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Use when a Cloudflare Worker needs TanStack AI chat persistence — writes a chat-persistence.ts into the app against its D1 binding (raw or via Drizzle), plus a Durable Object LockStore. Covers per-request bindings, wrangler config, D1 migrations, and lease-based locks.

Use when a Cloudflare Worker needs durable byte storage for TanStack AI generated media (images, audio, video, transcripts) — writes a BlobStore backed by R2 and an ArtifactStore backed by D1, composes them onto the generation persistence so withGenerationPersistence persists artifact bytes, and serves them back from a Worker GET route. Includes one-line sketches for S3, GCS, Vercel Blob, Supabase, and a dev filesystem BlobStore.

Use when an app needs TanStack AI chat persistence on a database with no dedicated recipe — raw Postgres (pg/postgres.js), Kysely, node:sqlite, MongoDB, Supabase, Redis. Writes a chat-persistence.ts against the app's existing client, covering the four stores, the idempotency invariants, and the conformance gate. Route to the Drizzle, Prisma, or Cloudflare skills instead when one of those matches.

Use when an app already runs Drizzle ORM and needs TanStack AI chat persistence — writes a chat-persistence.ts into the app against its existing db handle, schema file, and drizzle-kit journal. Covers the four tables (SQLite/Postgres/MySQL), the onConflict idempotency rules, JSON columns, and per-request bindings like D1.

Use when an app already runs Prisma and needs TanStack AI chat persistence — writes a chat-persistence.ts into the app against its existing PrismaClient and schema.prisma. Covers the four models, BigInt timestamps, JSON-as-string columns, upsert-with-empty-update idempotency, and model renaming.

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