🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the incomplete metadata and lack of a GitHub repository suggest potential unreliability, raising suspicion about its origin and maintenance.

Incomplete author details
No associated GitHub repository

Per-check LLM notes

Network: No network calls detected, which is normal for a package not requiring external communications.
Shell: No shell executions detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has no associated GitHub repository and the author details are incomplete, indicating potential unreliability.

🔬 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: 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 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 affine-mpc

Create a Python-based mini-application that simulates a simple robotic arm control system using the 'affine-mpc' package. This application will allow users to input desired positions for the robotic arm's end-effector and compute optimal trajectories for the joints to achieve these positions within specified time constraints, ensuring smooth and efficient movement while avoiding obstacles.

Step 1: Set up the environment
- Ensure Python is installed on your system.
- Install the required packages, including 'affine-mpc', 'numpy', and 'matplotlib'.
- Import necessary modules from 'affine-mpc' for model predictive control and trajectory generation.

Step 2: Define the Robotic Arm Model
- Use 'affine-mpc' to define a simplified 2D or 3D robotic arm model with revolute joints.
- Specify the kinematic and dynamic parameters such as link lengths, masses, and inertias.

Step 3: Implement Obstacle Avoidance
- Integrate collision detection logic to ensure the robotic arm avoids predefined obstacles in its workspace.
- Utilize 'affine-mpc' functionalities to adjust the trajectory planning algorithm to account for these constraints.

Step 4: User Interface Design
- Develop a basic command-line interface where users can input target positions and observe the calculated joint angles over time.
- Optionally, create a simple graphical user interface (GUI) using 'tkinter' or another suitable library to visualize the robotic arm's motion in real-time.

Step 5: Test and Optimize
- Test the application with various target positions and obstacle configurations.
- Fine-tune the parameters of the MPC controller to improve performance and responsiveness.

Suggested Features:
- Real-time plotting of the robotic arm's path and joint angles using 'matplotlib'.
- Ability to save and load different robotic arm configurations and scenarios.
- Integration of a physics engine like 'PyBullet' for more realistic simulations.

How 'affine-mpc' is Utilized:
- The 'affine-mpc' package is central to defining the model predictive control strategy for the robotic arm.
- It provides tools for formulating optimization problems related to trajectory planning and constraint handling.
- Users will interact with functions from 'affine-mpc' to calculate joint movements that satisfy the desired end-effector position and avoid collisions.