🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risk due to potential low maintainer effort and lack of author information, despite showing no signs of malicious activities like network calls or credential harvesting.

Metadata risk indicates low maintainer effort and lack of author information
No direct evidence of malicious intent found

Per-check LLM notes

Network: No network calls detected, which is normal and expected.
Shell: Shell execution appears to be used for clearing the console, likely for user interface purposes.
Obfuscation: No obfuscation patterns detected, suggesting legitimate use.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets.
Metadata: The package shows signs of low maintainer effort and lack of author information, raising some suspicion but not definitive indicators 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)

---\n') # os.system('cls' if os.name == 'nt' else 'clear') tqdm.

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: hs-offenburg.de>

✓ 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 DES-sim

Your task is to develop a mini-application that simulates a simplified district heating network using the DES-sim Python package. This application will allow users to input parameters such as the number of buildings, the heat demand of each building, and the characteristics of the heating system (e.g., boiler efficiency, storage capacity). The application should then simulate the operation of the district heating network over a specified period (e.g., one year), taking into account seasonal variations in heat demand.

### Key Features:
1. **User Input Interface**: Develop a simple command-line interface where users can input the necessary parameters for the simulation.
2. **Simulation Engine**: Utilize DES-sim to model the district heating network, including components like boilers, distribution networks, and end-users (buildings).
3. **Visualization**: Implement basic visualization tools to display key metrics from the simulation, such as total heat production, energy consumption, and CO2 emissions over time.
4. **Report Generation**: After the simulation, generate a report summarizing the performance of the district heating system, including efficiency metrics and environmental impact.
5. **Scenario Analysis**: Allow users to run multiple scenarios by varying parameters like the type of fuel used or the insulation level of buildings, and compare the results.

### How to Use DES-sim:
- Install DES-sim via pip: `pip install DES-sim`
- Import the necessary modules from DES-sim to define your simulation environment.
- Use DES-sim’s API to set up components (e.g., boiler, heat exchanger, storage tank) and their interactions within the district heating network.
- Configure the simulation settings, including the time step and duration.
- Run the simulation and analyze the output data.

### Deliverables:
- A fully functional Python script that integrates DES-sim for district heating network simulation.
- Documentation explaining how to use the application and interpret the results.
- Sample input files and expected outputs for verification.