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Motherduck Model Data

Organization
motherduckdb
motherduck-model-data

Design or implement MotherDuck analytical schemas and transformation models, including grain, types, and materialization.

Overview

Publishermotherduckdb
Repositoryagent-skills
Skill namemotherduck-model-data
Stars
62
Forks
6
Bundled files
1
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Motherduck Model Data 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/motherduckdb/agent-skills/tree/main/plugins/motherduck-skills-claude/skills/motherduck-model-data
  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/motherduckdb/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/plugins/motherduck-skills-claude/skills/motherduck-model-data .claude/skills/motherduck-model-data
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Motherduck Model Data 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 Motherduck Model Data 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 Motherduck Model Data 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.

Model Data in MotherDuck

Core Behavior

For multi-model work, keep transformations in reviewable SQL files using the project's existing dbt, SQLMesh, or local conventions. If none exist, use stage directories and a model_manifest.yml recording dependencies, materialization, and target database. A single-table request needs only the requested SQL or change.

Prerequisites

Use the known source schema and connection. Discover missing types, grain, and join keys before implementing; planning can use supplied schema without a live connection.

Default Posture

  • Design for analytical reads, not transactional writes.
  • Prefer wide denormalized tables and pre-aggregated serving tables over highly normalized OLTP-style schemas.
  • Use fully qualified names and add comments to tables and columns. Preserve stable object names so Guides can reference the intended catalog objects reliably.
  • Use NOT NULL aggressively; do not assume primary keys or foreign keys are enforced.
  • Reuse an existing dbt, SQLMesh, or repo-local modeling convention when one is already present; create the lightweight scaffold only when there is no established project shape.
  • Separate raw, staging, and analytics lifecycle stages when the project is non-trivial.

Workflow

  1. Inspect the current source tables and actual column types before designing new models.
  2. Choose the target lifecycle stage and grain for each modeled table. Map dependencies between models.
  3. Place SQL in the existing project, or use the scaffold reference for a new multi-model project.
  4. Author each model as a standalone SQL file. Use explicit types, nullability, comments, and fully qualified names. Decide between a table, CTAS rebuild, or view based on freshness and cost.
  5. Record dependencies and materializations in the project's framework or lightweight manifest, not both.
  6. For implementation, run the in-scope models and verify grain and row counts; MCP DDL and CTAS require query_rw. For an answer, review, or plan, return the requested explanation or SQL without creating a project or mutating the warehouse unless requested.

References

Read only the reference sections needed for the current task.

  • Read references/MODELING_PLAYBOOK.md for schema patterns, data-type guidance, CTAS/view decisions, complex types, constraints, project scaffold conventions, and common modeling mistakes.

Related Skills

Load related skills only for missing capabilities; reuse established context.

  • motherduck-duckdb-sql for type syntax and function details
  • motherduck-query for executing DDL, rebuilds, and validation queries
  • motherduck-explore for understanding the source schema before remodeling
  • motherduck-load-data for ingestion paths that feed the modeled tables
  • motherduck-manage-guides for durable business definitions and join rules that do not belong in transformation code

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Motherduck Model Data AI skill do?

Design or implement MotherDuck analytical schemas and transformation models, including grain, types, and materialization.

Why use Motherduck Model Data on TypingMind?

Because you install it once and use it with any model. Motherduck Model Data 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 Motherduck Model Data in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/motherduckdb/agent-skills/tree/main/plugins/motherduck-skills-claude/skills/motherduck-model-data. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Motherduck Model Data?

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 Motherduck Model Data?

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

Is the Motherduck Model Data AI skill free?

Yes. It is published on GitHub by motherduckdb under the MIT license. 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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