What problem does it solve? Maintaining the In-App Learning integration in Revenue Cloud Foundations requires coordinating a legacy SQL-to-SFDMU converter, per-release content remaps, strict RLM_Learning_* naming conventions, and deploy/reload/verify workflows, where mistakes like hand-editing CSVs or trusting SPA HTTP statuses silently corrupt data. ## Core Features & Use Cases - Converter-driven data pipeline: Edit convert_from_legacy.py and regenerate SFDMU v5 CSVs instead of hand-editing RLM_Learning_*.csv files, keeping the converter as the single source of truth. - Per-release content updates: Remap Help, dev-guide, release-notes, and Trailhead IDs grounded against docs/salesforce/{release}/ snapshots, with reliable link verification beyond curl status codes. - Deploy, reload, and verify workflow: Run prepare_inapp or deploy plus load_inapp_dataset, then confirm the SectionBlockController returns resolved data and the Learning Home renders in a browser. - Use Case: When bumping learning content to a new Salesforce release, add renamed article IDs to the remap dicts, regenerate the dataset, validate with the SFDMU v5 scripts, reload a scratch org, and render-check the Learning Home. ## Quick Start Ask the agent to update the inapp learning content for the new release by editing the converter remap dicts, regenerating the dataset, validating it, and verifying the Learning Home on a scratch org.