🤖 AI Analysis

Final verdict: SAFE

The package OpenPyTEA v2.1.0 exhibits minimal risk indicators with no network calls, shell executions, or credential mishandling. While there is some concern about metadata quality and maintainer history, these alone do not suggest a supply-chain attack.

No network calls detected.
No shell execution patterns detected.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The package shows low effort in metadata and maintainer history, but lacks clear malicious indicators.

🔬 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: tudelft.nl>

✓ 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 OpenPyTEA

Create a fully-functional mini-application using the 'OpenPyTEA' Python package that allows users to perform a basic techno-economic analysis on a hypothetical chemical processing plant. This app should enable users to input various parameters such as raw material costs, production capacities, energy consumption rates, and capital investment details. Users should also be able to define different scenarios for sensitivity analysis and uncertainty evaluation, allowing them to explore how changes in key variables affect the overall economics of the plant.

Core functionalities of the application include:
- Input forms for plant data including raw materials, utilities, and operational parameters.
- A feature to calculate the total annualized cost (TAC) based on user inputs.
- An interface for conducting sensitivity analyses on selected parameters.
- Visualization tools for displaying results, such as graphs showing TAC under different scenarios.
- Utilization of OpenPyTEA's capabilities to evaluate uncertainties and provide probabilistic outcomes for the techno-economic assessments.

The application should leverage OpenPyTEA's functions to handle complex calculations and simulations related to plant economics, making it a valuable tool for students, engineers, and researchers involved in process design and optimization.