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开发与工程 · SKILL.md

cli-local-development

cli-local-development

【开发与工程】Use when a coding agent needs to run local Dagster OSS CLI commands for project scaffolding, workspace/code-location loading, local development, definitions validation, asset materialization, job execution/launch/listing, schedule/sensor operations, instance inspection, or debug export/import.

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技能说明

GitHub

name: cli-local-development description: "Use when a coding agent needs to run local Dagster OSS CLI commands for project scaffolding, workspace/code-location loading, local development, definitions validation, asset materialization, job execution/launch/listing, schedule/sensor operations, instance inspection, or debug export/import." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

CLI Local Development

Use this sub-skill for local dagster command-line workflows in an installed Dagster Python environment. It covers commands that inspect or run user code locally, validate code locations, start dagster dev, execute jobs/assets, manage schedule/sensor state, inspect the current instance, and collect debug artifacts.

Route First

  • Use this sub-skill for dagster project, dagster dev, dagster definitions validate, dagster asset, dagster job, dagster schedule, dagster sensor, dagster instance, and dagster debug usage.
  • Route webserver startup internals, GraphQL API calls, GraphQL client code, and webserver debug viewing to ../graphql-and-webserver/SKILL.md if that sub-skill exists.
  • Route production instance storage, daemon deployment, run launcher configuration, and operational deployment configuration to ../deployment-operations/SKILL.md if that sub-skill exists.
  • Route dg component/project tooling to ../components-projects/SKILL.md if that sub-skill exists; many legacy dagster commands now print supersession warnings but remain useful for local CLI support.

Start Here

  1. Confirm the environment can import Dagster and expose the needed commands with python scripts/dagster_cli_doctor.py from this sub-skill directory, or run its path from elsewhere.
  2. Choose a loading target deliberately: -f/--python-file, -m/--module-name, --package-name, -a/--attribute, -d/--working-directory, or -w/--workspace.
  3. Validate loading before executing work: dagster definitions validate ... with the same target flags.
  4. For run config, use either repeated -c/--config YAML files/patterns or a single --config-json string, never both.
  5. For persistent schedule/sensor/instance behavior, set DAGSTER_HOME; otherwise many commands use an ephemeral instance and state may disappear after the command exits.

References

  • Local CLI workflows for concrete command patterns, target selection, config merging, project scaffolding, local dev, validation, assets, jobs, schedules, sensors, instances, and debug commands.
  • Troubleshooting for import failures, target/load errors, optional dependency gaps, config misuse, partitions, schedule/sensor state, and instance surprises.
  • Dagster CLI doctor script for a safe environment and --help smoke check that does not start services or execute user code.

Safety Notes

  • dagster dev starts a local webserver and daemon; do not run it as a smoke test unless the user asked to start services.
  • dagster asset wipe, schedule/sensor start/stop, dagster instance migrate, dagster instance reindex, and debug import mutate local Dagster state; confirm intent before running them.
  • dagster project from-example may download examples; prefer dagster project list-examples or scaffold commands for offline checks.
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