🤖 AI Analysis

Final verdict: SAFE

The package exhibits minimal risk indicators with no network calls, shell executions, or obfuscation. However, its low maintainer activity and poor metadata quality raise some concerns about its reliability and long-term support.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate potential issues but does not definitively suggest 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

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 addftool

Create a simple yet powerful financial data analysis tool using the 'addftool' package in Python. This tool will allow users to fetch historical stock price data from major stock exchanges, perform basic statistical analyses on the data, and visualize the results through interactive charts. The application should follow these steps:

1. **User Input**: Allow users to input a stock symbol and select a date range for which they want to retrieve historical prices.
2. **Data Fetching**: Utilize the 'addftool' package to fetch historical stock price data based on user input. Ensure the data includes at least the closing price, opening price, high, low, and volume.
3. **Statistical Analysis**: Perform basic statistical analyses such as calculating the mean, median, standard deviation of the closing prices, and identifying any outliers in the dataset.
4. **Visualization**: Create interactive line charts displaying the closing prices over time, and bar charts showing the distribution of daily volumes.
5. **Output**: Provide an output summary that includes key statistics derived from the analysis and display the visualizations within the application.

Suggested Features:
- Error handling for invalid stock symbols or date ranges.
- Option to save the fetched data into a CSV file.
- Integration with a web framework like Streamlit or Flask to make the application accessible via a web browser.
- User-friendly interface design to enhance usability.

The 'addftool' package is assumed to provide essential functions for fetching financial data and performing necessary operations. Your task is to leverage its capabilities to create a robust and user-friendly financial data analysis tool.