asynccpu

v1.4.0 safe
4.0
Medium Risk

Supports async / await pattern for CPU-bound operations.

🤖 AI Analysis

Final verdict: SAFE

The package has low risks associated with network calls, obfuscation, and credential handling. However, it uses Popen with shell=True, which poses a moderate risk if input is not sanitized properly. The incomplete maintainer's author information slightly raises the metadata risk.

  • Moderate shell risk due to use of Popen with shell=True
  • Incomplete maintainer's author information
Per-check LLM notes
  • Network: No network calls detected, which is not unusual and doesn't indicate risk.
  • Shell: Use of Popen with shell=True can be risky if not properly sanitized, potentially allowing execution of arbitrary commands.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author information is incomplete, suggesting potential lack of transparency or a new, less established account.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: pyproject.toml
  • 5 test file(s) detected (e.g. test_process_cpu_bound.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (9590 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 50 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 84 commits in yukihiko-shinoda/asynccpu
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • ) with Popen(command, shell=True, env=env) as popen: # noqa: DUO116,RUF100,S602 # nosec B6
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository yukihiko-shinoda/asynccpu appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 asynccpu 
Create a Python-based utility called 'AsyncImageProcessor' that leverages the 'asynccpu' package to handle CPU-bound image processing tasks asynchronously. This tool will allow users to perform batch operations on multiple images, such as resizing, converting formats, applying filters, and saving the processed images to disk. The application should be designed with a user-friendly command-line interface (CLI) for ease of use.

### Core Features:
1. **Batch Image Processing:** Users should be able to specify a directory containing images and a set of processing instructions. The utility will process all images in the specified directory according to these instructions.
2. **Asynchronous Execution:** Utilize the 'asynccpu' package to ensure that each image processing task runs asynchronously, improving overall performance when dealing with large numbers of images.
3. **Customizable Filters:** Implement at least three different image filters (e.g., grayscale conversion, brightness adjustment, and blur effect). Users should be able to select which filters to apply during the processing.
4. **Progress Tracking:** Display a progress bar or percentage completion indicator to show the user how much of the batch processing has been completed.
5. **Error Handling:** Gracefully handle errors such as invalid file paths, unsupported image formats, or issues during the processing phase.
6. **Output Directory Management:** Allow users to specify an output directory where the processed images will be saved. If no output directory is provided, save the images in a default location.

### Implementation Steps:
1. **Setup Project Environment:** Initialize a new Python project, install necessary packages including 'asynccpu', and set up a virtual environment.
2. **CLI Design:** Develop a command-line interface that accepts arguments for input directory, output directory, and processing options.
3. **Image Processing Functions:** Write functions using 'asynccpu' to asynchronously handle image resizing, format conversion, and applying filters.
4. **Integration with asynccpu:** Use 'asynccpu' to wrap your image processing functions so they can be executed concurrently without blocking the main thread.
5. **Testing:** Thoroughly test the application with various inputs and edge cases to ensure reliability and robustness.
6. **Documentation:** Provide clear documentation on how to install, configure, and run the 'AsyncImageProcessor'.

This project aims to showcase the power of asynchronous programming in handling CPU-intensive tasks efficiently, making it a valuable tool for anyone needing to process large batches of images quickly.

💬 Discussion Feed

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