🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low individual risks but raises suspicion due to its newness and limited metadata, which could potentially indicate a supply-chain attack.

Metadata risk due to limited maintainer information
Potential shell execution that needs further investigation

Per-check LLM notes

Network: No network calls detected, which is not unusual and does not indicate risk.
Shell: Detected shell execution may be for package installation purposes but should be scrutinized to ensure it doesn't perform unauthorized actions.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is newly created with limited information from the maintainer, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

age_name): try: subprocess.check_call([sys.executable, "-m", "pip", "install", package_name])

✓ 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 Peyman20122/Tsemodules-Library appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 Tsemodules7-library

Your task is to develop a fully-functional mini-app that allows users to monitor stock prices and other financial data from the Tehran Stock Exchange (TSE). This app will leverage the 'Tsemodules7-library' Python package, which provides easy access to real-time and historical data from the TSE.

### Project Overview:
- **Name:** TSE Watcher
- **Goal:** To create a user-friendly tool that fetches and displays relevant financial information from the TSE, including stock prices, market indices, trading volumes, and more.
- **Target Audience:** Investors, traders, and anyone interested in tracking the performance of companies listed on the TSE.

### Key Features:
1. **Real-Time Data Fetching:** Utilize 'Tsemodules7-library' to pull live stock price updates and other financial indicators directly from the TSE.
2. **Historical Data Analysis:** Implement functionality to retrieve historical data over specified periods, such as daily, weekly, or monthly data points.
3. **Customizable Alerts:** Allow users to set up alerts based on specific criteria, like when a particular stock reaches a certain price point or when there's a significant change in trading volume.
4. **User Interface:** Design a simple yet effective UI using Python's Tkinter library or a web framework like Flask/Django for displaying fetched data.
5. **Data Visualization:** Integrate libraries such as Matplotlib or Plotly to provide graphical representations of stock trends and market indices.
6. **Saving & Exporting Data:** Enable users to save their data locally or export it in CSV format for further analysis.

### Implementation Steps:
1. **Setup Environment:** Install necessary packages including 'Tsemodules7-library', Matplotlib/Plotly, and any chosen UI framework.
2. **Fetching Data:** Write functions to call 'Tsemodules7-library' methods for fetching both real-time and historical data.
3. **Displaying Data:** Develop the UI to display fetched data in a clear and organized manner, possibly with filters for different types of information.
4. **Alert System:** Implement logic for setting up and managing alerts based on user-defined conditions.
5. **Visualization:** Create visual graphs and charts to help users better understand trends and patterns in the data.
6. **Saving & Exporting:** Add functionality to allow users to save their data to local files or export it in CSV format.
7. **Testing & Debugging:** Conduct thorough testing to ensure all features work correctly and address any bugs or issues.
8. **Documentation:** Prepare comprehensive documentation explaining how to use the app, its features, and how to interpret the displayed data.

This project aims to showcase your ability to integrate external APIs and libraries into a practical application while providing valuable insights into the financial markets.