autowsgr_native

v0.2.0 suspicious
4.0
Medium Risk

Image recognition library for AutoWSGR

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell execution, obfuscation, and credential handling. However, its metadata suggests low maintenance, which raises suspicion about potential supply-chain risks.

  • Low maintenance indicated by metadata
  • Potential supply-chain risk due to lack of updates
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk of unauthorized command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintenance and potentially low effort, which may indicate some level of risk.

📦 Package Quality Overall: Low (1.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 8.0

4 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" 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 autowsgr_native 
Create a fully functional mini-application called 'AutoWSGR Art Collector' which leverages the capabilities of the 'autowsgr_native' package for image recognition within the context of the AutoWSGR game environment. This application aims to help players efficiently manage and collect unique in-game art pieces. Here’s a detailed breakdown of the application's functionality and features:

1. **Image Recognition**: Utilize the 'autowsgr_native' package to identify specific in-game art pieces from screenshots taken directly within the game. The app should be able to recognize different styles, artists, and types of artwork.

2. **Database Integration**: Implement a local SQLite database to store information about recognized art pieces. Each entry should include details such as the art piece's name, artist, style, and a timestamp when it was identified.

3. **User Interface**: Develop a simple yet intuitive GUI using Python’s Tkinter library. The UI should allow users to upload game screenshots, view recognized art pieces, and manage their collection.

4. **Art Collection Management**: Provide functionalities within the application to categorize and organize the collected art pieces based on various criteria (e.g., artist, style, date of discovery).

5. **Export Functionality**: Allow users to export their art collection data into a CSV file for backup or sharing purposes.

6. **Notification System**: Implement a notification system that alerts users whenever a new art piece is successfully identified and added to their collection.

The 'autowsgr_native' package will primarily be used for its advanced image recognition algorithms tailored specifically for the AutoWSGR game environment. Users should be able to take a screenshot during gameplay, upload it through the application, and have the app automatically detect and categorize any new art pieces found. Additionally, the application should continuously update its recognition capabilities by periodically checking for updates or new models provided by the 'autowsgr_native' package.

💬 Discussion Feed

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