gcp-pipeline-orchestration

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This skill helps the agent generate or update orchestration pipeline definitions for Google Cloud Composer to initialize orchestration pipeline or update the orchestration definition for orchestration of various data pipelines, like dbt pipelines, notebooks, Spark jobs, Dataform, Python scripts or inline BigQuery SQL queries. This skill also helps deploy and trigger orchestration pipelines.

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Sourcegemini-cli-extensions/data-agent-kit-starter-pack

Added on2026-04-29

NPX Install

npx skill4agent add gemini-cli-extensions/data-agent-kit-starter-pack gcp-pipeline-orchestration

SKILL.md Content

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Mandatory Reference Routing

If relevant, call the associated reference file(s) before you take actions. Refer to the table below to determine which reference file to retrieve in different scenarios involving specific functions. [!IMPORTANT]: DO NOT GUESS filenames. You MUST only use the exact paths provided below.

Function/Use Case	Required Reference File	Capabilities & Intent Keywords
orchestration-pipelines schema	`references/orchestration-pipelines-schema.md`	orchestrate, generate, create, update

How to use this skill

Orchestration pipelines require creating two files to ensure a complete and deployable pipeline:

1.  `Orchestration File` (e.g., `orchestration-pipeline.yaml`,
    `test-pipeline.yaml`): Defines the pipeline's logic, tasks, and
    schedule. **IMPORTANT:** Check if a `deployment.yaml` file exists and
    references an existing orchestration file. If it does, you **must update
    the existing orchestration file** (e.g.,`test_pipeline.yaml`) instead of
    creating a new one. The filename can be customized but must be
    referenced in the `deployment.yaml` file.
2.  `deployment.yaml`: Defines the environment-specific
    configurations.(e.g., `dev`, `prod`). `deployment.yaml`should only
    exists in the repository root and must be named `deployment.yaml`

All files should always be maintained together. And all files should be placed on the root of the workspace folder.
This skill is helpful to create or update configuration files to orchestrate data pipelines.

How to use this skill

Step 1: Assess Orchestration Pipeline Status and Initialize if Necessary

Examine the repository's root directory for a

deployment.yaml

file.

Check for existing setup: The absence of
```
deployment.yaml
```
indicates that orchestration has not been set up.
Determine if initialization is required: Initialization is required if
```
deployment.yaml
```
is missing. you MUST run the
```
init
```
command in Step 3 to scaffold the project if
```
deployment.yaml
```
is missing. Do NOT create the files manually.
Pipeline Name: If initialization is needed, ask the user for the pipeline name. If user hasn't provided the orchestration pipeline name, name should be "orchestration_pipeline"
Environment Name: If initialization is needed, you MUST ask the user for the environment name. If the user does not provide it, use dev as the default.
Execute Initialization: Once you have the pipeline name, run the following command:

# Replace <ORCHESTRATION_PIPELINE_NAME> with the actual name
# Replace <ENV_NAME> with the actual environment name
gcloud beta orchestration-pipelines init <ORCHESTRATION_PIPELINE_NAME> --environment=<ENV_NAME>

Step 2: Review the orchestration pipeline code structure and syntax instruction

*** Pipeline Models (mapping to YAML)

[!IMPORTANT] While the internal pipeline models are defined using protobuf (which typically uses
snake_case
), the YAML configuration expects
camelCase
for almost all field names.
Mapping Rule: Always convert
snake_case
proto fields (e.g.,
pipeline_id
) to
camelCase
in YAML (e.g.,
pipelineId
).

Orchestration-Pipelines yaml structure and syntax instruction

Reference to file

references/orchestration-pipelines-schema.md

Required Tags (Top-Level): You MUST add a

tags

field to the top-level of the orchestration pipeline YAML definition. The value of this field depends on the IDE environment:

For Antigravity, use
```
["job:datacloud:antigravity"]
```
.
For VS Code, use
```
["job:datacloud:vscode"]
```
.
For any other environment, use
```
["job:datacloud:other"]
```
.

