AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity such as network calls, shell executions, or credential harvesting. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone does not suggest a supply-chain attack.
- No network calls detected
- Single package from maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal for a package focused on file reading.
- Shell: No shell execution patterns detected, consistent with a benign package purpose.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which might indicate a new or less active account, but there are no other red flags.
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
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository Utilified/aemo-mdff-reader appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Utilified Holdings Pty Ltd" 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-mdff-reader
Create a Python-based mini-application called 'NEM Meter Data Explorer' that leverages the 'aemo-mdff-reader' package to provide users with an intuitive way to explore and analyze AEMO MDFF NEM12/NEM13 metering data. The application should be able to read large metering files efficiently without requiring any additional dependencies. Here’s a step-by-step guide on how to develop this application: 1. **Setup Project Environment**: Start by setting up your Python environment and installing the 'aemo-mdff-reader' package using pip. 2. **File Selection Interface**: Develop a simple interface where users can select one or multiple MDFF files to process. This could be done via command-line arguments or a basic GUI. 3. **Data Reading & Streaming**: Use 'aemo-mdff-reader' to stream and read the selected files. Implement functionality to handle large datasets efficiently, ensuring that the application does not consume excessive memory. 4. **Data Processing**: Once the data is streamed, implement functions to process this data. For instance, calculate total energy consumption per day, week, or month. Additionally, identify peak usage times and low usage periods. 5. **Visualization**: Integrate a plotting library like matplotlib or seaborn to visualize key metrics such as daily energy consumption, monthly trends, and peak vs off-peak usage. 6. **Export Options**: Allow users to export processed data and visualizations in various formats (CSV, Excel, PNG). Ensure these exports are well-formatted and easy to understand. 7. **Interactive Exploration**: Provide an interactive mode where users can query specific time ranges, meters, or other parameters directly from the application. 8. **Documentation & User Guide**: Finally, write comprehensive documentation including a user guide explaining how to use each feature of the application effectively. By following these steps, you'll create a powerful yet user-friendly tool for exploring AEMO MDFF metering data.