Upgrading Mwaa Environments
aws/agent-toolkit-for-aws
Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary.
Expert guidance for working with Dagster and the dg CLI. An agent skill from dagster-io/skills.
Install Dagster Expert
$ npx skills add dagster-io/skills --skill dagster-expert -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install dagster-io/skills dagster-expert --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/dagster-io/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/dagster/skills/dagster-expert .claude/skills/dagster-expert && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "dagster-expert" agent skill from https://github.com/dagster-io/skills/tree/master/plugins/dagster/skills/dagster-expert into .claude/skills/dagster-expert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dagster-expert", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/dagster-io/skills/tree/master/plugins/dagster/skills/dagster-expertType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add dagster-io/skills --skill dagster-expert -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install dagster-io/skills dagster-expert --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dagster-io/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/dagster/skills/dagster-expert .agents/skills/dagster-expert && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "dagster-expert" agent skill from https://github.com/dagster-io/skills/tree/master/plugins/dagster/skills/dagster-expert into .agents/skills/dagster-expert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dagster-expert", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add dagster-io/skills --skill dagster-expert -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install dagster-io/skills dagster-expert --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dagster-io/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/dagster/skills/dagster-expert .cursor/skills/dagster-expert && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "dagster-expert" agent skill from https://github.com/dagster-io/skills/tree/master/plugins/dagster/skills/dagster-expert into .cursor/skills/dagster-expert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dagster-expert", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/dagster-io/skills.git --path plugins/dagster/skills/dagster-expert--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add dagster-io/skills --skill dagster-expert -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install dagster-io/skills dagster-expert --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dagster-io/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/dagster/skills/dagster-expert .gemini/skills/dagster-expert && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "dagster-expert" agent skill from https://github.com/dagster-io/skills/tree/master/plugins/dagster/skills/dagster-expert into .gemini/skills/dagster-expert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dagster-expert", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install dagster-io/skills dagster-expertInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add dagster-io/skills --skill dagster-expert -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/dagster-io/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/dagster/skills/dagster-expert .github/skills/dagster-expert && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "dagster-expert" agent skill from https://github.com/dagster-io/skills/tree/master/plugins/dagster/skills/dagster-expert into .github/skills/dagster-expert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dagster-expert", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add dagster-io/skills --skill dagster-expert -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install dagster-io/skills dagster-expert --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/dagster-io/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/dagster/skills/dagster-expert .opencode/skills/dagster-expert && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "dagster-expert" agent skill from https://github.com/dagster-io/skills/tree/master/plugins/dagster/skills/dagster-expert into .opencode/skills/dagster-expert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dagster-expert", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
dagster-expert- GitHub stars
- 211
- Token cost
- ~2k tokens
- SKILL.md length
- 809 words
- Files
- 262 (incl. references)
- Skills in repo
- 2
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Expert guidance for working with Dagster and the dg CLI. An agent skill from dagster-io/skills.
- Tasks that involve Data pipelines and ETL
- SKILL.md covers Core Dagster Concepts, Integration Workflow, Programmatic Access: dg CLI… and UV Compatibility, plus 2 more sections
- Calls uv
What it does
Dagster Expert is an agent skill from dagster-io/skills, published by the product's own GitHub organization. Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, components, data tools or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific…
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 266 other files, including reference files (for example `references/asset-selection.md`, `references/assets/INDEX.md` and `references/assets/advanced-patterns.md`).
It sits in Data & Analytics, covering Data pipelines and ETL. It works with Dagster and Model Context Protocol. The repository describes itself as: A collection of AI skills for working with Dagster. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Data pipelines and ETL
Example prompts
- “/dagster-expert”
Requirements
- Docker
What it can do on your machine
Read from SKILL.md and the folder at commit 8547cac. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Dagster Expert loads about 2k tokens when it runs, and up to ~59k if it reads all its reference files. Until then it costs about 137 tokens; SKILL.md has 809 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from dagster-io/skills at commit 8547cac, republished under its Apache-2.0 licence (© dagster-io). 809 words, ~2,036 tokens.
Download SKILL.mdSave it as
.claude/skills/dagster-expert/SKILL.md (or your agent's skills folder). This skill also uses 261 other files; get the full folder from GitHub.- name
- dagster-expert
- description
- Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, components, data tools or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept.
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.
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
dgCLI — everything in the local project: adding definitions, scaffolding, validating, exploring project structure, and launching runs locally. Installed as part of thedagster-dg-clipackage. 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 apicommands.
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 — 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_assetCRITICAL: 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.
