AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in network and shell activities, but the metadata suggests it comes from a less active or new maintainer, raising some suspicion.
- Low network and shell execution risks
- Maintainer has only one package, indicating potential lack of history or trust
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized access.
- Metadata: The maintainer has only one package, indicating a new or less active account, which raises some suspicion but not enough to conclusively determine 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
Email domain looks legitimate: lbl.gov
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository TonyZhou729/ABCMB appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Zilu Zhou, Cara Giovanetti, Hongwan Liu" 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 ABCMB
Create a mini-application named 'CMBExplorer' that allows users to explore and analyze Cosmic Microwave Background (CMB) data using the ABCMB package. This application will serve as both an educational tool and a basic research utility. Here are the steps and features you should include: 1. **Setup**: Begin by installing the ABCMB package and any other necessary Python libraries such as numpy, matplotlib, and astropy. Ensure your environment is set up correctly. 2. **Data Importation**: Implement functionality to import CMB data from various sources. Users should be able to upload their own data files or select from predefined datasets. 3. **Analysis Tools**: Utilize ABCMB's capabilities to perform basic analyses on the imported data. Include tools for calculating power spectra, temperature maps, and polarization maps. Each analysis tool should leverage ABCMB's speed and differentiability features. 4. **Visualization**: Develop a user-friendly interface for visualizing the results of these analyses. Use matplotlib to create interactive plots that allow users to zoom, pan, and adjust parameters. 5. **Customization**: Enable users to customize their analysis by adjusting parameters within the ABCMB functions. Provide a simple GUI where users can input values for parameters like frequency ranges, resolution settings, etc. 6. **Export Options**: Allow users to export their visualizations and analysis results in common formats like PNG, PDF, and CSV for further use or sharing. 7. **Documentation & Help**: Include comprehensive documentation and a help section within the application that explains the usage of ABCMB and how to interpret the results of each analysis. The goal is to create a versatile yet straightforward tool that showcases the capabilities of ABCMB while being accessible to beginners in CMB studies.