What problem does it solve? Building custom Python transformations for Salesforce Data Cloud requires coordinating the SF CLI plugin, the Data Cloud Custom Code SDK, permission scanning, local testing, and Docker-based deployment, which is error-prone without a guided workflow. ## Core Features & Use Cases - Project Scaffolding: Initialize script-based (batch) or function-based (real-time) code extension projects with the correct payload directory structure. - Permission Scanning and Local Testing: Auto-detect DLO/DMO read and write permissions into config.json, validate DLO schemas, and run transformations locally against a real org. - Versioned Deployment: Deploy code extensions to Data Cloud with CPU sizing, semantic versions, and descriptions for scheduled or on-demand execution. - Use Case: Create a transformation that reads an Employee DLO, uppercases the position field, and writes results to a new DLO, then test it locally and deploy it as a scheduled Data Cloud transform. ## Quick Start Create a new Data Cloud code extension project called employee-transform that reads from Employee__dll and writes an uppercased position field to Employee_Upper__dll.
