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name: authoring-mwaa-workflow description: > Authors and deploys MWAA workflow artifacts: Python Airflow DAGs for provisioned environments or YAML workflow files for Serverless. Covers operator selection, timeout design, retry strategy, scheduling, failure notifications, idempotency, and MWAA Serverless schema compliance. Deploys the artifact (S3 DAG upload or Serverless CreateWorkflow/UpdateWorkflow), creates an environment inline when approved, and redeploys fixes, then optionally hands off to testing-mwaa-workflow. Triggers on: create a DAG, write a pipeline, build a workflow, orchestrate tasks, Airflow DAG, data pipeline, schedule a job, deploy a DAG, deploy a workflow, YAML workflow. Not applicable to converting or migrating existing DAGs between provisioned and serverless (conversion is out of scope), running or smoke-testing a deployed workflow (handled by testing-mwaa-workflow) or diagnosing a failed run (handled by debugging-mwaa-workflow). metadata: version: "1"

Authoring MWAA Workflows

AWS MCP server (optional but recommended): running the AWS CLI commands in this skill through the AWS MCP server gives sandboxed execution and audit logging. Every command here also works with the plain AWS CLI, so the skill does not require the MCP server or any MCP-only tools.

Author production-grade workflow artifacts for Amazon MWAA. Routes to one of two paths: Python DAG (provisioned) or YAML workflow (Serverless).

Execution note — poll in discrete steps: whenever you wait for an AWS operation to reach a terminal or ready state, issue one status check per call and decide in your own loop whether to check again. Never block a single command or script on the wait (no while+sleep until done), regardless of the operation or how long it takes.

Guardrail — where this skill's own files live (MCP vs local install)

This skill can be loaded two ways, and they resolve the skill's own bundled files from different places. Determine how the skill was loaded before reading a reference:

• Loaded through the AWS MCP retrieve_skill tool: The skill is not installed on the local filesystem. You MUST fetch each reference via retrieve_skill with the file parameter (e.g. file="references/authoring-provisioned-dag.md") and read the returned content. Do NOT file_read these paths locally — they do not exist on disk.
• Installed locally (e.g. .kiro/skills/authoring-mwaa-workflow/ or ~/.claude/skills/authoring-mwaa-workflow/): Read the files from the local skill directory using relative paths.

This distinction applies only to the skill's own packaged files. User data and session artifacts are always read from and written to the user's working directory. Never fetch or write customer data through retrieve_skill.

Step 0: Route to Path

Evaluate in this order:

1. Resolvable target provided? A target reference is a definitive routing signal regardless of other keywords:
   • A provisioned environment (ARN like arn:aws:airflow:<region>:<account>:environment/<name>, or a name resolvable via aws mwaa get-environment) → go to Path A.
   • A Serverless workflow ARN (arn:aws:airflow-serverless:<region>:<account>:workflow/<name>) → go to Path B.
2. Both-path keywords present? If the request contains keywords from both paths and no resolvable target, disambiguate by intent:
   • PythonOperator is supported on Serverless, so an operator-level python cue (PythonOperator, python_callable, "Python function/task") alongside a Path B signal (yaml, serverless, workflow ARN) is NOT ambiguous → go to Path B.
   • Conversion context ("convert my Python DAG to serverless") → this skill does not apply; conversion is out of scope.
   • A genuine provisioned cue (Python DAG, provisioned) alongside a Serverless cue with no target → ask the clarifying question.
3. Exactly one path keyword? Treat only deployment-target terms as Path A signals: provisioned, Python DAG, or "a .py for my environment" → go to Path A. yaml or serverless → go to Path B. Operator-level Python mentions are not Path A signals.
4. No routing signal? "DAG" or "Workflow" alone is ambiguous — it does NOT indicate a path. Ask: is the target MWAA provisioned (Python DAG) or MWAA Serverless (YAML)?

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Paths

Follow the reference for the path you routed to (you do not need the other path's reference):

• Path A — Python DAG (MWAA Provisioned): references/authoring-provisioned-dag.md
• Path B — YAML Workflow (MWAA Serverless): references/authoring-serverless-workflow.md

After the routed path's Write step, continue with Deploy & Test below.

Deploy & Test (optional, after Write)

Authoring owns all deployment and redeployment. Detail in references/deploying-mwaa.md.

Steps

1. Ask — present options based on whether the artifact has a schedule. Frame the question using path-appropriate language:

   • Provisioned: "deploy this DAG to an environment" (DAGs are uploaded to an environment's S3 bucket).
   • Serverless: "deploy this workflow" (workflows are standalone resources — never say "deploy to an environment").

