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.