🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell execution, obfuscation, or credential harvesting patterns. However, the low metadata score due to the maintainer's single package and lack of repository engagement suggests caution and further monitoring.

Low risk scores across multiple categories
Single package from maintainer with low repository engagement

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external API interactions.
Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package and the repository lacks engagement, indicating potential low activity or newness.

🔬 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Phillip Duncan-Gelder" 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 acherion

Create a user-friendly mini-application using the Python package 'acherion' that allows users to design and execute simple data processing pipelines visually. This application will serve as a demonstration of how 'acherion' can be used to build complex workflows without writing extensive code. The application should include the following features:

1. **Graph Editor Interface**: Users should be able to drag and drop nodes representing different data processing operations (e.g., filter, transform, aggregate) onto a canvas. Connections between nodes should represent data flow.
2. **Node Library**: Provide a library of pre-defined nodes such as 'CSV Reader', 'JSON Reader', 'Filter Rows', 'Aggregate Data', 'Plot Data', etc.
3. **Real-time Execution Preview**: As users connect nodes, the application should automatically compile the graph into executable Python code and display real-time results in a separate panel.
4. **Save & Load Graphs**: Implement functionality to save the current graph configuration to a file and load it back into the editor for future use.
5. **Interactive Data Exploration**: Allow users to upload datasets directly from their local machine or URL and see immediate visualizations of the data within the application.
6. **Custom Node Creation**: Enable advanced users to create custom nodes by providing inputs and outputs through a form interface, which then gets compiled into the workflow.

Use 'acherion' to handle the embedding of the NiceGUI-based flow-graph editor, compiling the graph into Python code, and running the compiled code. Ensure the application is intuitive and provides clear feedback to users about the status of their operations.