AI Analysis
Final verdict: SUSPICIOUS
The package shows no immediate signs of malicious behavior such as network calls, shell executions, or credential harvesting. However, the low author activity and metadata quality raise concerns about the maintainability and legitimacy of the package.
- Low author activity
- Metadata quality issues
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires them for its functionality.
- Shell: No shell executions detected, indicating the package does not perform any system-level command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: Low author activity and metadata quality suggest potential low effort or new project.
Package Quality Overall: Low (4.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
1 test file(s) detected (e.g. test_active_classification_env.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (16234 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
179 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
6.0
Limited contributor diversity
2 unique contributor(s) across 100 commits in TimSchneider42/active-perception-gymTwo distinct contributors found
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: robot-learning.de>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository TimSchneider42/active-perception-gym appears legitimate
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 ap-gym
Create a mini-application that simulates a robot's environment exploration using the 'ap-gym' package. This application will allow users to interactively explore different environments, collect data, and learn about the benefits and challenges of active perception in robotics. Step 1: Setup your development environment. Ensure you have Python installed and set up a virtual environment. Install the 'ap-gym' package and any other necessary dependencies. Step 2: Define the environments. Use 'ap-gym' to create or load predefined environments where the robot can operate. These environments should simulate various conditions such as different lighting, obstacles, and terrains. Step 3: Implement the robot's perception system. Using 'ap-gym', develop a basic perception algorithm that allows the robot to gather information about its surroundings. This could include sensors like cameras or lidars, and the ability to interpret sensor data. Step 4: Develop the interaction interface. Create a user-friendly interface that allows users to control the robot's actions, view its collected data, and navigate through the simulated environments. Step 5: Integrate learning capabilities. Allow the robot to improve its perception over time based on the data it collects. Users should be able to see how the robot's performance improves with more experience. Suggested Features: - Multiple pre-defined environments to choose from. - Real-time visualization of the robot's perspective and collected data. - Adjustable parameters for the robot's sensors to simulate different conditions. - A leaderboard showing the most efficient perception algorithms developed by users. - Tutorial mode to help new users understand the basics of active perception. Utilizing 'ap-gym': - Use 'ap-gym' to define and manage the environments. - Leverage 'ap-gym's active perception tasks to implement the robot's perception logic. - Utilize 'ap-gym's simulation capabilities to provide real-time feedback and visualization.
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