Cosmos + dbt Core: Implementation Checklist

Execute steps in order. Prefer the simplest configuration that meets the user's constraints.

Version note: This skill targets Cosmos 1.11+ and Airflow 3.x. If the user is on Airflow 2.x, adjust imports accordingly (see Appendix A).

Reference: Latest stable: https://pypi.org/project/astronomer-cosmos/

Before starting, confirm: (1) dbt engine = Core (not Fusion → use cosmos-dbt-fusion), (2) warehouse type, (3) Airflow version, (4) execution environment (Airflow env / venv / container), (5) DbtDag vs DbtTaskGroup, (6) manifest availability.

1. Choose Parsing Strategy (RenderConfig)

Pick ONE load mode based on constraints:

Load mode	When to use	Required inputs	Constraints
`dbt_manifest`	Large projects; containerized execution; fastest	`ProjectConfig.manifest_path`	Remote manifest needs `manifest_conn_id`
`dbt_ls`	Complex selectors; need dbt-native selection	dbt installed OR `dbt_executable_path`	Cannot use with containerized execution
`dbt_ls_file`	dbt_ls selection without running dbt_ls every parse	`RenderConfig.dbt_ls_path`	`select` / `exclude` won't work
`automatic`	Simple setups; let Cosmos pick	(none)	Falls back: manifest → dbt_ls → custom

CRITICAL: Containerized execution (
DOCKER
/
KUBERNETES
/etc.) → MUST use
dbt_manifest
load mode.

python

from cosmos import RenderConfig, LoadMode

_render_config = RenderConfig(
    load_method=LoadMode.DBT_MANIFEST,  # or DBT_LS, DBT_LS_FILE, AUTOMATIC
)

2. Choose Execution Mode (ExecutionConfig)

Reference: See reference/cosmos-config.md for detailed configuration examples per mode.

Pick ONE execution mode:

Execution mode	When to use	Speed	Required setup
`WATCHER`	Fastest; single `dbt build` visibility	Fastest	dbt adapter in env OR `dbt_executable_path`
`LOCAL` + `DBT_RUNNER`	dbt + adapter in Airflow env	Fast	dbt 1.5+ in `requirements.txt`
`LOCAL` + `SUBPROCESS`	dbt in venv baked into image	Medium	`dbt_executable_path`
`AIRFLOW_ASYNC`	BigQuery + long-running transforms	Varies	Airflow ≥2.8; provider deps
`VIRTUALENV`	Can't modify image; runtime venv	Slower	`py_requirements` in operator_args
Containerized	Full isolation per task	Slowest	manifest required; container config

CRITICAL: Containerized execution (
DOCKER
/
KUBERNETES
/etc.) → MUST use
dbt_manifest
load mode.

python

from cosmos import ExecutionConfig, ExecutionMode

_execution_config = ExecutionConfig(
    execution_mode=ExecutionMode.LOCAL,  # or WATCHER, VIRTUALENV, AIRFLOW_ASYNC, KUBERNETES, etc.
)

3. Configure Warehouse Connection (ProfileConfig)

Reference: See reference/cosmos-config.md for detailed ProfileConfig options and all ProfileMapping classes.

Option A: Airflow Connection + ProfileMapping (Recommended)

python

from cosmos import ProfileConfig
from cosmos.profiles import SnowflakeUserPasswordProfileMapping

_profile_config = ProfileConfig(
    profile_name="default",
    target_name="dev",
    profile_mapping=SnowflakeUserPasswordProfileMapping(
        conn_id="snowflake_default",
        profile_args={"schema": "my_schema"},
    ),
)

Option B: Existing profiles.yml

CRITICAL: Do not hardcode secrets; use environment variables.

python

from cosmos import ProfileConfig

_profile_config = ProfileConfig(
    profile_name="my_profile",
    target_name="dev",
    profiles_yml_filepath="/path/to/profiles.yml",
)

4. Configure Project (ProjectConfig)

Approach	When to use	Required param
Project path	Files available locally	`dbt_project_path`
Manifest only	`dbt_manifest` load; containerized	`manifest_path` + `project_name`

python

from cosmos import ProjectConfig

_project_config = ProjectConfig(
    dbt_project_path="/path/to/dbt/project",
    # manifest_path="/path/to/manifest.json",  # for dbt_manifest load mode
    # project_name="my_project",  # if using manifest_path without dbt_project_path
    # install_dbt_deps=False,  # if deps precomputed in CI
)

5. Configure Testing Behavior (RenderConfig)

Reference: See reference/cosmos-config.md for detailed testing options.

TestBehavior	Behavior
`AFTER_EACH`	Tests run immediately after each model (default)
`BUILD`	Combine run + test into single `dbt build`
`AFTER_ALL`	All tests after all models complete
`NONE`	Skip tests

python

from cosmos import RenderConfig, TestBehavior

_render_config = RenderConfig(
    test_behavior=TestBehavior.AFTER_EACH,
)

6. Configure operator_args

Reference: See reference/cosmos-config.md for detailed operator_args options.

python

_operator_args = {
    # BaseOperator params
    "retries": 3,

    # Cosmos-specific params
    "install_deps": False,
    "full_refresh": False,
    "quiet": True,

    # Runtime dbt vars (XCom / params)
    "vars": '{"my_var": "{{ ti.xcom_pull(task_ids=\'pre_dbt\') }}"}',
}

7. Assemble DAG / TaskGroup

Option A: DbtDag (Standalone)

python

from cosmos import DbtDag, ProjectConfig, ProfileConfig, ExecutionConfig, RenderConfig
from cosmos.profiles import SnowflakeUserPasswordProfileMapping
from pendulum import datetime

_project_config = ProjectConfig(
    dbt_project_path="/usr/local/airflow/dbt/my_project",
)