Show full SKILL.md (419 more words)Show less
Reference Index
<!-- BEGIN GENERATED 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 or the Dagster Plus MCP server, programmatically querying or managing Dagster Plus resources (assets, runs, deployments, code locations, schedules, sensors, secrets, issues, alert policies, etc.); Dagster credits, compute or warehouse cost, usage, and other insights metrics for an asset, job, or deployment; retrying or re-executing a failed run or backfill; terminating a run
- 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, 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
<!-- END GENERATED INDEX -->© dagster-io, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 261 other files (references) in plugins/dagster/skills/dagster-expert of dagster-io/skills.
- SKILL.md
- references/asset-selection.md
- references/assets/INDEX.md
- references/assets/advanced-patterns.md
- references/assets/definition-metadata.md
- references/assets/dependencies.md
- references/automation/choosing-automation.md
- references/automation/declarative-automation/INDEX.md
- references/automation/declarative-automation/advanced.md
- references/automation/declarative-automation/core-concepts.md
- references/automation/declarative-automation/customization.md
- references/automation/declarative-automation/operands.md
- references/automation/declarative-automation/operators.md
- references/automation/schedules.md
- references/automation/sensors/asset-sensors.md
- references/automation/sensors/basic-sensors.md
- … and 246 more
Open the folder on GitHubat commit 8547cac
Compare with similar skills
Dagster Expert next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Dagster Expert this skilldagster-io/skills | 211 | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Upgrading Mwaa Environmentsaws/agent-toolkit-for-aws | 2.8k | — | ~7.3k | Automated safety check: Pass | Apache-2.0 | |
| Tushare Plugin BuilderYourdaylight/stock_datasource | 189 | — | ~2.5k | Automated safety check: Pass | MIT | |
| Monte Carlo Performance Diagnosissickn33/agentic-awesome-skills | 47k | 1 repos | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Authoring Mwaa Workflowaws/agent-toolkit-for-aws | 2.8k | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Configuring Dbt MCP ServerKilo-Org/kilo-marketplace | 190 | — | ~2.5k | Automated safety check: Notes | Apache-2.0 |
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More from dagster-io/skills
- Official
Dagster Expert
dagster-io/skills
A skill your agent uses for any task involving Dagster, the dg CLI, assets, materialization, components, data tools or data pipelines, matching the situations the Dagster skill handles.
Related
Works with
Categories
Questions about Dagster Expert
What does Dagster Expert do?
Expert guidance for working with Dagster and the dg CLI. An agent skill from dagster-io/skills. Dagster Expert is an agent skill from dagster-io/skills, published by the product's own GitHub organization. Expert guidance for working with Dagster and the dg CLI.
When should I use Dagster Expert?
Dagster Expert fits situations like: tasks that involve Data pipelines and ETL.
How do I install Dagster Expert in Claude Code?
Run `npx skills add dagster-io/skills --skill dagster-expert -a claude-code`. Or copy the skill folder (plugins/dagster/skills/dagster-expert in dagster-io/skills) into .claude/skills/dagster-expert in your project. Claude Code loads it when a task matches its description.
How do I install Dagster Expert in Codex?
Run `npx skills add dagster-io/skills --skill dagster-expert -a codex`. Or copy the skill folder (plugins/dagster/skills/dagster-expert in dagster-io/skills) into .agents/skills/dagster-expert in your project. Codex loads it when a task matches its description.
Can I use Dagster Expert in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add dagster-io/skills --skill dagster-expert -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dagster-expert, .gemini/skills/dagster-expert, .github/skills/dagster-expert and .opencode/skills/dagster-expert in your project.
What does Dagster Expert need to run?
Going by SKILL.md and its folder, Dagster Expert needs the command-line tools its instructions call (uv). Our summary lists: Docker.
Does Dagster Expert access the network?
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Dagster Expert safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Dagster Expert use?
Dagster Expert is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Dagster Expert use?
About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 57k tokens, read only when the agent opens those files.
What are the alternatives to Dagster Expert?
Skills that share tags, products or a category with Dagster Expert: Upgrading Mwaa Environments (aws/agent-toolkit-for-aws, 2.8k stars), Tushare Plugin Builder (Yourdaylight/stock_datasource, 189 stars), Monte Carlo Performance Diagnosis (sickn33/agentic-awesome-skills, 47k stars) and Authoring Mwaa Workflow (aws/agent-toolkit-for-aws, 2.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Dagster Expert?
dagster-io (a GitHub organization, an official publisher) maintains it in dagster-io/skills, which has 211 GitHub stars. The repository holds 2 skills in this directory. The repository was last updated on October 9, 2026.
Source: dagster-io/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.