dlthub-start 0.9.1

Create a dltHub workspace and hand off to your coding agent — scaffold, install, log in, and connect a playground, then the agent deploys and runs the sample pipeline.

dlthub-start (beta)

Create a ready-to-run dltHub workspace with example pipelines, local uv dependency setup, and bundled dltHub AI workbench files.

Quickstart

uvx is the recommended way to run the CLI. Run it from inside an empty project directory so the AI workbench files (skills + MCP server) land at the project root, where your coding agent runs:

mkdir my-workspace && cd my-workspace
uvx dlthub-start@latest

No uv? Install the CLI with pip (into your current Python environment) and run it directly:

pip install dlthub-start
dlthub-start

The CLI scaffolds the workspace, checks for uv (offering to install it if missing), installs dependencies with uv sync, logs you in to dltHub and connects a playground workspace, then prompts for your coding agent and sets up its files. Finally it asks whether to launch that agent in the workspace — seeded with a prompt to deploy and run the sample pipeline, so the agent (not the CLI) runs it. Decline, or use an agent with no command-line launcher, and it prints that prompt (with the bundled skill's location) and copies it to your clipboard so you can paste it into an agent you start yourself.

The full setup runs through the interactive prompts:

mkdir my-workspace && cd my-workspace
uvx dlthub-start@latest

You can also pass a target directory (uvx dlthub-start@latest my-workspace), but then the AI files live one level down — so launch your coding agent from inside that directory. If the generated workspace needs uv and it is not installed yet, the CLI offers to install it; or install it yourself via the official uv installation guide.

What You Get

• A Python dltHub workspace with project metadata customized to your directory name.
• A bundled scaffold copied from this package, not downloaded at create time.
• dltHub AI workbench files for your chosen coding agent (Claude, Cursor, or Codex).
• Shared dltHub AI toolkit files for data exploration, dltHub platform deployment, and REST API pipeline work.
• A zero-setup playground destination, managed by the dltHub platform — no credentials or config.

Usage

uvx dlthub-start@latest [project-dir] [options]

Initializes a workspace in place when the target is empty: the current directory by default, or project-dir if given. A non-empty target never fails — the CLI scaffolds into a free directory instead and tells you where it landed. With no argument it nests a playground subdirectory (then playground-1, playground-2, …); an explicit project-dir that's occupied falls back to <project-dir>-1, <project-dir>-2, …. Existing contents are left untouched. A directory holding only benign cruft — editor/OS files (.idea, .vscode, .DS_Store), tool caches, and a bare .git — still counts as empty and initializes in place; anything the scaffold ships (.gitignore, .dlt, …) counts as content and triggers the fallback.

Common options:

Option	Description
--agent claude	Use the Claude workbench files. Choose exactly one agent (claude, cursor, or codex); if omitted you're prompted (defaults to claude).
--agent cursor	Use the Cursor workbench files.
--agent codex	Use the Codex workbench files.
--verbose, -v	Stream output from underlying subprocesses.

Examples:

uvx dlthub-start@latest                         # interactive setup in the current (empty) directory — recommended
uvx dlthub-start@latest --agent codex           # skip the agent prompt
uvx dlthub-start@latest my-workspace            # alternative: create + initialize a subdirectory

Workspace contents

The bundled workspace is a quick, runnable first look: a sample online-shop pipeline, local warehouse config, and a generated deployment module.

Generated Workspace

The workspace is initialized at the project root, shaped roughly like this:

.
|-- pyproject.toml
|-- pipeline.py
|-- __deployment__.py
|-- README.md
|-- .dlt/
|-- .mcp.json
`-- .claude/        # your selected agent (.cursor/, or .agents/ for codex)

Next Steps

From the workspace root:

uv run dlthub run load_sample_shop
uv run dlthub show

(If you scaffolded into a subdirectory, cd into it first.)

Troubleshooting

uvx: command not found

Install the CLI with pip install dlthub-start (into your current Python environment) and run dlthub-start instead. The CLI will still offer to install uv before syncing the generated workspace dependencies.

My workspace landed in a playground/-1 subdirectory

That's expected when the target wasn't empty: rather than refuse, the CLI scaffolds into a free directory and prints where it went. To control the location, pass an explicit empty target — uvx dlthub-start@latest my-workspace — or run from an empty directory. The CLI never writes into a non-empty directory; it picks a fresh one alongside it.

uv sync fails

Re-run with --verbose to see subprocess output:

uvx dlthub-start@latest my-workspace --verbose

If the scaffold was created successfully, you can also enter the workspace and run uv sync directly after fixing the underlying dependency or network issue.

Development

For local setup, tests, build commands, make workspace, and AI workbench scaffold regeneration, see CONTRIBUTING.md.

Publishing

To build and publish a release to PyPI:

make publish

This removes any previous dist/ artifacts, builds the package with uv build, lists the artifacts, and prompts for a PyPI API token before uploading with uv publish. Before publishing, run the release checklist in CONTRIBUTING.md and make sure the version in pyproject.toml has been bumped.