🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activities and has minimal metadata risks. It appears to be safe for use.

No network calls or shell executions detected
Maintainer has only one package, suggesting it might be a new or less active account

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell executions detected, indicating no direct system command execution from the package.
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 may indicate a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://allenneuraldynamics.github.io/Aind.Behavior.VrForagi
Detailed PyPI description (3354 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

13 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

4 unique contributor(s) across 100 commits in AllenNeuralDynamics/Aind.Behavior.VrForaging
Small but multi-author team (3–4 contributors)

🔬 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: galenlynch.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository AllenNeuralDynamics/Aind.Behavior.VrForaging appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Bruno Cruz, Tiffany Ona, Galen Lynch" 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 aind-behavior-vr-foraging-curricula

Develop a mini-application called 'ForagerSim' that simulates a virtual environment where agents perform foraging tasks based on the VR Foraging task curricula provided by the 'aind-behavior-vr-foraging-curricula' package. This application will serve as a tool for researchers and students interested in studying behavior, learning algorithms, and reinforcement learning strategies in a controlled virtual setting.

Step 1: Set up your development environment with Python and install the 'aind-behavior-vr-foraging-curricula' package.
Step 2: Design a simple graphical user interface (GUI) using a Python library such as PyQt5 or Tkinter, allowing users to select from various predefined foraging scenarios provided by the package.
Step 3: Implement functionality within the GUI that allows users to customize parameters of the foraging task, such as the size of the virtual environment, the number and types of resources available, and the difficulty level of the task.
Step 4: Integrate the selected curriculum from 'aind-behavior-vr-foraging-curricula' into your simulation, ensuring that it runs smoothly and accurately reflects the behaviors and challenges defined by the curriculum.
Step 5: Add logging capabilities to record the performance metrics of the agents over time, including metrics like efficiency, path length, and resource collection rates.
Step 6: Provide visualization tools within the GUI to display real-time statistics and agent behaviors, helping users to better understand the dynamics of the foraging process.
Step 7: Include documentation and examples for how to extend the application with custom curricula or additional behaviors, encouraging community contribution and expansion of the application's capabilities.

Suggested Features:
- Support for multiple agents with different behaviors or learning algorithms.
- Real-time adjustment of task parameters during a simulation run.
- Export of simulation data to CSV or other common file formats for further analysis.
- Integration with popular machine learning frameworks for training agents directly within the simulation.

By utilizing the 'aind-behavior-vr-foraging-curricula' package, you will ensure that your application adheres to scientifically validated and well-documented behavioral standards, making it a valuable tool for educational and research purposes.