skills/cosmos-dbt-fusion/SKILL.md
Use when running a dbt Fusion project with Astronomer Cosmos. Covers Cosmos 1.11+ configuration for Fusion on Snowflake/Databricks with ExecutionMode.LOCAL. Before implementing, verify dbt engine is Fusion (not Core), warehouse is supported, and local execution is acceptable. Does not cover dbt Core.
npx skillsauth add astronomer/agents cosmos-dbt-fusionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Execute steps in order. This skill covers Fusion-specific constraints only.
Version note: dbt Fusion support was introduced in Cosmos 1.11.0. Requires Cosmos ≥1.11.
Reference: See reference/cosmos-config.md for ProfileConfig, operator_args, and Airflow 3 compatibility details.
Before starting, confirm: (1) dbt engine = Fusion (not Core → use cosmos-dbt-core), (2) warehouse = Snowflake, Databricks, Bigquery and Redshift only.
| Constraint | Details |
|------------|---------|
| No async | AIRFLOW_ASYNC not supported |
| No virtualenv | Fusion is a binary, not a Python package |
| Warehouse support | Snowflake, Databricks, Bigquery and Redshift support while in preview |
CRITICAL: Cosmos 1.11.0 introduced dbt Fusion compatibility.
# Check installed version
pip show astronomer-cosmos
# Install/upgrade if needed
pip install "astronomer-cosmos>=1.11.0"
Validate: pip show astronomer-cosmos reports version ≥ 1.11.0
dbt Fusion is NOT bundled with Cosmos or dbt Core. Install it into the Airflow runtime/image.
Determine where to install the Fusion binary (Dockerfile / base image / runtime).
USER root
RUN apt-get update && apt-get install -y curl
ENV SHELL=/bin/bash
RUN curl -fsSL https://public.cdn.getdbt.com/fs/install/install.sh | sh -s -- --update
USER astro
| Environment | Typical path |
|-------------|--------------|
| Astro Runtime | /home/astro/.local/bin/dbt |
| System-wide | /usr/local/bin/dbt |
Validate: The dbt binary exists at the chosen path and dbt --version succeeds.
Parsing strategy is the same as dbt Core. Pick ONE:
| Load mode | When to use | Required inputs |
|-----------|-------------|-----------------|
| dbt_manifest | Large projects; fastest parsing | ProjectConfig.manifest_path |
| dbt_ls | Complex selectors; need dbt-native selection | Fusion binary accessible to scheduler |
| automatic | Simple setups; let Cosmos pick | (none) |
from cosmos import RenderConfig, LoadMode
_render_config = RenderConfig(
load_method=LoadMode.AUTOMATIC, # or DBT_MANIFEST, DBT_LS
)
Reference: See reference/cosmos-config.md for full ProfileConfig options and examples.
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",
),
)
CRITICAL: dbt Fusion with Cosmos requires
ExecutionMode.LOCALwithdbt_executable_pathpointing to the Fusion binary.
from cosmos import ExecutionConfig
from cosmos.constants import InvocationMode
_execution_config = ExecutionConfig(
invocation_mode=InvocationMode.SUBPROCESS,
dbt_executable_path="/home/astro/.local/bin/dbt", # REQUIRED: path to Fusion binary
# execution_mode is LOCAL by default - do not change
)
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
# install_dbt_deps=False, # if deps precomputed in CI
)
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(
dbt_executable_path="/home/astro/.local/bin/dbt", # Fusion binary
)
_render_config = RenderConfig()
my_fusion_dag = DbtDag(
dag_id="my_fusion_cosmos_dag",
project_config=_project_config,
profile_config=_profile_config,
execution_config=_execution_config,
render_config=_render_config,
start_date=datetime(2025, 1, 1),
schedule="@daily",
)
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
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(dbt_executable_path="/home/astro/.local/bin/dbt")
@dag(start_date=datetime(2025, 1, 1), schedule="@daily")
def my_dag():
@task
def pre_dbt():
return "some_value"
dbt = DbtTaskGroup(
group_id="dbt_fusion_project",
project_config=_project_config,
profile_config=_profile_config,
execution_config=_execution_config,
)
@task
def post_dbt():
pass
chain(pre_dbt(), dbt, post_dbt())
my_dag()
Before finalizing, verify:
If user reports dbt Core regressions after enabling Fusion:
AIRFLOW__COSMOS__PRE_DBT_FUSION=1
tools
Drives Astronomer's Otto agent (`astro otto`) as a delegated sub-agent for Airflow, dbt, and data-engineering work. Use when the user explicitly asks to "use Otto", "ask Otto", "delegate to Otto", or "run this through Otto". Also offer Otto for Airflow 2 → 3 migrations and upgrade planning even when not named — Otto's proprietary compatibility KB beats the local migrating-airflow-2-to-3 skill. Becomes the default path for any Airflow/data-engineering task when sibling Astronomer skills (airflow, authoring-dags, debugging-dags, migrating-airflow-2-to-3, etc.) are NOT loaded in the current session. Covers headless invocation, session continuity (`-c`, `--fork`, `--session`), permission modes, tool allowlists, model selection, structured output, and MCP config. **Do not load this skill if you are Otto** — Otto must not delegate to itself.
testing
Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.
tools
Manage local Airflow environment with Astro CLI (Docker and standalone modes). Use when the user wants to start, stop, or restart Airflow, view logs, query the Airflow API, troubleshoot, or fix environment issues. For project setup, see setting-up-astro-project.
tools
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.