🤖 AI Analysis

Final verdict: SAFE

The package appears to be designed for legitimate use, fetching data from the NSE of India. While there are some minor concerns regarding metadata, no significant risks were identified.

Low risk scores across all categories
Missing maintainer information

Per-check LLM notes

Network: The observed network call patterns suggest normal HTTP request handling and session management, which is common for packages that interact with web services.
Shell: No shell execution patterns detected.
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 maintainer's author name is missing or very short, and they appear to have only one package, which could indicate a new or inactive account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

elf): self.session = requests.Session() self._initialize_session() def _initialize
expiry self.session = requests.Session() self._initialize_session() def _initialize_se

✓ 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 Prasad1612/NseKit appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 NseKit

Develop a Python-based stock analysis mini-app using the NseKit package. This app will allow users to analyze stocks listed on the National Stock Exchange (NSE) of India. Here’s a detailed breakdown of the steps and features you need to implement:

1. **Setup**: Begin by installing the NseKit package via pip. Ensure your development environment supports Python.
2. **User Interface**: Design a simple command-line interface (CLI) for user interaction. Users should be able to enter commands to fetch stock details.
3. **Stock Details Fetching**: Utilize NseKit to fetch real-time and historical stock price data. Implement functionality to display key metrics like opening price, closing price, highest price, lowest price, and volume traded.
4. **Stock Analysis**: Add features to calculate and display important financial ratios such as Price/Earnings (P/E) ratio, Dividend Yield, and Beta. Use NseKit to retrieve necessary data points for these calculations.
5. **Portfolio Management**: Allow users to input their portfolio holdings. Calculate the total value of the portfolio based on current market prices fetched from NseKit.
6. **Notifications**: Implement a feature where users can set alerts for specific stocks. When the stock reaches a predefined threshold (e.g., price level), send a notification to the user.
7. **Graphical Representation**: Integrate a library like matplotlib to plot stock price trends over time. Users should be able to select a timeframe for the graph.
8. **Documentation**: Provide comprehensive documentation explaining how to use the CLI, interpret outputs, and integrate the app into personal finance management systems.

By following these steps, you’ll create a robust, user-friendly tool that leverages the power of NseKit to provide valuable insights into NSE stocks.