🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, or obfuscations detected. The metadata risk is slightly elevated due to the maintainer's single package history, but there are no other suspicious activities.

No network calls detected
Maintainer has only one package

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external resources.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other suspicious activities are detected.

📦 Package Quality Overall: Low (3.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

2 test file(s) detected (e.g. __init__.py)

◈ Medium Documentation 7.0

Some documentation present

1 documentation file(s) (e.g. conf.py)
Detailed PyPI description (7682 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ 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: alleninstitute.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Allen Institute for Neural Dynamics" 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-dynamic-foraging-population-analysis

Create a mini-application that simulates and analyzes the foraging behavior of a population over time using the 'aind-dynamic-foraging-population-analysis' package. This application will allow users to input parameters such as initial population size, food availability, and environmental factors to observe how these variables affect the population's foraging efficiency and survival rates.

Steps:
1. Begin by installing the 'aind-dynamic-foraging-population-analysis' package if it isn't already installed.
2. Design a user-friendly interface where users can input various parameters including initial population size, food distribution patterns, and environmental challenges like weather conditions or predation risks.
3. Implement a simulation engine using functions from the 'aind-dynamic-foraging-population-analysis' package to model the population's foraging behavior based on the provided inputs.
4. Integrate analytical tools from the package to calculate metrics such as average foraging success rate, population growth rate, and energy expenditure.
5. Visualize the simulation results through graphs and charts showing trends over time, such as population changes, food consumption, and foraging efficiency.
6. Allow users to run multiple simulations with different parameter sets to explore 'what-if' scenarios and understand the impact of varying environmental factors.
7. Include documentation and comments within your code to explain how each part of the application works, especially how it leverages the 'aind-dynamic-foraging-population-analysis' package.

Features:
- User Input Interface: Allows customization of simulation parameters.
- Simulation Engine: Uses 'aind-dynamic-foraging-population-analysis' to simulate population dynamics.
- Analytical Tools: Provides metrics for evaluating foraging efficiency and population health.
- Visualization: Presents simulation outcomes visually for easier interpretation.
- Multiple Scenario Testing: Facilitates comparison between different simulation setups.