dagster-best-practices

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

`dg`

Comprehensive skill for all dg CLI operations. Use natural language to interact with the Dagster

command-line tool.

What you can do:

• Create projects and workspaces
• Scaffold components (assets, schedules, sensors, integrations)
• Launch assets with partitions and configurations
• List definitions and discover components
• View logs and troubleshoot failures

Examples:

```

/dg create a new project called analytics

/dg scaffold a dbt integration

/dg launch all assets with tag:priority=high

/dg show me the logs for the last run

/dg help me debug why my asset failed

```

`dagster-best-practices`

Expert guidance for Dagster patterns and architectural decisions.

What you can do:

• Learn asset design patterns (dependencies, partitions, multi-assets)
• Choose automation strategies (declarative automation, schedules, sensors)
• Understand resource patterns (database connections, API clients, env vars)
• Implement testing strategies (unit tests, integration tests, asset checks)
• Apply ETL patterns (dbt, dlt, Sling integration patterns)
• Organize project structure (single project vs workspace)

Example questions:

```

How should I structure my assets?

When should I use schedules vs sensors?

How do I test my data pipeline?

What's the best way to integrate dbt?

```

`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?

```

See [CONTRIBUTING.md](./CONTRIBUTING.md).