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add dltHub AI Workbench references #17

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16 changes: 16 additions & 0 deletions README.md
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Expand Up @@ -15,6 +15,22 @@ is self-contained -- you can jump to any chapter, but the concepts build on each
- A [dltHub Runtime](https://dlthub.com) account
- (Optional) A [MotherDuck](https://motherduck.com) account for cloud destinations

> **Using an AI coding assistant?** Pair this tutorial with the
> [dltHub AI Workbench](https://github.com/dlt-hub/dlthub-ai-workbench) —
> a set of toolkits (skills, slash commands, MCP server) that give Claude Code,
> Cursor, and Codex guided workflows for building pipelines, exploring data,
> writing transformations, and deploying to Runtime. Quick start:
>
> ```sh
> uv add "dlt[workspace,hub]"
> uv run dlt ai init
> uv run dlt ai toolkit dlthub-runtime install
> uv run dlt ai toolkit data-exploration install
> uv run dlt ai toolkit transformations install
> ```
>
> See the [Agentic data engineering workflow docs](https://dlthub.com/docs/dlt-ecosystem/llm-tooling/llm-native-workflow) for the full list of toolkits.

### Preview Prerequisites
> dlt comes from: https://github.com/dlt-hub/dlt/archive/refs/heads/feat/workspace-deployment.zip and is added to each pyproject.toml
> You must setup runtime client to use locally running backend (already added to config.toml(s))
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