Deployment yaml structure and syntax instruction.

Top-Level Structure: The root of the YAML should be an object with the following fields:

```
environments
```
(dictionary): A map where keys are environment names (e.g., 'dev', 'prod', etc) and values are Environment objects.

Environment: Each environment object contains the following fields:

```
project
```
(string): The Google Cloud Project ID.
```
region
```
(string): The Google Cloud region (e.g., 'us-central1').
```
composer_environment
```
(string): The Cloud Composer environment name.
```
artifact_storage
```
- ```
bucket
```
  (string): GCS bucket
- ```
path_prefix
```
  (string): prefix of path that we want to put in bucket
```
pipelines
```
- ```
- source
```
  (string): orchestration pipeline yaml file names. It can be multiple
```
variables
```
(dictionary, optional): Key-value pairs representing environment variables. Values can be strings, numbers, or booleans.

[!TIP] If the user doesn't provide specific paths for scripts, dbt projects, or GCP details (Project ID, Region), use tools like
find_by_name
to search the repository and
gcloud
commands (e.g.,
gcloud config get-value project
) to retrieve the necessary information.

Step 3: Generate the pipeline files

Before generating, check if an orchestration pipeline definition file and
```
deployment.yaml
```
already exist in the current directory. If they do, inform the user and ask if they want to update the existing files or create new ones with different names. Do not overwrite without confirmation.
First, before creating the orchestration pipeline definition file, you must first run the following command to get the list of available dataproc environments for the user's project. This avoids using placeholder values to run the jobs.
```
# Replace <PROJECT_ID> with the actual project_id
# Replace <REGION> with the actual region
gcloud dataproc clusters list \
--project <PROJECT_ID> \
--region <REGION> \
```
[!TIP] Running the command without
--format=yaml
provides a clear, tabular output that is easier to read.
Then use the returned dataproc list with details to create the orchestration pipeline definition file based on the user's requirements for the pipeline's logic and schedule. IMPORTANT: Every schedule must include an
```
endTime
```
. Every schedule must use the current date as
```
startTime
```
if the user hasn't specified.
[!IMPORTANT] A Composer environment is not a Dataproc cluster. If no Dataproc clusters are available, do not use a Composer environment for the
sparkHistoryServerConfig
. It is better to omit this configuration if a dedicated Spark History Server is not available.
If you want to schedule the python job, check the content of Python content to determine if it's a spark job. If it is, use
```
pyspark
```
as type instead of script as type.
Before creating or updating the
```
deployment.yaml
```
file, you must first run the following command to get the list of available Composer environments for the user's project.
```
# Replace <PROJECT_ID> with the actual project_id
# Replace <REGION> with the actual region
gcloud composer environments list \
--project <PROJECT_ID> \
--locations <REGION> \
```
After listing available Composer environments, you must check each environment to ensure the composer is using the right image version or has installed right PyPI packages. Run the following command for each environment:
```
# Replace <ENVIRONMENT_NAME> with the Composer environment name
# Replace <REGION> with the region
gcloud composer environments describe <ENVIRONMENT_NAME> \
--location <REGION> \
--format="json(config.softwareConfig.imageVersion, config.softwareConfig.pypiPackages)"
```
From the output, select an environment where the imageVersion value is one of is "composer-3-airflow-3.1.7-build.x, composer-3-airflow-2.11.1-build.x, composer-3-airflow-2.10.5-build.x, composer-3-airflow-2.9.3-build.x, composer-2.16.11-airflow-2.11.1, composer-2.16.11-airflow-2.10.5, composer-2.16.11-airflow-2.9.3" or select an environment where
```
orchestration-pipelines
```
field is presented listed in the PyPI packages. This ensures the selected environment is compatible with orchestration pipelines.
Third, before generating the
```
deployment.yaml
```
file, you must ask the user to provide the
```
artifact_storage
```
bucket name. Note that the
```
artifact_storage
```
bucket is typically initialized as a placeholder (e.g.,
```
YOUR_BUCKET
```
) by the
```
init
```
command in Step 1. You must identify any such placeholders, ask the user for the actual bucket name, and then update the
```
deployment.yaml
```
file with the provided value.
Use the returned composer list with details, along with the project ID, region, and the bucket name provided by the user, to generate or update the
```
deployment.yaml
```
file. When generating or updating the
```
deployment.yaml
```
file, you must replace placeholders (e.g., "<YOUR_PROJECT_ID>", "<YOUR_REGION>", "<YOUR_COMPOSER>", "<YOUR_BUCKET>") with the actual retrieved and provided values. Additionally, you must remove any associated
```
# TODO:
```
comments once the placeholders are replaced.
Ensure both files adhere to the code structures and syntax specified in this document.
Renaming Pipelines: If requested to change the orchestration pipeline name, you must rename the orchestration YAML file accordingly (e.g., from
```
dbt_clean_pipeline.yaml
```
to
```
new_name.yaml
```
) and update the
```
source
```
field within the
```
pipelines
```
list in
```
deployment.yaml
```
to match the new filename.

