astar-utils

v0.5.3 safe
4.0
Medium Risk

Contains commonly-used utilities for AstarVienna's projects.

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks with no network calls, shell executions, obfuscations, or credential harvesting activities detected. However, the metadata risk score is slightly elevated due to the maintainer's new or inactive account and lack of community engagement.

  • No network calls detected
  • Repository lacks community engagement
  • Maintainer has a new or inactive account
Per-check LLM notes
  • Network: No network calls detected, which is normal for utility packages.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account and the repository lacks community engagement.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Detailed PyPI description (2939 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

  • 34 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 100 commits in AstarVienna/astar-utils
  • Active community — 5 or more distinct 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: univie.ac.at

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Fabian Haberhauer" 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 astar-utils 
Create a pathfinding visualization tool using the Python package 'astar-utils'. This tool will allow users to interactively design a grid-based map, add obstacles, and compute the shortest path between two points using the A* algorithm. Here’s a step-by-step guide on how to develop this application:

1. **Setup**: Begin by setting up your Python environment. Ensure you have the 'astar-utils' package installed, which provides essential utilities for pathfinding tasks.
2. **UI Design**: Design a simple graphical user interface (GUI) where users can draw a grid and mark cells as obstacles. You might consider using a library like Tkinter for this purpose.
3. **Grid Creation**: Implement functionality within your application that allows users to create a customizable grid of any size. Users should be able to specify dimensions and obstacle placements through the GUI.
4. **Pathfinding Functionality**: Integrate 'astar-utils' into your application to handle the A* algorithm calculations. When the user selects a start and end point on the grid, your app should use 'astar-utils' to find the optimal path from start to finish, taking obstacles into account.
5. **Visualization**: After computing the path, display it visually on the grid. Highlight the path in a distinct color to make it easily identifiable.
6. **Interactive Features**: Enhance user experience by adding features such as the ability to clear the grid, reset the grid, or recompute paths with different settings.
7. **Testing & Debugging**: Thoroughly test your application to ensure that the pathfinding works correctly under various conditions. Pay special attention to edge cases and large grids.
8. **Documentation**: Write clear documentation explaining how to install your application, how to use its features, and how 'astar-utils' contributes to its functionality.

By completing this project, you will not only gain practical experience with pathfinding algorithms but also learn how to effectively utilize third-party libraries in your Python applications.

💬 Discussion Feed

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