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: 10 days 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 (about 1 month ago)
- Default Branch: master
- Last Pushed: 2026-01-30T00:16:50.000Z (18 days ago)
- Last Synced: 2026-01-30T04:33:14.568Z (17 days 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
Awesome Lists containing this project
README
# Dagster Skills
[](https://github.com/dagster-io/skills/actions/workflows/lint.yml)
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
```
### 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
```
### `dagster-integrations`
Comprehensive catalog of 82+ Dagster integrations organized by category.
**What's included:**
- **AI & ML**: OpenAI, Anthropic, Gemini, MLflow, W&B
- **ETL/ELT**: dbt, Fivetran, Airbyte, dlt, Sling, PySpark
- **Storage**: Snowflake, BigQuery, Postgres, S3, DuckDB, Weaviate
- **Compute**: AWS, Azure, GCP, Databricks, Spark, Kubernetes
- **BI**: Looker, Tableau, PowerBI, Sigma, Hex
- **Monitoring**: Datadog, Prometheus, Papertrail
- **Alerting**: Slack, PagerDuty, MS Teams, Discord, Twilio
- **Testing**: Great Expectations, Pandera
- **Other**: Pandas, Polars
**Example questions:**
```
Which tool should I use for data warehousing?
Does Dagster support dbt?
How do I compare Snowflake vs BigQuery?
What integrations are available for ML?
```
### `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).
