🤖 AI Analysis

Final verdict: SAFE

The package VolFe v1.0.2 is assessed as safe with a low risk score. It shows no signs of obfuscation or credential harvesting, and while the metadata suggests a new maintainer, there are no additional red flags.

No obfuscation patterns detected
No credential harvesting patterns detected

Per-check LLM notes

Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository eryhughes/VolFe appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Pip Liggins, Penny Wieser" 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 VolFe

Your task is to create a Python-based mini-app named 'Volatility Explorer' using the 'VolFe' package. This application will serve as a tool for analyzing and visualizing volatility in financial markets. The app should allow users to input historical price data of various financial instruments such as stocks, commodities, and cryptocurrencies, and then calculate and visualize the implied volatility using the core functionalities provided by the 'VolFe' package.

The main steps for building this mini-app are:
1. Set up the environment by installing Python and the necessary packages including 'VolFe'.
2. Design a user-friendly interface where users can upload CSV files containing historical price data.
3. Implement functionality to parse the uploaded CSV files and extract relevant price information.
4. Use 'VolFe' to calculate the implied volatility from the extracted price data.
5. Visualize the calculated volatility data using matplotlib or any other preferred visualization library.
6. Allow users to save the visualized results as images or PDFs.
7. Add error handling to manage potential issues such as incorrect file formats or missing data.
8. Include documentation explaining how to use the application and how it leverages 'VolFe' for its calculations.

Suggested features include:
- Support for multiple financial instruments (stocks, commodities, cryptocurrencies).
- Real-time data fetching capabilities (optional).
- Option to choose different methods for calculating implied volatility within 'VolFe'.
- Exporting results to Excel or CSV files.
- Interactive plots that allow users to zoom in/out and explore data points.

By following these steps and incorporating the suggested features, you'll create a powerful yet easy-to-use tool for anyone interested in understanding and analyzing market volatility.