_profile_config = ProfileConfig(
    profile_name="default",
    target_name="dev",
    profile_mapping=SnowflakeUserPasswordProfileMapping(
        conn_id="snowflake_default",
    ),
)

_execution_config = ExecutionConfig()
_render_config = RenderConfig()

my_cosmos_dag = DbtDag(
    dag_id="my_cosmos_dag",
    project_config=_project_config,
    profile_config=_profile_config,
    execution_config=_execution_config,
    render_config=_render_config,
    operator_args={},
    start_date=datetime(2025, 1, 1),
    schedule="@daily",
)

Option B: DbtTaskGroup (Inside Existing DAG)

python

from airflow.sdk import dag, task  # Airflow 3.x
# from airflow.decorators import dag, task  # Airflow 2.x
from airflow.models.baseoperator import chain
from cosmos import DbtTaskGroup, ProjectConfig, ProfileConfig, ExecutionConfig, RenderConfig
from pendulum import datetime

_project_config = ProjectConfig(dbt_project_path="/usr/local/airflow/dbt/my_project")
_profile_config = ProfileConfig(profile_name="default", target_name="dev")
_execution_config = ExecutionConfig()
_render_config = RenderConfig()

@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def my_dag():
    @task
    def pre_dbt():
        return "some_value"

    dbt = DbtTaskGroup(
        group_id="dbt_project",
        project_config=_project_config,
        profile_config=_profile_config,
        execution_config=_execution_config,
        render_config=_render_config,
    )

    @task
    def post_dbt():
        pass

    chain(pre_dbt(), dbt, post_dbt())

my_dag()

Setting Dependencies on Individual Cosmos Tasks

python

from cosmos import DbtDag, DbtResourceType
from airflow.sdk import task, chain

with DbtDag(...) as dag:
    @task
    def upstream_task():
        pass

    _upstream = upstream_task()

    for unique_id, dbt_node in dag.dbt_graph.filtered_nodes.items():
        if dbt_node.resource_type == DbtResourceType.SEED:
            my_dbt_task = dag.tasks_map[unique_id]
            chain(_upstream, my_dbt_task)

8. Safety Checks

Before finalizing, verify:

Execution mode matches constraints (AIRFLOW_ASYNC → BigQuery only; containerized → manifest required)
Warehouse adapter installed for chosen execution mode
Secrets via Airflow connections or env vars, NOT plaintext
Load mode matches execution (containerized → manifest; complex selectors → dbt_ls)
Airflow 3 asset URIs if downstream DAGs scheduled on Cosmos assets (see Appendix A)

Appendix A: Airflow 3 Compatibility

Import Differences

Airflow 3.x	Airflow 2.x
`from airflow.sdk import dag, task`	`from airflow.decorators import dag, task`
`from airflow.sdk import chain`	`from airflow.models.baseoperator import chain`

Asset/Dataset URI Format Change

Cosmos ≤1.9 (Airflow 2 Datasets):

postgres://0.0.0.0:5434/postgres.public.orders

Cosmos ≥1.10 (Airflow 3 Assets):

postgres://0.0.0.0:5434/postgres/public/orders

CRITICAL: Update asset URIs when upgrading to Airflow 3.

Appendix B: Operational Extras

Caching

Cosmos caches artifacts to speed up parsing. Enabled by default.

Reference: https://astronomer.github.io/astronomer-cosmos/configuration/caching.html

Memory-Optimized Imports

bash

AIRFLOW__COSMOS__ENABLE_MEMORY_OPTIMISED_IMPORTS=True

When enabled:

python

from cosmos.airflow.dag import DbtDag  # instead of: from cosmos import DbtDag

Artifact Upload to Object Storage

bash

AIRFLOW__COSMOS__REMOTE_TARGET_PATH=s3://bucket/target_dir/
AIRFLOW__COSMOS__REMOTE_TARGET_PATH_CONN_ID=aws_default

python

from cosmos.io import upload_to_cloud_storage

my_dag = DbtDag(
    # ...
    operator_args={"callback": upload_to_cloud_storage},
)

dbt Docs Hosting (Airflow 3.1+ / Cosmos 1.11+)

bash

AIRFLOW__COSMOS__DBT_DOCS_PROJECTS='{
    "my_project": {
        "dir": "s3://bucket/docs/",
        "index": "index.html",
        "conn_id": "aws_default",
        "name": "My Project"
    }
}'

Reference: https://astronomer.github.io/astronomer-cosmos/configuration/hosting-docs.html

Related Skills

cosmos-dbt-fusion: For dbt Fusion projects (not dbt Core)
authoring-dags: General DAG authoring patterns
testing-dags: Testing DAGs after creation

cosmos-dbt-core

NPX Install

Tags

SKILL.md Content

Cosmos + dbt Core: Implementation Checklist

1. Choose Parsing Strategy (RenderConfig)

2. Choose Execution Mode (ExecutionConfig)

3. Configure Warehouse Connection (ProfileConfig)

Option A: Airflow Connection + ProfileMapping (Recommended)

Option B: Existing profiles.yml

4. Configure Project (ProjectConfig)

5. Configure Testing Behavior (RenderConfig)

6. Configure operator_args

7. Assemble DAG / TaskGroup

Option A: DbtDag (Standalone)

Option B: DbtTaskGroup (Inside Existing DAG)

Setting Dependencies on Individual Cosmos Tasks

8. Safety Checks

Appendix A: Airflow 3 Compatibility

Import Differences

Asset/Dataset URI Format Change

Appendix B: Operational Extras

Caching

Memory-Optimized Imports

Artifact Upload to Object Storage

dbt Docs Hosting (Airflow 3.1+ / Cosmos 1.11+)

Related Skills