🤖 AI Analysis

Final verdict: SUSPICIOUS

The CHAMP-tool package has a moderate risk score due to its newness and limited metadata, which raises concerns about its origins and intentions.

Minimal package activity
Limited metadata details

Per-check LLM notes

Metadata: The package appears to be newly created with minimal activity and metadata, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

", xtc_file] result = subprocess.run(cmd_check, capture_output=True, text=True) if "Last
ur compact -pbc none" subprocess.run(cmd_trjconv_1, shell=True, check=True) print(f"~~~~ STE
= "1\n0\n" process = subprocess.Popen(cmd_trjconv_2, stdin=subprocess.PIPE, stdout=subprocess.PIPE
ndex}\n1\n" process = subprocess.Popen(cmd_gmx_spatial, stdin=subprocess.PIPE, stdout=subprocess.PI
nd" try: subprocess.run(cmd, shell=True, check=True, env=env) except subproc
ile, "w") as log: subprocess.run(cmd, stdout=log, stderr=subprocess.STDOUT, check=False)

✓ 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

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 "David Sotillo Núñez" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with CHAMP-tool

Create a Python-based mini-application named 'MembraneProteinAnalyzer' that leverages the CHAMP-tool package to detect and analyze potential hotspots of membrane components around proteins. This application will serve as a user-friendly tool for researchers studying protein-membrane interactions. Here are the key steps and features for building this application:

1. **Setup**: Begin by installing the necessary Python packages including CHAMP-tool, along with any dependencies it requires. Ensure that your environment is set up correctly for scientific computing.
2. **Data Input**: Design a simple interface where users can upload protein structure files (e.g., PDB format). The application should validate these inputs to ensure they are compatible with CHAMP-tool.
3. **Hotspot Detection**: Utilize CHAMP-tool's core functionalities to process the uploaded protein data and identify potential hotspots of membrane components around the protein. Implement error handling to manage any issues during processing.
4. **Visualization**: Integrate visualization capabilities into the application. Users should be able to view the detected hotspots in a 3D model, alongside the original protein structure. Consider using libraries such as Matplotlib or Plotly for enhanced visual representation.
5. **Report Generation**: After processing, the application should generate a detailed report summarizing the findings. Include information on the location, size, and significance of each hotspot identified. Provide options for users to export these reports in PDF or HTML formats.
6. **Interactive Exploration**: Allow users to interactively explore the hotspots within the 3D model. Features could include zooming, rotating, and selecting specific regions for more detailed analysis.
7. **Customization Options**: Offer customization options for the analysis parameters, such as setting thresholds for hotspot detection, or choosing different types of membrane components to focus on.
8. **User Interface**: Develop a clean, intuitive user interface using a framework like PyQt or Tkinter, ensuring that the application is accessible and easy to use for non-technical users.
9. **Documentation**: Provide comprehensive documentation detailing how to install and use the application, as well as a guide on interpreting the results generated by MembraneProteinAnalyzer.
10. **Testing & Validation**: Conduct thorough testing of the application to ensure accuracy and reliability of the hotspot detection process. Validate results against known datasets to establish credibility.

By following these guidelines, you'll create a powerful yet accessible tool for researchers interested in exploring protein-membrane interactions.