Add Onboarding Tour logo

Add Onboarding Tour

OrganizationPopular
lightdash
add-onboarding-tour

Add a first-run guided tour, product walkthrough, coachmarks, or empty-state mock/example data to a Lightdash frontend feature. Use when the user wants to onboard users to a page, add a "Take the tour" flow, explain an unfamiliar UI, or show sample data on an empty page.

Overview

Publisherlightdash
Repositorylightdash
Skill nameadd-onboarding-tour
Stars
6.2K
Forks
790
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by lightdash on GitHub. Read the source before you install it.

Installation

Install the Add Onboarding Tour AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/lightdash/lightdash/tree/main/.claude/skills/add-onboarding-tour
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lightdash/lightdash.git /tmp/lightdash
mkdir -p .claude/skills
cp -r /tmp/lightdash/.claude/skills/add-onboarding-tour .claude/skills/add-onboarding-tour
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add Onboarding Tour in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Add Onboarding Tour on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Add Onboarding Tour is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Add an Onboarding Tour

A zero-dependency, centralised kit for first-run onboarding in packages/frontend. Three reusable pieces do the heavy lifting; each feature only supplies its own steps, copy, anchors, and (optionally) example data.

Building blocks

PiecePathJob
useGuidedToursrc/hooks/useGuidedTour.tslocalStorage seen-flag, first-visit auto-open, replay. Returns { isOpen, startTour, closeTour }.
GuidedToursrc/components/common/GuidedTourSpotlight rendering. Dims the page, highlights a data-tour target, anchors a Next/Back/Skip card. target: null → centered card.
useOnboardingMocksrc/hooks/useOnboardingMock.tsA react-query select that swaps real data for deterministic mock rows while a flag is on.

Reference implementation: the Reviews page — src/ee/features/aiCopilot/components/Admin/settings/AiReviewsSettingsPage.tsx (wiring), AiAgentAdminReviewItemsTable.tsx (mock rows), and Admin/onboarding/ (the content). Read these first — copying them is the fastest path.

Where content lives

Kit = global, content = per-feature. The three building blocks above are shared. Everything specific to one feature — its steps, copy, sample rows, and any onboarding-only visuals — goes in a co-located onboarding/ folder next to the feature, with the same fixed layout every time:

<feature-dir>/onboarding/
  index.ts          public surface (re-exports)
  steps.tsx         TOUR_STEPS: GuidedTourStep[]   (all the step copy)
  exampleData.ts    EXAMPLE_*, isExample*()         (only if the feature shows mock rows)
  <Visual>.tsx      onboarding-only visuals, e.g. a diagram (+ .module.css)

The feature imports from ./onboarding. Do not put feature content in a global folder, and do not inline steps or mock data in the page/table — keep components about rendering. Only re-export from index.ts what's consumed outside the folder (ts-unused-exports is enforced).

Recipe

  1. Wire the tour state in the feature page:

    tsx
    const { isOpen, startTour, closeTour } = useGuidedTour({
        storageKey: 'ld.<feature>.tour.v1',
    });
  2. Define steps in onboarding/steps.tsx as a module constant (they're static — no useMemo needed). Each target is a CSS selector resolved when the step is reached, or null for a centered explainer:

    tsx
    export const TOUR_STEPS: GuidedTourStep[] = [
        { target: '[data-tour="<feature>-intro"]', title: '…', body: '…' },
        { target: '[data-tour="<feature>-row"]',   title: '…', body: '…' },
        { target: null, title: '…', body: <SomeDiagram /> }, // centered
    ];

    The page imports { TOUR_STEPS } from ./onboarding and passes it to <GuidedTour>.

  3. Add data-tour anchors to the elements each step points at. For a table row, add it in the row props so the whole row is spotlit:

    tsx
    mantineTableBodyRowProps: ({ row }) =>
        row.index === 0 ? { 'data-tour': '<feature>-row' } : {},
  4. Render the tour and a replay button:

    tsx
    <Button variant="subtle" leftSection={<MantineIcon icon={IconRoute} />} onClick={startTour}>
        Take the tour
    </Button>
    <GuidedTour steps={steps} opened={isOpen} onClose={closeTour} />
  5. (Optional) Deterministic example data so a tour on an empty (or any) page always highlights the same rows. Put stable, clearly-labelled mock rows and the isExample helper in onboarding/exampleData.ts, and inject them via select while the tour is open:

    tsx
    const select = useOnboardingMock(EXAMPLE_ROWS, isOpen);
    const { data } = useThings(args, { select }); // hook must forward `select` to useQuery

    Render example rows muted and inert (disabled actions, no navigation); mark them with an "Example" badge. Gate interactivity off a sentinel id (e.g. id.startsWith('example:')).

Conventions

  • Zero dependencies. No joyride/driver/intro.js. The spotlight is a box-shadow: 0 0 0 9999px dim — already handled by GuidedTour.
  • storageKey: ld.<feature>.tour.v<n>. Bump the version to re-show the tour after a redesign.
  • Copy: warm, natural, straight to the point. No em dashes, no arrows. Short titles.
  • Styling: follow frontend-style-guide — no style prop (pass runtime geometry via __vars), CSS modules, theme tokens / ldGray/ldDark.
  • Mock rows must never look or act real: muted, "Example" badge, disabled actions.

Gotchas

  • Targets that render late (data still loading): handled — GuidedTour polls for each step's element and shows a centered card until it appears. Do not filter steps at open time; that drops steps whose targets haven't rendered yet.
  • Determinism: tie mock data to isOpen (tour running), not to emptiness, if you want the tour to highlight the same rows every run. Closing the tour flips back to real data.
  • select passthrough: the data hook must accept and forward a select option to useQuery (see useAiAgentAdminReviewItems). Add it if missing.

Frequently asked questions

What does the Add Onboarding Tour AI skill do?

Add a first-run guided tour, product walkthrough, coachmarks, or empty-state mock/example data to a Lightdash frontend feature. Use when the user wants to onboard users to a page, add a "Take the tour" flow, explain an unfamiliar UI, or show sample data on an empty page.

Why use Add Onboarding Tour on TypingMind?

Because you install it once and use it with any model. Add Onboarding Tour is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Add Onboarding Tour in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lightdash/lightdash/tree/main/.claude/skills/add-onboarding-tour. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Add Onboarding Tour?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Add Onboarding Tour?

As many as you like. As long as a model supports skills, you can use Add Onboarding Tour with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Add Onboarding Tour AI skill free?

It is published on GitHub by lightdash. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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