AI Analysis
The package has low immediate risks but raises concerns due to low maintainer profile and recent activity in the repository.
- Low maintainer profile
- Recent activity in the repository
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
- Shell: No shell executions detected, indicating the package does not attempt to run external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
- Metadata: The repository's recent activity and low maintainer profile raise concerns about potential malicious intent.
Package Quality Overall: Medium (5.2/10)
Partial test coverage signals detected
2 test file(s) detected (e.g. test_client.py)
Some documentation present
Documentation URL: "Documentation" -> https://aistemsplitter.org/developers/apiDetailed PyPI description (1297 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
10 type-annotated function signatures detected in source
Limited contributor diversity
2 unique contributor(s) across 3 commits in aistemsplitter/aistemsplitter-pythonTwo distinct contributors found
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
Email domain looks legitimate: aistemsplitter.org>
All external links appear legitimate
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forksAll 3 commits happened within 24 hours
2 maintainer concern(s) found
Author 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 Python-based desktop application called 'AudioMaster' that leverages the 'aistemsplitter' package to manage audio files efficiently. AudioMaster should allow users to upload multiple audio files, automatically split them into segments based on silence detection, and save these segments as separate files. Additionally, it should provide options to merge audio segments back into a single file, rename files, and delete unwanted segments. Users should also be able to preview each segment before saving changes. Implement a user-friendly GUI using PyQt5 or Tkinter to interact with the application. Utilize the 'aistemsplitter' package's core functionalities such as silence detection and segmentation to automate the process of splitting audio files. Ensure that the application supports common audio formats like MP3, WAV, and FLAC.
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