🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low direct risks such as network calls or shell execution but has poor metadata quality and low maintainer activity, raising concerns about its long-term maintenance and potential vulnerabilities.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating no unexpected system command executions.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The package shows some signs of low maintainer activity and poor metadata quality, but lacks clear indicators of malicious intent.

🔬 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: ligo.org>

✓ 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 PycWB

Create a mini-application named 'GravWaveBurstAnalyzer' that leverages the PycWB Python package for analyzing coherent gravitational wave bursts. This application should serve as a user-friendly interface for scientists and researchers to input their data and receive detailed analyses of gravitational wave signals. Here are the steps and features to include in your project:

1. **Setup Environment**: Begin by setting up a virtual environment for your project and installing necessary packages including PycWB.
2. **Data Input Interface**: Develop an intuitive GUI or CLI where users can upload their gravitational wave data files. Ensure that the interface supports various file formats commonly used in gravitational wave research.
3. **Preprocessing Module**: Implement a preprocessing module that cleans and normalizes the uploaded data using functions from PycWB. This step is crucial for accurate analysis.
4. **Analysis Engine**: Utilize PycWB's core functionalities to analyze the preprocessed data. Focus on detecting coherent gravitational wave bursts within the dataset. Allow users to specify parameters such as frequency ranges and coherence thresholds for more customized analyses.
5. **Visualization Tools**: Integrate visualization tools to display the results of the analysis in real-time. Use matplotlib or similar libraries to plot graphs showing detected bursts, signal strengths, and other relevant metrics.
6. **Report Generation**: Implement a feature that generates comprehensive reports summarizing the findings of the analysis. Include statistical summaries, visual plots, and interpretations based on the data provided.
7. **Documentation and Help Section**: Provide thorough documentation explaining how to use each feature of the application. Include examples, FAQs, and a section dedicated to troubleshooting common issues.

By following these steps and incorporating these features, you will create a powerful yet accessible tool for gravitational wave researchers.