https://github.com/dagster-io/skills
A collection of Claude Code plugins for working with Dagster.
https://github.com/dagster-io/skills
ai-tools claude-code dagster data-engineering data-orchestration marketplace
Last synced: 2 months ago
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A collection of Claude Code plugins for working with Dagster.
- Host: GitHub
- URL: https://github.com/dagster-io/skills
- Owner: dagster-io
- Created: 2026-01-14T15:12:14.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2026-01-30T00:16:50.000Z (5 months ago)
- Last Synced: 2026-01-30T04:33:14.568Z (5 months ago)
- Topics: ai-tools, claude-code, dagster, data-engineering, data-orchestration, marketplace
- Language: Python
- Homepage: https://dagster.io
- Size: 327 KB
- Stars: 30
- Watchers: 0
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
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README
# Dagster Skills
AI assistant skills for building workflows and data pipelines using Dagster.
**Compatible with Claude Code, OpenCode, OpenAI Codex, Pi, and other Agent Skills-compatible tools.**
## Installation
### Claude Code
Install using the
[Claude plugin marketplace](https://code.claude.com/docs/en/discover-plugins#add-from-github):
```
/plugin marketplace add dagster-io/skills
/plugin install dagster-expert@dagster-skills
/dagster-expert "What's an asset?"
```
### Using `npx skills`
Install using the [`npx skills`](https://skills.sh/) command-line:
```bash
npx skills add dagster-io/skills
```
### Manual Installation
See full instructions...
Clone the repository and copy skills to your tool's skills directory:
**OpenCode:**
```bash
git clone https://github.com/dagster-io/skills.git
cp -r skills/skills/* ~/.config/opencode/skill/
```
**OpenAI Codex:**
```bash
git clone https://github.com/dagster-io/skills.git
cp -r skills/skills/* ~/.codex/skills/
```
**Pi Agent:**
```bash
git clone https://github.com/dagster-io/skills.git
cp -r skills/skills/* ~/.pi/agent/skills/
```
## Skills
### `dagster-expert`
Expert guidance for building production-quality Dagster projects, covering CLI commands, asset patterns, automation strategies, and implementation workflows.
**What you can do:**
- Create and scaffold projects, assets, schedules, and sensors
- Understand asset patterns (dependencies, partitions, multi-assets, metadata)
- Implement automation (declarative automation, schedules, sensors)
- Use CLI commands (launch, list, check, scaffold, logs)
- Design project structure and configure environments
- Follow implementation workflows and best practices
- Debug issues and validate project configuration
**Example prompts:**
```
Create a new Dagster project called analytics
How do I scaffold a new asset?
Show me how to set up declarative automation
What's the proper way to partition my assets?
Help me debug why my materialization failed
How should I structure my project for multiple pipelines?
Launch all assets tagged with priority=high
```
### `dignified-python`
Production-quality Python coding standards for modern Python.
Use for general Python code quality, not Dagster-specific patterns.
**What's included:**
- Modern type syntax (list[str], str | None)
- LBYL exception handling patterns
- Pathlib operations
- Python version-specific features (3.10-3.13)
- CLI patterns (Click, argparse)
- Advanced typing patterns
- Interface design (ABC, Protocol)
- API design principles
**Example questions:**
```
Is this good Python code?
How should I annotate this function?
What's the difference between LBYL and EAFP?
Should I use pathlib or os.path?
```
## Contributing
See [CONTRIBUTING.md](./CONTRIBUTING.md).
