🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity based on the provided analysis notes. It does not engage in risky behaviors such as making network calls, executing shell commands, or obfuscating code.

No network calls detected
No shell execution patterns
No obfuscation patterns
No credential harvesting

Per-check LLM notes

Network: No network calls detected, which is normal for most packages unless it's designed to interact with external services.
Shell: No shell execution patterns detected, indicating the package does not execute system commands, reducing the risk of malicious activities.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting no immediate threat to stored secrets.
Metadata: The maintainer has only one package, indicating a potentially new or less active account.

🔬 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: powston.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository powston/aemo_to_tariff appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Ian Connor" 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 aemo-to-tariff

Develop a user-friendly web-based application that converts AEMO (Australian Energy Market Operator) spot prices from $/MWh to c/kWh for various networks and tariffs. The application should allow users to input specific date ranges and network codes to retrieve accurate conversion results. Utilize the 'aemo-to-tariff' Python package as the core engine for price conversions.

### Key Features:
- **User Interface**: Create an intuitive front-end using HTML, CSS, and JavaScript frameworks like React or Vue.js.
- **Backend Integration**: Use Flask or Django to create the backend API that interacts with the 'aemo-to-tariff' package.
- **Data Input**: Allow users to select start and end dates, along with network codes, through the UI.
- **Real-time Conversion**: Display the converted spot prices in c/kWh in real-time as users adjust their inputs.
- **Chart Visualization**: Implement a charting library such as Chart.js or D3.js to visualize the price trends over the selected period.
- **Documentation**: Provide clear documentation on how to install and use the application.

### Steps to Build the Application:
1. **Setup Environment**: Install necessary Python packages including 'aemo-to-tariff', Flask/Django, and any additional libraries required for data processing.
2. **Frontend Development**: Design and develop the frontend interface focusing on ease of use and visual appeal.
3. **Backend Implementation**: Develop the backend API endpoints to handle user requests, process data using 'aemo-to-tariff', and return the converted prices.
4. **Integration**: Integrate the frontend with the backend to ensure seamless interaction between the two.
5. **Testing**: Conduct thorough testing to ensure accuracy of conversions and functionality of all features.
6. **Deployment**: Deploy the application on a cloud platform like AWS or Heroku.
7. **Maintenance**: Continuously update the application based on user feedback and new requirements.

By following these steps and incorporating the suggested features, you'll create a powerful tool that simplifies the understanding and management of energy market data.