[!IMPORTANT]
Time Format: Do NOT include the
Z
suffix in
startTime
and
endTime
. Use the format
"YYYY-MM-DDTHH:MM:SS"
(e.g.,
"2025-10-01T00:00:00"
).

Step 4: Validate the content (REQUIRED)

After creating or editing pipeline files, you MUST validate them using the

gcloud beta orchestration-pipelines validate

command. you must: a. Read the

deployment.yaml

file to identify all defined environments. b. Run the

validate

command below for each environment found in

deployment.yaml

# Replace <ENV_NAME> with the identified environment name
gcloud beta orchestration-pipelines validate --environment=<ENV_NAME>

Step 5: Handle Validation Errors

Check the output of the validation command.
If the command returns an error or failure message:
- Read the error message carefully.
- Edit the orchestration and deployment files to fix the specific issue mentioned.
Re-run the validation command to confirm the fix. Do not mark the task as complete until the validation passes (exit code 0), and do not fall back to create airflow dag in python if validation fails.

Declarative Pipeline Templates

When asked to generate or verify declarative pipeline files, ensure they follow these compliant structures. Do not use the exact values below; adapt them to the user's specific project, region, and environment details.

deployment.yaml

Template - IMPORTANT FORMAT MUST MATCH-

yaml

environments:
  <environment_name>: # e.g., dev, prod
    project: <PROJECT_ID>
    region: <REGION>
    composer_environment: <COMPOSER_ENVIRONMENT_NAME>
    gcs_bucket: "" # Optional
    artifact_storage:
      bucket: <ARTIFACT_BUCKET_NAME>
      path_prefix: "<prefix>-" # e.g., namespace or username prefix
    pipelines:
      - source: '<orchestration-pipeline.yaml>' # e.g., list of pipeline yaml names

Step 6: Deploy the Orchestration Pipeline (Optional)

If requested to deploy the orchestration pipeline:

You MUST ask the user which environment to deploy to. If no environment name is provided, list the available environments from
```
deployment.yaml
```
and ask the user to choose one, defaulting to
```
dev
```
if it exists.
Read the orchestration YAML to extract the
```
pipelineId
```
.

Deploy with

--local

. This uploads the DAG without running it:

# Replace <ENV_NAME> with the target environment
# Replace <PIPELINE_SOURCE> with the orchestration YAML filename
gcloud beta orchestration-pipelines deploy \
  --environment=<ENV_NAME> --local

Parse the deploy output to extract the bundle ID (version). The output includes a line like:
```
Pipeline deployment successful for version local-b32d15e307b5
```
The version string (e.g.,
```
local-b32d15e307b5
```
) is the bundle ID.

