What problem does it solve? Populating a Convex database with initial or test data is repetitive and error-prone when done manually, and re-running seed scripts can create duplicates or inconsistent state. ## Core Features & Use Cases - Fixture Seeding via internalMutation: Write a re-runnable internal mutation that inserts sample rows matching your schema validators, then execute it with npx convex run. - Bulk Data Import: Shape existing datasets to match the schema and load them with npx convex import. - Idempotent Seeding: Use clear-then-insert or upsert patterns so re-running the seed never corrupts data. - Use Case: You just defined a new tasks table in your Convex schema and need realistic sample rows for local development. Create an idempotent seed mutation, run it, and verify row counts before sharing the deployment. ## Quick Start Ask the AI to create an idempotent Convex seed mutation for your schema and run it with npx convex run.