Can I vibe code Supermetrics?

Build a light, robust Python-based ELT pipeline that extracts marketing performance data from Shopify, Meta Ads, and Google Ads, loading it into a local DuckDB instance and exporting a summary to Google Sheets. 1. DATA SOURCES: Implement extractors using 'requests' or vendor SDKs for: (a) Shopify Admin API: Pull daily orders, gross revenue, net revenue, and order count. (b) Meta Marketing API: Pull daily spend, impressions, clicks, purchases, and purchase_value grouped by campaign. (c) Google Ads API: Pull daily cost_micros, impressions, clicks, and conversions grouped by campaign. 2. DATA PROCESSING: Use Python's `dlt` library to infer schema and automatically handle table creation and updates in DuckDB. Normalize data into standard columns: date, source, campaign_name, spend_usd, revenue_usd, clicks, impressions, purchases. 3. EXPORT MODULE: Create a post-load script that queries DuckDB for blended metrics (Blended ROAS = Total Revenue / Total Spend, Blended CAC = Total Spend / Total Orders) for the last 30 days and writes the aggregated results to a specified Google Sheet tab using `gspread`. 4. FAILURE HANDLING & RETRIES: Implement tenacity-based exponential backoff (retry up to 5 times) for HTTP status codes 429, 500, 502, 503, 504. Securely fetch API access keys and OAuth tokens from environment variables (`.env`). Write errors to a local log file and fail gracefully without wiping target tables. OUT OF SCOPE: Full web UI, complex user authentication management, non-standard ad channels.

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