🤖 AI Analysis

Final verdict: SAFE

The package is assessed as safe due to low risk scores across all categories, with no indications of malicious activities or supply-chain attacks.

No network calls detected.
No shell execution patterns found.

Per-check LLM notes

Network: No network calls detected, which is typical for a package focused on reading ADI files without external dependencies.
Shell: No shell execution patterns detected, indicating the package does not execute external commands, which is expected for a file reader utility.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package, which might indicate a new or less active developer.

🔬 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

Repository JimHokanson/adinstruments_sdk_python appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Jim Hokanson" 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 adi-reader

Create a mini-application that reads and visualizes LabChart recorded data using the 'adi-reader' Python package. Your application should allow users to upload an ADI file, parse it using 'adi-reader', and then display key metrics and plots of the data in a user-friendly interface. Here’s a step-by-step guide on how to approach this project:

1. **Setup Environment**: Install necessary packages including 'adi-reader', pandas, matplotlib, and streamlit.
2. **Data Parsing**: Use 'adi-reader' to load the ADI file into your application. Explore the package documentation to understand how to handle different types of data within the file.
3. **Data Visualization**: Utilize matplotlib or another plotting library to visualize the parsed data. Consider plotting multiple channels from the ADI file as line graphs.
4. **User Interface**: Build a simple UI using Streamlit where users can upload their ADI files. Ensure the UI clearly displays any errors if the file cannot be read.
5. **Advanced Features**: Implement additional features such as filtering options for specific data points, zooming into particular sections of the plot, or saving the visualizations as images.
6. **Testing & Documentation**: Thoroughly test your application with various ADI files to ensure reliability. Document your code well and provide instructions on how to run the application.

Remember to leverage 'adi-reader' effectively by exploring its full capabilities and integrating them into your application design.