AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity, with low scores across all specific risk categories. The only notable concern is the metadata risk due to the maintainer having just one package.
- No network calls detected
- Maintainer has only one package
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other suspicious activities are flagged.
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 2.0
1 maintainer concern(s) found
Author "AK" 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 ak-robotics-vision-2026-final
Develop a real-time object tracking and classification system using the 'ak-robotics-vision-2026-final' package. This application will capture video feeds from a webcam and perform two primary functions: track moving objects within the frame and classify them into predefined categories such as vehicles, animals, or humans. Utilize the Markov chains for predicting the movement patterns of objects, least-squares learning for improving the accuracy of object classification over time, and advanced image processing techniques for feature extraction and object recognition. Steps to implement: 1. Initialize the application by setting up the webcam feed and importing necessary modules from 'ak-robotics-vision-2026-final'. 2. Implement a Markov chain model to analyze the historical positions of detected objects to predict their future locations. 3. Use least-squares learning to train a classifier on a dataset of labeled images representing different categories of objects. 4. Apply image processing techniques to segment objects from the background and extract relevant features for classification. 5. Integrate the prediction and classification models to continuously update the tracking and categorization of objects in real-time. 6. Visualize the results by overlaying bounding boxes and labels on the live video feed to indicate the tracked objects and their classifications. 7. Enhance the user experience by allowing users to manually input new object categories and train the system on-the-fly. Features: - Real-time video capture and processing - Object detection and tracking using Markov chains - Object classification with continuous learning via least-squares methods - Interactive user interface for viewing tracking data and classifications - User-driven training capabilities for new object types The 'ak-robotics-vision-2026-final' package will be crucial for implementing the Markov chains for predictive analysis, least-squares learning algorithms for adaptive classification, and comprehensive image processing tools for efficient feature extraction.