harvard-artifacts-etl-pipeline
Pass
Audited by Gen Agent Trust Hub on Oct 1, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill implements standard ETL (Extract, Transform, Load) patterns for data engineering. It correctly uses environment variables for sensitive credentials (API keys and database passwords) and follows standard library usage for networking (requests) and database interaction (mysql-connector-python).
- [SAFE]: The repository referenced (github.com/Manali0711/Harvard-Artifacts-Collection-Data-Engineering-Analytics-App.git) is a legitimate source for a data engineering project.
- [SAFE]: All dependencies listed (streamlit, pandas, requests, mysql-connector-python, plotly, python-dotenv) are standard, well-known libraries in the Python data ecosystem.
- [SAFE]: The SQL implementation uses parameterized batch inserts (
executemany) in theload_to_sqlfunction, which is a secure practice for database interactions.
Audit Metadata