dagster-expert
SolidExpert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, components, data tools or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept.
Install
Quality Score: 86/100
Skill Content
Details
- Author
- dagster-io
- Repository
- dagster-io/skills
- Created
- 7 months ago
- Last Updated
- today
- Language
- Python
- License
- Apache-2.0
Integrates with
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
building-dagster-assets
Build Dagster pipelines using software-defined assets — asset dependencies, partitions, resources and IO managers, asset checks, and schedules/sensors. Use when creating Dagster assets or jobs, modeling data as assets, adding partitions or backfills, wiring resources/IO managers, or migrating from task-based orchestration to assets.
dagster-orchestration-adapter
Run the medallion sequence UNATTENDED / in CI as a Dagster asset graph -- the scheduler sibling of the retail-orchestrate conductor -- running ONLY already-approved steps behind every gate, and recording what each asset DID as DERIVED run-evidence. Use when someone asks to schedule the pipeline, run the medallion in CI, "orchestrate unattended", or wire Dagster in the Seshat BI repo. Dagster RUNS approved steps; Tower BI (the gate exit code + the named human) decides whether a stage passed. This skill READS committed approvals as the GO signal, EXECUTES approved steps behind STOP / HUMAN-SEAM edges, and WRITES run-evidence; it never defines meaning, never moves a stage to pass, never publishes Power BI (it only TRIGGERS F016), and HARD-STOPS at every human judgment call.
airflow-dags
Use when creating, debugging, or configuring Apache Airflow 3 DAGs — builds data pipelines with TaskFlow API or traditional operators, configures scheduling and asset-driven triggers, wires XCom data passing, sets up sensors and deferrable operators, generates dynamic task mappings, and structures multi-layer test suites. Triggers on DAG authoring, TaskFlow API, operators, sensors, scheduling, assets, dynamic tasks, XCom, or pipeline testing.