Dagster Expert logo

Dagster Expert

Organization
Kilo-Org
dagster-expert

This skill should be used for Dagster project work involving assets, components, integrations, or the dg CLI; it requires consulting the bundled command and API references before proposing implementation details.

Overview

PublisherKilo-Org
Repositorykilo-marketplace
Skill namedagster-expert
Stars
179
Forks
168
Bundled files
173
LicenseApache-2.0
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.

  • 173 bundled files

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

  • Open source

    Published by Kilo-Org on GitHub. Read the source before you install it.

Installation

Install the Dagster Expert 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.
  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/Kilo-Org/kilo-marketplace.git /tmp/kilo-marketplace
mkdir -p .claude/skills
cp -r /tmp/kilo-marketplace/skills/dagster-expert .claude/skills/dagster-expert
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dagster Expert 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 Dagster Expert 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 Dagster Expert 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.

Core Dagster Concepts

Brief definitions only (see reference files for detailed examples):

  • Asset: Persistent object (table, file, model) produced by your pipeline
  • Component: Reusable building block that generates definitions (assets, schedules, sensors, jobs, etc.) relevant to a particular domain.

Integration Workflow

When integrating with ANY external tool or service, read the Integration libraries index. This contains information about which integration libraries exist, and references on how to create new custom integrations for tools that do not have a published library.

dg CLI

The dg CLI is the recommended way to programmatically interact with Dagster (adding definitions, launching runs, exploring project structure, etc.). It is installed as part of the dagster-dg-cli package. If a relevant CLI command for a given task exists, always attempt to use it.

ONLY explore the existing project structure if it is strictly necessary to accomplish the user's goal. In many cases, existing CLI tools will have sufficient understanding of the project structure, meaning listing and reading existing files is wasteful and unnecessary.

Almost all dg commands that return information have a --json flag that can be used to get the information in a machine-readable format. This should be preferred over the default table output unless you are directly showing the information to the user.

UV Compatibility

Projects typically use uv for dependency management, and it is recommended to use it for dg commands if possible:

bash
uv run dg list defs
uv run dg launch --assets my_asset

CRITICAL: Always Read Reference Files Before Answering

NEVER answer from memory or guess at CLI commands, APIs, or syntax. ALWAYS read the relevant reference file(s) from the Reference Index below before responding.

For every question, identify which reference file(s) are relevant using the index descriptions, read them, then answer based on what you read.

Reference Index

  • Asset Selection Syntax — filtering assets by tag, group, kind, upstream, or downstream; AssetSelection in Python, UI search bar, or CLI
  • Environment Variables — configuring environment variables across different environments
  • Asset Patterns — defining assets, dependencies, metadata, partitions, or multi-asset definitions
  • Choosing an Automation Approach — deciding between schedules, sensors, and declarative automation
  • Schedules — time-based automation with cron expressions
  • Declarative Automation — asset-centric condition-based automation using AutomationCondition
  • Asset Sensors — triggering on asset materialization events
  • Basic Sensors — event-driven automation with file watching or custom polling
  • Run Status Sensors — reacting to run success, failure, or other status changes
  • dg check — validating project configuration or definitions
  • create-dagster — creating a new Dagster project from scratch
  • dg dev — starting a local Dagster development instance
  • dg launch — materializing assets or executing jobs locally
  • dg list components — seeing available component types for scaffolding
  • dg list defs — listing or filtering registered definitions
  • Dagster Plus API — dg api, programmatically querying or managing Dagster Plus resources (assets, runs, deployments, code locations, schedules, sensors, secrets, issues, etc.)
  • dg list — exploring project structure (component tree, environment variables, workspace projects)
  • Dagster Plus CLI — dg plus, Dagster Plus authentication, configuration, and deployment; logging in, setting config, creating API tokens, deploying code, pulling env vars, managing dbt manifests
  • dg scaffold component — creating a custom reusable component type
  • dg scaffold defs — adding new definitions (assets, schedules, sensors, components) to a project
  • dg utilities — dg utils, inspecting component types, viewing integrations, refreshing state-backed component cache
  • Creating Components — building a new custom component from scratch
  • Designing Component Integrations — designing a component that wraps an external service or tool; custom integrations
  • Resolved Framework — defining custom YAML schema types using Resolver, Model, or Resolvable
  • Subclassing Components — extending an existing component via subclassing; customize dagster integration component
  • Template Variables — using Jinja2 template variables in component YAML (env, dg, context, or custom scopes)
  • Creating State-Backed Components — building a component that fetches and caches external state
  • Using State-Backed Components — managing state-backed components in production, CI/CD, or refreshing state
  • Deployment Configuration Files — build.yaml, container_context.yaml, dagster_cloud.yaml; Dagster Plus deployment configuration; configuring Docker registry, container context, agent queue; Hybrid deployment files
  • Integration libraries index for 40+ tools and technologies (dbt, Fivetran, Snowflake, AWS, etc.). — integration, external tool, dagster-*; dbt, fivetran, airbyte, snowflake, bigquery, sling, aws, gcp
  • Migration Guides — sensor migration to declarative automation, sensor migration to automation condition

Bundled files

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

and 113 more files.

Frequently asked questions

What does the Dagster Expert AI skill do?

This skill should be used for Dagster project work involving assets, components, integrations, or the dg CLI; it requires consulting the bundled command and API references before proposing implementation details.

Why use Dagster Expert on TypingMind?

Because you install it once and use it with any model. Dagster Expert 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 Dagster Expert in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/dagster-expert. 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 Dagster Expert?

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 Dagster Expert?

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

Is the Dagster Expert AI skill free?

Yes. It is published on GitHub by Kilo-Org under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