Dagster

Use this skill for Dagster OSS framework and repository tasks. Dagster is an orchestration platform for building, running, and observing data assets, jobs, schedules, sensors, resources, and external-process integrations.

First Checks

1. Confirm whether the user is using Dagster in an application project or editing the Dagster OSS repository itself.
2. For application work, confirm dagster is installed in the active Python environment and run a minimal import check when useful:

python - <<'PY'
import dagster as dg
print(dg.__version__)
PY

3. For local CLI work, prefer help-only checks before executing user code:

dagster --help
dagster definitions validate --help
dagster-webserver --help
dagster-graphql --help

4. Read references/repo-provenance.md before deciding whether this generated skill is current for a Dagster checkout or needs refresh.
5. Use references/troubleshooting.md for cross-cutting install/import, optional dependency, target loading, and environment issues before routing deeper.
6. Use scripts/dagster_skill_doctor.py --help for a safe local package and command availability probe.

Route By Task

• Assets, jobs, ops, and Definitions: use sub-skills/asset-definitions/SKILL.md for @asset, @multi_asset, @asset_check, @op, @job, Definitions, define_asset_job, partitions, backfills, selections, and local materialization tests.
• Config and resources: use sub-skills/configuration-resources/SKILL.md for Config, RunConfig, ConfigurableResource, EnvVar, resource dependencies, IO managers, and resource tests.
• Schedules, sensors, and automation: use sub-skills/automation-schedules-sensors/SKILL.md for schedules, sensors, run requests, cursors, asset sensors, run status sensors, declarative automation, freshness, and backfill automation.
• Local CLI and development server: use sub-skills/cli-local-development/SKILL.md for dagster project, dagster dev, dagster definitions validate, asset/job execution, workspace targets, schedule/sensor commands, instance inspection, and debug commands.
• Deployment and operations: use sub-skills/deployment-operations/SKILL.md for DAGSTER_HOME, dagster.yaml, daemon/webserver services, run launchers, run coordinators, storage, Docker/Kubernetes/ECS checklists, monitoring, and operational failures.
• GraphQL and webserver: use sub-skills/graphql-and-webserver/SKILL.md for dagster-webserver, dagster-graphql, DagsterGraphQLClient, remote GraphQL URLs, path prefixes, headers/auth, read-only mode, and API troubleshooting.
• Pipes external processes: use sub-skills/pipes-external-processes/SKILL.md for dagster-pipes, open_dagster_pipes, external process message protocols, materialization/check reporting, loaders, writers, and subprocess patterns.
• Components and projects: use sub-skills/components-projects/SKILL.md for component-ready projects, component scaffolding, component YAML/templates, dagster.components, dg, create-dagster, and current project-tooling caveats.
• Dagster repo edits: use sub-skills/repo-development/SKILL.md for package lookup, Python/UI/docs validation, coding conventions, mandatory make ruff, uv, focused tests, and git/stack constraints in the Dagster OSS monorepo.

Installation Notes

• Public application users normally install dagster; add dagster-webserver, dagster-graphql, dagster-pipes, or integration packages only when the workflow needs them.
• The generated skill verified core packages and help commands for dagster, dagster-webserver, and dagster-graphql at version 1!0+dev from the source snapshot.
• dg and create-dagster project tooling are covered from source/docs evidence, but this checkout’s dagster-dg-core depends on an unpublished dagster-cloud-cli==1!0+dev package; verify those entry points in the user’s environment before relying on them.
• Broad integration libraries, Dagster Cloud account administration, UI internals, Helm templates, CI/release automation, and credentialed/service-specific examples are intentionally long-tail gaps unless a user explicitly asks to extend this skill.

Safety

• Do not start long-running services, run destructive CLI commands, wipe assets, migrate instances, deploy infrastructure, push images, or mutate production schedule/sensor state without explicit user approval.
• Keep credentials in environment variables or secret managers; do not write secrets into dagster.yaml, workspace files, Dockerfiles, manifests, or generated code.
• For repo edits, always follow sub-skills/repo-development/SKILL.md; after any Python code change in the Dagster repo, make ruff from the repo root is mandatory.