   If the DAG/workflow has a schedule:

   • Deploy and test — deploy, unpause, trigger a run now
   • Deploy and unpause — deploy, unpause, let it run on schedule (no immediate trigger)
   • Deploy only — upload to S3, leave paused

   If the DAG/workflow has no schedule (manual-trigger only):

   • Deploy and test — deploy, trigger a run now
   • Deploy only — upload to S3, leave paused (no "unpause" option — nothing to schedule)

   The user may also decline all options.

2. Deploy:

   • Provisioned: upload the DAG to the environment's SourceBucketArn/ DagS3Path. Run post-deploy verification (see deploying-mwaa.md) to confirm the scheduler parsed the new file without import errors or dag_id conflicts. If no environment exists and the user approves, create one inline (plan-validate-execute + explicit confirmation), then poll CREATING -> AVAILABLE (~20-40 min). The user may instead supply an existing environment.
   • Serverless: CreateWorkflow (new) or UpdateWorkflow (redeploy); the YAML is validated synchronously here. If the workflow uses PythonOperator/BashOperator, first build and upload the code package to S3 and pass it via --code (see references/serverless-code-packaging.md and references/deploying-mwaa.md). The user may instead supply an existing ARN.
   • Redeploy (fix loop): the same upload / UpdateWorkflow path, reused when testing-mwaa-workflow delegates an ARTIFACT or ENVIRONMENT fix.

3. If "Deploy and unpause" selected — deploy per step 2, then unpause. Do not trigger a run or invoke testing-mwaa-workflow.

4. If "Deploy and test" selected — deploy per step 2, unpause if applicable, then invoke testing-mwaa-workflow with the resolved target (env name + dag_id, or workflow ARN). That hand-off is testing's delegated invocation mode.

HARD GATE: If testing is requested — whether upfront ("deploy and test") or later in the conversation ("test it", "run it", "try it") — you MUST invoke testing-mwaa-workflow. Do NOT trigger, monitor, or verify DAG runs manually. "Deploy and unpause" is NOT a test request — it is a deploy-only action.

• Production safety: create-environment, update-environment, create-workflow, and update-workflow mutate state — confirm each with its impact stated. Warn on prod-named targets.

Troubleshooting

Error	Cause	Fix
dagrun_timeout kills DAG early	< timeout set in service called	Raise dagrun_timeout or lower service timeout
YAML validation rejects workflow	Wrong type or param	Use timedelta format; check allowlist
Operator not found in Serverless	Not allowlisted	Use a supported operator, PythonOperator/BashOperator, or Lambda
Serverless run: cannot extract code / corrupt env	Bad code package	Files at zip root, no __pycache__, ≤250 MB; repackage
Serverless Python task ImportError	Missing dep or wrong-platform wheel	Bundle as manylinux2014_x86_64 / Py3.12 wheel; don't bundle pre-installed packages
Template variable undefined	Version mismatch	Check vars for exact Airflow version

References

• references/dag-patterns.md — Python DAG templates
• references/yaml-schema.md — MWAA Serverless format
• references/deploying-mwaa.md — deploy, inline env creation, and redeploy
• references/serverless-code-packaging.md — Python/Bash code packaging, pre-installed packages, limits
• references/authoring-provisioned-dag.md — Path A: provisioned Python-DAG authoring steps (A1–A7)
• references/authoring-serverless-workflow.md — Path B: Serverless YAML authoring steps (B1–B5)

Security Considerations

• State-mutating operations (create/update-environment, create/update-workflow, S3 DAG upload) require explicit confirmation with impact stated; warn on prod-named targets (see the Deploy HARD-GATE).
• Least-privilege IAM: the A5 check adds only the exact Action/Resource pairs the artifact needs — never *FullAccess or service:*.
• No hardcoded secrets/endpoints: use Airflow Variables/Connections backed by Secrets Manager or SSM Parameter Store; never emit credentials in DAG code or CLI examples.
• Serverless code packages ship only the user's own modules plus pinned, platform-matched wheels — no unreviewed third-party binaries.
• Data protection: keep sensitive data out of SNS/CloudWatch notification payloads and logs; rely on their encryption.
• Secure defaults for inline-created resources: encrypt and lock down any S3/MWAA/SNS/CloudWatch resource this skill creates — see deploying-mwaa.md "Secure defaults".
• AWS security best practices: verify the security posture against the MWAA User Guide's Security best practices page (and the MWAA Serverless equivalent) at runtime — AWS updates them over time; see deploying-mwaa.md "Secure defaults".