blueprint

Name: blueprint
Author: astronomer

360installs55forks

How to install blueprint?

astronomer/agents/blueprint

$npx -y skills add astronomer/agents --skill blueprint

Installs into the current project.

Summary

Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring for non-engineers.

Files · 1

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SKILL.md

1	# Blueprint Implementation
2
3	You are helping a user work with Blueprint, a system for composing Airflow DAGs from YAML using reusable Python templates. Execute steps in order and prefer the simplest configuration that meets the user's needs.
4
5	> Package: `airflow-blueprint` on PyPI
6	> Repo: https://github.com/astronomer/blueprint
7	> Requires: Python 3.10+, Airflow 2.5+, Blueprint 0.3.0+
8
9	## Before Starting
10
11	Confirm with the user:
12	1. Airflow version ≥2.5
13	2. Python version ≥3.10
14	3. Use case: Blueprint is for standardized, validated templates. If user needs full Airflow flexibility, suggest writing DAGs directly or using DAG Factory instead.
15
16	---
17
18	## Determine What the User Needs
19
20	\| User Request \| Action \|
21	\|--------------\|--------\|
22	\| "Create a blueprint" / "Define a template" \| Go to Creating Blueprints \|
23	\| "Build a template from other templates" \| Go to Composing Templates \|
24	\| "Create a DAG from YAML" / "Compose steps" \| Go to Composing DAGs in YAML \|
25	\| "Use a blueprint in an existing Python DAG" / "Generate DAGs in a loop" \| Go to Blueprints in Python DAGs \|
26	\| "Customize DAG args" / "Add tags to DAG" \| Go to Customizing DAG-Level Configuration \|
27	\| "Override config at runtime" / "Trigger with params" \| Go to Runtime Parameter Overrides \|
28	\| "Post-process DAGs" / "Add callback" \| Go to Post-Build Callbacks \|
29	\| "Validate my YAML" / "Lint blueprint" \| Go to Validation Commands \|
30	\| "Set up blueprint in my project" \| Go to Project Setup \|
31	\| "Version my blueprint" \| Go to Versioning \|
32	\| "Generate schema" / "Astro IDE setup" \| Go to Schema Generation \|
33	\| Blueprint errors / troubleshooting \| Go to Troubleshooting \|
34
35	---
36
37	## Project Setup
38
39	If the user is starting fresh, guide them through setup:
40
41	### 1. Install the Package
42
43	```bash
44	# Add to requirements.txt
45	airflow-blueprint>=0.3.0
46
47	# Or install directly
48	pip install airflow-blueprint
49	```
50
51	### 2. Create the Loader
52
53	Create `dags/loader.py`:
54
55	```python
56	from blueprint import build_all_dags
57
58	build_all_dags()
59	```
60
61	> Use `build_all_dags`, not `build_all`. The function was renamed in 0.3.0 so the loader's import line contains the substring `dag`, which Airflow's safe-mode DAG file processor requires — otherwise the file is silently skipped and no DAGs appear. `build_all` still works as a deprecated alias (emits `DeprecationWarning`); migrate existing loaders.
62
63	DAG-level configuration (schedule, description, tags, default_args, etc.) is handled via YAML fields and `BlueprintDagArgs` templates — see Customizing DAG-Level Configuration.
64
65	### 3. Verify Installation
66
67	```bash
68	uvx --from airflow-blueprint blueprint list
69	```
70
71	If no blueprints found, user needs to create blueprint classes first.
72
73	> Provider operators in the CLI. The `uvx --from airflow-blueprint` environment is isolated and does not include the Airflow provider packages your Astro Runtime project has. If your templates import provider operators (BigQuery, Snowflake, etc.), add `--with` so the CLI can import them — otherwise `list`/`lint`/`schema` fail with `ModuleNotFoundError: No module named 'airflow.providers.X'`:
74	>
75	> ```bash
76	> uvx --from airflow-blueprint --with apache-airflow-providers-google blueprint list --template-dir dags/templates
77	> ```
78
79	---
80
81	## Creating Blueprints
82
83	When user wants to create a new blueprint template:
84
85	### Blueprint Structure
86
87	```python
88	# dags/templates/my_blueprints.py
89	from airflow.operators.bash import BashOperator
90	from airflow.utils.task_group import TaskGroup
91	from blueprint import Blueprint, BaseModel, Field
92
93	class MyConfig(BaseModel):
94	# Required field with description (used in CLI output and JSON schema)
95	source_table: str = Field(description="Source table name")
96	# Optional field with default and validation
97	batch_size: int = Field(default=1000, ge=1)
98
99	class MyBlueprint(Blueprint[MyConfig]):
100	"""Docstring becomes blueprint description."""
101
102	def render(self, config: MyConfig) -> TaskGroup:
103	with TaskGroup(group_id=self.step_id) as group:
104	BashOperator(
105	task_id="my_task",
106	bash_command=f"echo '{config.source_table}'"
107	)
108	return group
109	```
110
111	### Key Rules
112
113	\| Element \| Requirement \|
114	\|---------\|-------------\|
115	\| Config class \| Must inherit from `BaseModel` \|
116	\| Blueprint class \| Must inherit from `Blueprint[ConfigClass]` \|
117	\| `render()` method \| Must return `TaskGroup` or `BaseOperator` \|
118	\| Task IDs \| Use `self.step_id` for the group/task ID \|
119	\| Field types \| Must be single-typed and YAML-compatible (see below) \|
120
121	### Config Field Types Must Be YAML-Compatible
122
123	As of 0.3.0, config fields must be single-typed. Multi-type unions like `str \| int` or `Union[

