🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, and obfuscation activities. However, the metadata risk score indicates potential issues with maintainer engagement and metadata quality, raising concerns about its reliability.

Low maintainer engagement
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network functionality.
Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer engagement and poor metadata quality, raising some suspicion but not conclusive evidence of malice.

🔬 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

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ASTRA-spectra

Create a Python-based mini-application called 'SpectralAnalyzer' that leverages the 'ASTRA-spectra' package to analyze stellar spectra. This tool should allow astronomers and researchers to upload their own stellar spectrum data, apply pre-defined or custom telluric correction models, and visualize the processed data. Here are the steps and features your application should include:

1. **Data Import**: Users should be able to import their own FITS or ASCII files containing stellar spectra.
2. **Telluric Correction Model Selection**: Provide a library of pre-built telluric correction models based on common atmospheric conditions. Allow users to select from these models or input custom parameters for more precise corrections.
3. **Spectrum Visualization**: Display both the original and corrected spectra side-by-side using matplotlib or similar libraries.
4. **Interactive Analysis Tools**: Include tools such as zooming, panning, and spectral line identification (with known stellar lines).
5. **Export Options**: Enable users to export the corrected spectra and analysis results in various formats including CSV, FITS, and PNG images.
6. **User-Friendly Interface**: Design a clean and intuitive graphical user interface using PyQt or Tkinter.
7. **Documentation and Help**: Include comprehensive documentation and a help section within the application explaining each feature and how to use them effectively.

Utilize the 'ASTRA-spectra' package to handle the loading of spectral data, application of telluric corrections, and any other necessary processing tasks. Ensure that your application is well-documented and easy to install via pip.