Lightdash Analytics

OrganizationPopular

lightdash

lightdash-analytics

Use when answering business questions, exploring governed metrics, querying Lightdash data, or creating Lightdash charts and dashboards through the Lightdash MCP server.

Overview

Publisher	lightdash
Repository	`lightdash`
Skill name	`lightdash-analytics`
Stars	6.1K
Forks	778
Bundled files	Instructions only
Links	Skill source Repository Homepage

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 Lightdash Analytics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/lightdash/lightdash/tree/main/plugins/lightdash/skills/lightdash-analytics

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/plugins/lightdash/skills/lightdash-analytics .claude/skills/lightdash-analytics

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lightdash Analytics 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 Lightdash Analytics 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 Lightdash Analytics 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.

Lightdash Analytics

Use the Lightdash MCP server as the source of truth for the user's governed semantic layer.

Discover before querying

Set the active project if the server requires one.
Use route_agent when available; otherwise list the available explores.
Inspect the selected explore and fields before composing a metric query.
Use verified content as a reference when it matches the request.

Never invent explore names, field IDs, metric definitions, filter values, or query UUIDs. Search field values when a string filter must match an existing value.

Answering questions

Prefer run_metric_query for questions that fit the semantic layer. Use run_sql, when it is available, only when the requested analysis cannot be represented through a governed explore.

If a metric query is still running, poll get_query_result with the returned query UUID. When a visual would clarify the result, render a completed metric query with render_chart.

State the metric, time period, filters, and any important caveats in the answer. Treat an empty result as a valid result rather than a failed query.

Creating content and changing analytics

Before creating a chart or dashboard, inspect the relevant schema and existing content. Validate the query first, then use the server's creation workflow.

For dbt or content-as-code changes, work in a branch and use the Lightdash CLI workflow: preview the change, validate it, review it, then merge. Do not deploy or start AI writeback unless the user explicitly asks for that external change.

Built-in Lightdash skills

The MCP server can expose additional Lightdash skills and references. Use list_skills when the client does not surface MCP resources directly, then read only the skill or reference relevant to the task.

Frequently asked questions

What does the Lightdash Analytics AI skill do?

Use when answering business questions, exploring governed metrics, querying Lightdash data, or creating Lightdash charts and dashboards through the Lightdash MCP server.

Why use Lightdash Analytics on TypingMind?

Because you install it once and use it with any model. Lightdash Analytics 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 Lightdash Analytics in TypingMind?

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

Which AI models can use Lightdash Analytics?

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 Lightdash Analytics?

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

Is the Lightdash Analytics 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Lightdash Analytics

Discover before querying

Answering questions

Creating content and changing analytics

Built-in Lightdash skills

Frequently asked questions

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