[!IMPORTANT]
--local
deployments now default to
--paused=true
. The deployed DAG will be visible in Airflow as a paused DAG without a schedule. It will not auto-run. Use Step 7 to trigger it.

Step 7: Trigger the Orchestration Pipeline Run (Optional)

If requested to trigger/run the orchestration pipeline, you MUST follow the Deploy → Poll → Trigger flow.

Ask for environment: You MUST ask the user which environment to use. Default to
```
dev
```
if it exists in
```
deployment.yaml
```
.
Deploy first (Step 6): Always deploy before triggering to ensure the run uses the latest code. Extract the
```
bundle ID
```
from deploy output and the
```
pipelineId
```
from the orchestration YAML.

Poll for DAG readiness: Wait for the DAG to be registered in Composer.


# Initial delay: wait 30 seconds after deploy

sleep 30

# Poll every 15 seconds, up to 2 minutes total

# Replace <ENV_NAME>, <BUNDLE_ID> with actual values

gcloud beta orchestration-pipelines list \
--environment=<ENV_NAME> \
--bundle=<BUNDLE_ID>

The pipeline is ready when it appears in the list output. If it does not appear after 2 minutes, report failure and advise the user to check YAML validity.

Trigger the pipeline:

    # Replace <ENV_NAME>, <BUNDLE_ID>, <PIPELINE_ID> with actual values

    gcloud beta orchestration-pipelines trigger \
    --environment=<ENV_NAME> \
    --bundle=<BUNDLE_ID> \
    --pipeline=<PIPELINE_ID>

Verify the run started:

gcloud beta orchestration-pipelines runs
    list \ --environment=<ENV_NAME> \ --bundle=<BUNDLE_ID> \
    --pipeline=<PIPELINE_ID>`

[!TIP] Trigger-only (no deploy): If the user wants to trigger an already-deployed pipeline, skip Step 6. Use
gcloud beta orchestration-pipelines list --environment=<ENV_NAME>
to find the bundle ID, then trigger directly with Step 7.4.

[!IMPORTANT] Fallback: If
gcloud trigger
fails, use the bundled script: Run script with -- help to discover and learn the interface.

python scripts/trigger/airflow_trigger.py \ --project <PROJECT_ID>
--location <REGION> \ --environment <COMPOSER_ENV> --dag_id <PIPELINE_ID>

Get

project

region

, and

composer_environment

from

deployment.yaml

Definition of done

```
deployment.yaml
```
file is created successfully.
The orchestration pipeline file (e.g.,
```
orchestration_pipeline.yaml
```
) is created successfully, includes a mandatory
```
endTime
```
for every schedule, and passes the validation command:
```
gcloud beta orchestration-pipelines validate --environment=<ENV_NAME>
```
If user requested to deploy the orchestration pipeline, the
```
gcloud beta orchestration-pipelines deploy --environment=<ENV_NAME> --local
```
command should return a success message with a version/bundle ID.
If user requested to trigger/run the orchestration pipeline:
1. Deploy succeeded (bundle ID extracted from output)
2. DAG appeared in
```
gcloud beta orchestration-pipelines list
```
  within 2 min
3. ```
gcloud beta orchestration-pipelines trigger
```
  returned success
4. Run is visible in
```
gcloud beta orchestration-pipelines runs list
```

Other actions

If requested to pause/stop the orchestration pipeline, use

bash

   # Replace <ENV_NAME>, <BUNDLE_ID>, <PIPELINE_ID> with actual values
   gcloud beta orchestration-pipelines pause \
   --environment=<ENV_NAME> \
   --bundle=<BUNDLE_ID> \
   --pipeline=<PIPELINE_ID>

If requested to unpause/resume the orchestration pipeline, use

bash

   # Replace <ENV_NAME>, <BUNDLE_ID>, <PIPELINE_ID> with actual values
   gcloud beta orchestration-pipelines unpause \
   --environment=<ENV_NAME> \
   --bundle=<BUNDLE_ID> \
   --pipeline=<PIPELINE_ID>