Generate validated Airflow DAGs from declarative YAML step definitions.
npx skills add https://github.com/miptah21/skills --skill blueprint-miptah21
Please help me install this Agent Skill. Skill: blueprint Source: https://github.com/miptah21/skills/tree/main/.agents/skills/blueprint Command: npx skills add https://github.com/miptah21/skills --skill blueprint-miptah21
Blueprint eliminates fragile, hand-written Airflow DAG code by letting you define standardized pipelines in YAML that are validated with strong Pydantic schemas before execution.
Use the blueprint skill to generate a validated Airflow DAG from a YAML file named my_pipeline.dag.yaml using the blueprints defined in your dags/templates directory.
High-intent search queries and answers about installing and using this skill.
Airflow DAGs can be defined declaratively in YAML and validated with Pydantic schemas to ensure configuration correctness. The Blueprint skill transforms these validated YAML step definitions into production-ready Airflow DAGs, eliminating fragile hand-written DAG code.
Runtime overrides are supported by passing Airflow params to override YAML configuration fields at trigger time. This preserves Pydantic schema constraints and error visibility, ensuring that dynamically supplied values remain validated and runtime-safe.
Standardizing Airflow task groups is achieved by defining reusable task-group templates as Blueprints with Pydantic-validated configuration models. You then compose complete DAGs by referencing these blueprints and passing validated step configuration, including dependency wiring.
Pydantic validation is required to enforce strong schemas on YAML inputs before execution. It ensures that declarative Airflow DAG definitions have consistent schemas, controlled configurability, and clear error visibility, preventing invalid configurations from building.
Declarative YAML orchestration requires Python 3.10+, Airflow 2.5+, and the airflow-blueprint library. Users are constrained by predefined Pydantic schemas and blueprint templates, meaning highly custom Python logic outside the defined step configuration may not be supported.
Yes, the Blueprint skill supports post-build callbacks and dependency graph wiring within the YAML definitions. It composes production DAGs by validating step configuration and mapping dependencies between referenced blueprints.
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