🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network calls, shell execution, obfuscation, and credential harvesting. However, its metadata suggests it comes from a new maintainer with limited history and no public repository, raising concerns about potential supply-chain risks.

New maintainer with limited history
No public repository available

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communication.
Shell: No shell execution patterns detected, indicating no direct system command execution by the package.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package appears to be from a new maintainer with limited history and no public repository, raising 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

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: example.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Shweta" 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 Topsis-Shweta-2004

Create a multi-criteria decision-making tool using the 'Topsis-Shweta-2004' Python package. This tool will assist users in making decisions based on multiple criteria by ranking alternatives according to their proximity to ideal solutions. Here’s a step-by-step guide to building this application:

1. **Introduction**: Explain the concept of TOPSIS and its importance in decision-making processes involving multiple criteria.
2. **Feature Overview**:
   - **User Input**: Allow users to input various criteria and weights associated with each criterion.
   - **Alternative Input**: Enable users to enter multiple alternatives they wish to evaluate against these criteria.
   - **Calculation Engine**: Utilize the 'Topsis-Shweta-2004' package to calculate the relative closeness to the ideal solution for each alternative.
   - **Result Presentation**: Display the ranked order of alternatives from best to worst based on the TOPSIS method.
3. **Implementation Steps**:
   - Set up a Python environment with necessary libraries including 'pandas', 'numpy', and 'Topsis-Shweta-2004'.
   - Design a user-friendly interface using a library such as 'tkinter' or 'streamlit'.
   - Implement functions to handle user inputs and call the TOPSIS function from 'Topsis-Shweta-2004'.
   - Develop a visualization component to display results in a clear and understandable format.
4. **Testing**: Ensure the application works correctly by testing it with predefined datasets and comparing results with expected outcomes.
5. **Documentation**: Write comprehensive documentation explaining how to use the application, its limitations, and potential improvements.
6. **Deployment**: Package the application for distribution, ensuring it runs smoothly on different operating systems.

This project aims to provide a practical tool for anyone dealing with complex decision-making scenarios involving multiple criteria.