arborize

v7.4.0 suspicious
4.0
Medium Risk

Write descriptions for NEURON cell models in an Arbor-like manner for both the Arbor and

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a low risk for common security issues like network calls, shell execution, obfuscation, and credential harvesting. However, metadata concerns such as missing maintainer information and a single associated package raise suspicion, warranting further investigation.

  • Lack of maintainer information
  • Single associated package
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has some red flags such as lack of maintainer information and a single associated package, but no clear signs of typosquatting or malicious intent.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (685 chars)
○ 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

  • 49 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

Email domain looks legitimate: alexandria.sc>

✓ 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 arborize 
Create a mini-application named 'NeuroSimulator' using Python and the 'arborize' package. This application will allow users to design and simulate simple neuronal models based on the Arbor framework, but it will also provide an easy-to-use interface for beginners to understand and experiment with these models. The app should have the following core functionalities:

1. **Model Creation**: Users should be able to input parameters such as neuron type, number of compartments, and connectivity details to create a basic model. The 'arborize' package will be used here to define these models in a way that is compatible with both Arbor and NEURON.

2. **Simulation Execution**: Once a model is created, users should be able to run simulations. The application should handle the execution process internally and display the results.

3. **Visualization**: After running a simulation, the app should visualize the output, showing things like voltage traces over time or synaptic activity. This could be done using matplotlib or a similar plotting library.

4. **Interactive Adjustment**: Allow users to tweak parameters during runtime and see the effects on the simulation instantly. For example, changing the stimulation frequency or adjusting the strength of synaptic connections.

5. **Documentation and Help**: Provide a comprehensive help section explaining the basics of neuronal modeling, the specific parameters available, and how they affect the model behavior.

Additionally, consider adding advanced features such as:
- Saving and loading model configurations.
- Comparing different models side-by-side.
- Exporting simulation results to CSV or other file formats.

This project aims to bridge the gap between complex computational neuroscience tools and novice users by leveraging the simplicity and power of the 'arborize' package.

💬 Discussion Feed

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