anchorr

v0.1.0 suspicious
4.0
Medium Risk

High-performance Rust-backed video encoding and batch processing engine

🤖 AI Analysis

Final verdict: SUSPICIOUS

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)

○ 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 (1724 chars)
○ 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 6.0

3 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)
Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with anchorr
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.

💬 Discussion Feed

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