quick-start

name: quick-start description: "Use when the user wants a guided end-to-end run from data to dashboard in a few prompts: 'show me a demo', 'give me a quick start', 'take me through the full workflow', 'how do I go from data to dashboard', 'walk me through ingestion to visualization', 'I want to try everything end-to-end'. Do NOT use when the user is asking what's available or where to start in general — use the toolkit-dispatch skill (in init) for capability-discovery questions ('what can you do', 'what toolkits are there', 'I'm new to dlthub'). Do NOT use when the user already has a specific task underway (debugging, adding an endpoint, deploying)." argument-hint: "[data-source] [path]"

Quick Start

Guide the user from zero to a deployed, production-ready pipeline in a few prompts. Make sure you keep the sampling for the first deployment.

Parse $ARGUMENTS:

• data-source (optional): what the user wants to extract data from
• path (optional): one of production (default), discover, inspect, cdm

Step 1 — Check workspace status

Run uv run dlthub ai status.

• If everything is set up: continue to Step 2.

• If prerequisites are missing (no workspace, MCP not connected, missing dependencies): briefly tell the user what is missing in one line, then point them to the authoritative bootstrap command:

  uvx dlthub-start@latest my-workspace
  

  This is the canonical way to get started with the dltHub AI workbench — it sets up the workspace and installs the toolkits needed downstream. Do not auto-run it — wait for the user to run it themselves, then re-check status before continuing to Step 2.

Step 2 — Present capability index and ask one question

If $ARGUMENTS already has both source and path, skip to Step 3.

If the user has mentioned an existing pipeline (has data already loaded), route directly:

• wants to explore or visualize → invoke explore-data
• wants to model or transform → invoke annotate-sources
• wants to deploy → invoke setup-runtime

Otherwise, show the capability table and depth menu, then ask: "What do you want to extract data from?"

INGEST     → REST API pipelines (find-source → create → debug → harden → validate)
EXPLORE    → Marimo dashboards (explore-data → build-notebook)
TRANSFORM  → Canonical data model — Kimball (annotate-sources → generate-cdm → create-transformation)
DEPLOY     → dltHub Runtime on a schedule (setup-runtime → prepare-deployment → deploy-workspace)

Pick a depth (default is Production):
  [1] Production  — ingest + harden + validate + deploy + visualize  ← default
  [2] Full CDM    — ingest + harden + validate + model + transform + deploy + visualize (~8 steps)
  [3] Inspect     — ingest + harden + validate + visualize (no deploy)
  [4] Discover    — ingest (demo only, leaves dev artifacts; explicit opt-in)

What do you want to extract data from?

Default routing rule: if the user answers with just a source name, or names a source without picking a depth, route to Production. Pick another path only if the user explicitly names it (e.g. "just a quick demo", "discover path", "skip deploy", "I just want to see the data", "no need to deploy", "Full CDM").

Step 3 — Confirm path and hand off

Announce the step sequence for the chosen path, then invoke find-source with the source name.

Why every non-Discover path includes adjust-endpoint: create-rest-api-pipeline intentionally leaves debug artifacts behind for fast iteration — dev_mode=True, single_page paginators, low per_page, no incremental. adjust-endpoint removes those before validation/deploy/exploration. Skipping it leads to deploying a sample loader, not a real pipeline.

Announce the path name and sequence to orient the user, then immediately invoke find-source with the source name as its argument. The path name is for user expectations only — it does NOT change find-source's behaviour. Downstream toolkit workflow.md rules handle subsequent steps.

Production path hardening checklist (delegated to adjust-endpoint, but state explicitly when announcing the path so the user knows the toy run will get hardened):

• Remove dev_mode=True from the pipeline
• Replace single_page paginators with the API-appropriate paginator (e.g. header_link for GitHub, json_link / offset / page_number elsewhere)
• Restore per_page to a normal value (typically 100)
• Add incremental cursors on resources that support it
• Remove any .add_limit(N) calls left from the first run

What NOT to do

• Do not re-explain downstream skills after handing off
• Do not run dlthub pipeline init or create any files yourself
• Do not ask more than one question before routing
• Do not re-invoke this skill after handing off to find-source