Dagster Expert

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.

When integrating with ANY external tool or service, read the Integration libraries index (opens in a new tab). 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.

Programmatic Access: `dg` CLI and the Dagster Plus MCP server

There are two ways to interact with Dagster programmatically. Pick by where the work happens:

The dg CLI — everything in the local project: adding definitions, scaffolding, validating, exploring project structure, and launching runs locally. Installed as part of the dagster-dg-cli package. If a relevant CLI command for a local task exists, always attempt to use it.
The Dagster Plus MCP server — querying and managing a deployed Dagster Plus organization: runs, assets, deployments, code locations, alert policies, Issues, and insights metrics. When it is connected, prefer its tools over the equivalent dg api commands.

dg api covers the same deployed resources as the MCP server from the command line, and remains the way to reach the parts the server does not expose. Before doing anything against a deployed Dagster Plus environment, read Dagster Plus API: General (opens in a new tab) — it covers how to choose between the two and how to fall back safely.

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:

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 (opens in a new tab) — filtering assets by tag, group, kind, upstream, or downstream; AssetSelection in Python, UI search bar, or CLI
Environment Variables (opens in a new tab) — configuring environment variables across different environments
Asset Patterns (opens in a new tab) — defining assets, dependencies, metadata, partitions, or multi-asset definitions
Choosing an Automation Approach — deciding between schedules, sensors, and declarative automation

Core Dagster Concepts

Integration Workflow

Programmatic Access: `dg` CLI and the Dagster Plus MCP server

UV Compatibility

CRITICAL: Always Read Reference Files Before Answering

Reference Index

Appendix 2.1–2.173

ReferencesMarkdown · 173 files

About this skill

Trigger

Colophon

Appendix 2.1–2.173

ReferencesMarkdown · 173 files

Core Dagster Concepts

Integration Workflow

Programmatic Access: dg CLI and the Dagster Plus MCP server

UV Compatibility

CRITICAL: Always Read Reference Files Before Answering

Reference Index

Appendix 2.1–2.173

ReferencesMarkdown · 173 files

About this skill

Trigger

Colophon

Appendix 2.1–2.173

ReferencesMarkdown · 173 files

Programmatic Access: `dg` CLI and the Dagster Plus MCP server