AI Analysis
The package shows minimal signs of potential malicious activity with very low scores for obfuscation and credential risks. The metadata risk is slightly elevated due to the author's limited history on PyPI, but this alone does not suggest a supply-chain attack.
- Low obfuscation risk
- Low credential risk
- Slightly elevated metadata risk due to limited author history
Per-check LLM notes
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author 'Aspose' has only one package on PyPI, which could indicate a new or less active account but no other red flags were raised.
Package Quality Overall: Low (3.6/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Documentation URL: "Docs" -> https://docs.aspose.com/psd/python-net/Detailed PyPI description (8872 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Limited contributor diversity
1 unique contributor(s) across 27 commits in aspose-psd/Aspose.PSD-for-Python-NETSingle author but highly active (27 commits)
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
Repository aspose-psd/Aspose.PSD-for-Python-NET appears legitimate
1 maintainer concern(s) found
Author "Aspose" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Develop a Python-based image manipulation tool named 'PhotoMash' that leverages the Aspose.PSD library to handle complex operations on Adobe Photoshop PSD and PSB files. This tool will enable users to perform basic editing tasks such as resizing, cropping, rotating images, and also more advanced functions like merging layers, extracting specific layers, and converting between PSD and PNG formats. Additionally, the application should allow users to apply simple filters (e.g., grayscale conversion) and save the edited images back into PSD or PSB formats. Step 1: Setup your development environment by installing Python and the Aspose.PSD package via pip. Step 2: Design a user-friendly command-line interface (CLI) that accepts input from the user regarding the file path of the PSD/PSB image they wish to edit. Step 3: Implement functionality to load the specified PSD/PSB file into memory using Aspose.PSD. Step 4: Add commands to resize, crop, and rotate the loaded image based on user inputs. Ensure these operations are non-destructive and allow for easy undoing. Step 5: Introduce options for layer management, including the ability to merge layers, extract specific layers into separate images, and manage layer visibility. Step 6: Include a feature to convert the entire PSD/PSB file into a PNG format, preserving all layers as individual PNG files if possible. Step 7: Provide a mechanism to apply simple filters such as converting the image to grayscale or applying a sepia tone effect. Step 8: Allow users to save their edited PSD/PSB file back to disk, ensuring all changes made through the CLI are preserved. Throughout the development process, utilize Aspose.PSD's extensive documentation and examples to understand how to interact with PSD and PSB files effectively. This project aims to demonstrate the power of Aspose.PSD for handling complex image editing tasks without requiring Adobe Photoshop.
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