AI Analysis
The package shows low risk in terms of network, shell execution, obfuscation, and credential harvesting activities. However, the metadata risk score is elevated due to the absence of maintainer history and a linked GitHub repository, raising concerns about its provenance.
- Elevated metadata risk due to missing maintainer history and GitHub repository.
- Otherwise low risk profile.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- 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 package is suspicious due to lack of maintainer history and no associated GitHub repository.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (1724 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a real-time video processing application using the 'anchorr' package, which leverages high-performance Rust-backed technology for efficient video encoding and batch processing. Your goal is to develop a mini-application that allows users to upload multiple video files, apply various filters or effects in real-time, and then save the processed videos. The application should support at least three different video effects such as grayscale conversion, slow-motion, and applying a sepia tone. Step 1: Set up your development environment with Python and install the 'anchorr' package. Step 2: Design a simple user interface where users can select their video files from their local machine. Step 3: Implement functionality to process each selected video file through the chosen effect(s), utilizing the 'anchorr' package for real-time processing. Step 4: Ensure the application provides feedback to the user on the progress of the video processing. Step 5: Add an option for users to download the processed video files after completion. Features: - Support for multiple video input files. - Real-time preview of effects before final processing. - Ability to apply multiple effects sequentially or simultaneously. - User-friendly interface for selecting and applying effects. - Progress bar for tracking the processing status. - Error handling for invalid inputs or processing failures. Utilization of 'anchorr': - Use 'anchorr' for the core video processing tasks, including encoding and applying visual effects. This will ensure that your application runs efficiently even when processing large video files.
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