node connections.
6. **Add Save/Load Features**: Implement functionality to serialize/deserialize tree structures and conversions.
7. **Test Thoroughly**: Run your tests to ensure all parts of the application function correctly.
8. **Finalize Application**: Polish the UI, add finishing touches like tooltips and help sections, and prepare the application for deployment.