Motherduck Build Data Pipeline

Organization

motherduckdb

motherduck-build-data-pipeline

Build ingestion-to-serving pipelines on MotherDuck, including stage boundaries, transformations, and publication. Use for any recurring ingestion from an API, SaaS app, or database into MotherDuck.

Overview

Publisher	motherduckdb
Repository	`agent-skills`
Skill name	`motherduck-build-data-pipeline`
Stars	62
Forks	6
Bundled files	24
License	MIT
Links	Skill source Repository

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.
24 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 Build Data Pipeline 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.
https://github.com/motherduckdb/agent-skills/tree/main/plugins/motherduck-skills-claude/skills/motherduck-build-data-pipeline
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/motherduckdb/agent-skills/tree/main/plugins/motherduck-skills-claude/skills/motherduck-build-data-pipeline

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/motherduckdb/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/plugins/motherduck-skills-claude/skills/motherduck-build-data-pipeline .claude/skills/motherduck-build-data-pipeline

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

Use it in TypingMind

Enable Motherduck Build Data Pipeline 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 Build Data Pipeline 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 Build Data Pipeline 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.

Build a Data Pipeline with MotherDuck

Start Here: Is a MotherDuck Server Active?

Use an active remote MotherDuck MCP server or local MotherDuck server to inspect the in-scope database, schema, grain, keys, and relevant metrics. Reuse known context and narrow discovery to the requested work; do not scan the whole workspace by default. Let the actual data model shape the result.

Resolve the target from the request or active context. Ask only if ambiguity materially affects the result. Without a server, use supplied schema and explicit assumptions for planning; do not imply live validation.

Pipeline Defaults

batch over streaming
raw landing before curation
explicit raw -> staging -> analytics boundaries
bulk ingest paths over row-by-row writes: stage files and load with read_parquet/read_json_auto or one INSERT ... SELECT; never executemany() or per-row INSERT loops against MotherDuck
idempotent stage rebuilds or append contracts before scheduled automation
verify the MotherDuck-supported DuckDB client version before recommending upstream-only write, checkpoint, or lakehouse features
native MotherDuck storage unless DuckLake is explicitly required
MotherDuck CLI for Flight source and large file-shaped output when the agent has a shell; MCP for chat-only operation
a flights Guide for reusable scheduling, naming, secret, and ingestion conventions when the organization has them
Flights sized to their Python work (F4 when the job mostly sends SQL) and instrumented for the Flight Explainer so each run shows step status and the failing step
AI enrichment (classification with prompt_jev(), extraction with prompt()) as an incremental transform step that writes results to a table, never recomputed at read time

Workflow

Inspect the available MotherDuck server or supplied source and target context, including the current target data model.
Define raw, staging, and analytics boundaries.
Ingest raw data.
Deduplicate, type, and promote into staging.
Materialize analytics-ready outputs.
Validate counts, freshness, uniqueness, and business metrics before publishing downstream assets.
When durable context is part of delivery, capture stable business definitions and operating caveats in referenced Guides; keep executable transformation logic in source control.

Match execution to the request: answer, review, or planning work returns the requested pipeline artifacts; build or change work creates the requested in-scope files and warehouse objects and validates them. Ask before destructive actions, unrelated external writes, or a material expansion of scope.

When this skill produces a native DuckDB (md:) connection, watermark it with custom_user_agent=agent-skills/3.0.0(harness-<harness>;llm-<llm>). If metadata is missing, fall back to harness-unknown and llm-unknown.

Output

For a full engagement, cover the following as relevant to the request:

the stage layout
the ingestion method
the transformation sequence
the serving tables or views
the validation checks

For explicit structured JSON requests, read the output contract. Otherwise use the format that fits the requested deliverable.

References

Read only the sections relevant to the task; these are guidance, not a mandatory itinerary.

references/dlt-dbt-motherduck-project/ -- fully runnable MotherDuck reference project using dlt, dbt-duckdb, and validation queries
references/PIPELINE_IMPLEMENTATION_GUIDE.md -- stage design, transformation sequencing, and ingestion-to-serving examples

Examples

Read the execution reference only to run the bundled examples or reproduce their validation.

pipeline_stage_example.py
pipeline_stage_example.ts

Related Skills

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

motherduck-connect -- choose the right connection path
motherduck-load-data -- ingestion mechanics
motherduck-model-data -- shape the analytics layer
motherduck-query -- write transformations and validations
motherduck-share-data -- publish curated outputs
motherduck-ducklake -- only when open-table-format storage is a real requirement
motherduck-create-flight -- schedule and run the pipeline on MotherDuck compute

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 Build Data Pipeline AI skill do?

Build ingestion-to-serving pipelines on MotherDuck, including stage boundaries, transformations, and publication. Use for any recurring ingestion from an API, SaaS app, or database into MotherDuck.

Why use Motherduck Build Data Pipeline on TypingMind?

Because you install it once and use it with any model. Motherduck Build Data Pipeline 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 Build Data Pipeline 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-build-data-pipeline. 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 Build Data Pipeline?

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 Build Data Pipeline?

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

Is the Motherduck Build Data Pipeline 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Build a Data Pipeline with MotherDuck

Start Here: Is a MotherDuck Server Active?

Pipeline Defaults

Workflow

Output

References

Examples

Related Skills

Bundled files

Frequently asked questions

Related AI Skills

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