ayase

v0.1.53 safe
3.0
Low Risk

Modular media quality metrics toolkit

🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with low risks across multiple categories, although the metadata risk score is slightly elevated due to limited maintainer activity.

  • No network calls detected
  • No shell execution or obfuscation patterns found
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution detected, indicating there is no direct system command execution within the package.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package shows minimal activity and the maintainer has few credentials, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (3502 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
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in seruva19/ayase
  • Single author but highly active (100 commits)

🔬 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Ayase Contributors" 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 ayase 
Create a media quality assessment tool using the 'ayase' package in Python. This tool will allow users to upload audio and video files and receive a comprehensive report on their quality based on various metrics provided by the 'ayase' toolkit. Here’s a detailed plan for the project:

1. **Setup**: Install the necessary packages including 'ayase', and ensure your environment supports multimedia file processing.
2. **User Interface**: Develop a simple web-based interface where users can upload their audio and video files. Use Flask or Django for backend development.
3. **File Processing**: Once a file is uploaded, process it using the 'ayase' package. Utilize its modular design to calculate metrics such as signal-to-noise ratio (SNR), mean squared error (MSE), and perceptual evaluation of speech quality (PESQ) for audio; and PSNR, SSIM, and VMAF for video.
4. **Report Generation**: Generate a detailed report summarizing the quality metrics calculated. Include visual aids like graphs or charts to help interpret the data.
5. **Feedback System**: Allow users to see their results instantly and provide feedback on the tool's performance. Consider integrating a feature where users can compare different versions of the same file to assess improvements.
6. **Security Measures**: Ensure that all uploaded files are securely handled and deleted after processing to protect user privacy.
7. **Documentation**: Write clear documentation for both the end-users and developers. Explain how each metric is calculated and why it is important for assessing media quality.
8. **Testing**: Conduct thorough testing to ensure accuracy and reliability of the quality metrics provided by 'ayase'. Validate the results against known standards or manually calculated values.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!