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Type

blueprint

360installs55forks

View on GitHub

How to install blueprint?

astronomer/agents/blueprint

$npx -y skills add astronomer/agents --skill blueprint

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Blueprint Implementation
2
3	You are helping a user work with Blueprint, a system for composing Airflow DAGs from YAML using reusable Python templates. Execute steps in order and prefer the simplest configuration that meets the user's needs.
4
5	> Package: `airflow-blueprint` on PyPI
6	> Repo: https://github.com/astronomer/blueprint
7	> Requires: Python 3.10+, Airflow 2.5+, Blueprint 0.3.0+
8
9	## Before Starting
10
11	Confirm with the user:
12	1. Airflow version ≥2.5
13	2. Python version ≥3.10
14	3. Use case: Blueprint is for standardized, validated templates. If user needs full Airflow flexibility, suggest writing DAGs directly or using DAG Factory instead.
15
16	---
17
18	## Determine What the User Needs
19
20	\| User Request \| Action \|
21	\|--------------\|--------\|
22	\| "Create a blueprint" / "Define a template" \| Go to Creating Blueprints \|
23	\| "Build a template from other templates" \| Go to Composing Templates \|
24	\| "Create a DAG from YAML" / "Compose steps" \| Go to Composing DAGs in YAML \|
25	\| "Use a blueprint in an existing Python DAG" / "Generate DAGs in a loop" \| Go to Blueprints in Python DAGs \|
26	\| "Customize DAG args" / "Add tags to DAG" \| Go to Customizing DAG-Level Configuration \|
27	\| "Override config at runtime" / "Trigger with params" \| Go to Runtime Parameter Overrides \|
28	\| "Post-process DAGs" / "Add callback" \| Go to Post-Build Callbacks \|
29	\| "Validate my YAML" / "Lint blueprint" \| Go to Validation Commands \|
30	\| "Set up blueprint in my project" \| Go to Project Setup \|
31	\| "Version my blueprint" \| Go to Versioning \|
32	\| "Generate schema" / "Astro IDE setup" \| Go to Schema Generation \|
33	\| Blueprint errors / troubleshooting \| Go to Troubleshooting \|
34
35	---
36
37	## Project Setup
38
39	If the user is starting fresh, guide them through setup:
40
41	### 1. Install the Package
42
43	```bash
44	# Add to requirements.txt
45	airflow-blueprint>=0.3.0
46
47	# Or install directly
48	pip install airflow-blueprint
49	```
50
51	### 2. Create the Loader
52
53	Create `dags/loader.py`:
54
55	```python
56	from blueprint import build_all_dags
57
58	build_all_dags()
59	```
60
61	> Use `build_all_dags`, not `build_all`. The function was renamed in 0.3.0 so the loader's import line contains the substring `dag`, which Airflow's safe-mode DAG file processor requires — otherwise the file is silently skipped and no DAGs appear. `build_all` still works as a deprecated alias (emits `DeprecationWarning`); migrate existing loaders.
62
63	DAG-level configuration (schedule, description, tags, default_args, etc.) is handled via YAML fields and `BlueprintDagArgs` templates — see Customizing DAG-Level Configuration.
64
65	### 3. Verify Installation
66
67	```bash
68	uvx --from airflow-blueprint blueprint list
69	```
70
71	If no blueprints found, user needs to create blueprint classes first.
72
73	> Provider operators in the CLI. The `uvx --from airflow-blueprint` environment is isolated and does not include the Airflow provider packages your Astro Runtime project has. If your templates import provider operators (BigQuery, Snowflake, etc.), add `--with` so the CLI can import them — otherwise `list`/`lint`/`schema` fail with `ModuleNotFoundError: No module named 'airflow.providers.X'`:
74	>
75	> ```bash
76	> uvx --from airflow-blueprint --with apache-airflow-providers-google blueprint list --template-dir dags/templates
77	> ```
78
79	---
80
81	## Creating Blueprints
82
83	When user wants to create a new blueprint template:
84
85	### Blueprint Structure
86
87	```python
88	# dags/templates/my_blueprints.py
89	from airflow.operators.bash import BashOperator
90	from airflow.utils.task_group import TaskGroup
91	from blueprint import Blueprint, BaseModel, Field
92
93	class MyConfig(BaseModel):
94	# Required field with description (used in CLI output and JSON schema)
95	source_table: str = Field(description="Source table name")
96	# Optional field with default and validation
97	batch_size: int = Field(default=1000, ge=1)
98
99	class MyBlueprint(Blueprint[MyConfig]):
100	"""Docstring becomes blueprint description."""
101
102	def render(self, config: MyConfig) -> TaskGroup:
103	with TaskGroup(group_id=self.step_id) as group:
104	BashOperator(
105	task_id="my_task",
106	bash_command=f"echo '{config.source_table}'"
107	)
108	return group
109	```
110
111	### Key Rules
112
113	\| Element \| Requirement \|
114	\|---------\|-------------\|
115	\| Config class \| Must inherit from `BaseModel` \|
116	\| Blueprint class \| Must inherit from `Blueprint[ConfigClass]` \|
117	\| `render()` method \| Must return `TaskGroup` or `BaseOperator` \|
118	\| Task IDs \| Use `self.step_id` for the group/task ID \|
119	\| Field types \| Must be single-typed and YAML-compatible (see below) \|
120
121	### Config Field Types Must Be YAML-Compatible
122
123	As of 0.3.0, config fields must be single-typed. Multi-type unions like `str \| int` or `Union[

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