blueprint

Name: blueprint
Availability: InStock
Author: miptah21

Generate validated Airflow DAGs from declarative YAML step definitions.

2|Updated Apr 8, 2026

Search Tags:#yaml #airflow #runtime overrides #pydantic validation #task groups #dag templates #no-code orchestration

Ready to run

npx skills add https://github.com/miptah21/skills --skill blueprint-miptah21

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

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.

Core Features & Use Cases

Reusable task-group templates: Define Airflow building blocks as Blueprints with Pydantic-validated config models.
Compose DAGs from YAML: Build complete DAGs by referencing blueprints and passing validated step configuration (including dependency wiring).
Runtime-safe configuration overrides: Support Airflow params to override config fields at trigger time while preserving schema constraints and error visibility.

Quick Start

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.

Frequently Asked Questions about blueprint

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I define Airflow DAGs in YAML instead of writing Python code?▼

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.

Can I override Airflow DAG configuration parameters at runtime using YAML?▼

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.

What is the best way to standardize Airflow task groups across multiple DAGs?▼

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.

Do I need Pydantic validation for Airflow YAML DAG templates?▼

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.

What are the limitations of declarative YAML orchestration in Airflow?▼

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.

Does Blueprint work with Airflow post-build callbacks and dependency graphs?▼

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.