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Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured path for creating and deploying Amazon Managed Workflows for Apache Airflow (MWAA). It handles the distinct requirements for provisioned environments (Python DAGs) and MWAA Serverless (YAML workflows). The skill covers the entire lifecycle from operator selection, retry strategies, and scheduling to environment discovery and deployment. It includes built-in safety checks for state-mutating operations and integrates with testing workflows to ensure reliable pipeline delivery.
What it does
- Routes requests between provisioned Python DAGs and Serverless YAML workflows.
- Validates operator selection and configuration against MWAA-specific constraints.
- Automates S3 uploads for DAGs and Create/UpdateWorkflow calls for Serverless.
- Handles inline environment creation and redeployment of fixes.
- Enforces security best practices for IAM, secrets management, and resource defaults.
- Provides a clear hand-off mechanism to testing-mwaa-workflow for verification.
When to use it
- When you need to create a new data pipeline or workflow for Amazon MWAA.
- When you are unsure whether your project requires a provisioned environment or Serverless.
- When you need to deploy a validated DAG or YAML workflow to an AWS environment.
- When you need to update an existing workflow or redeploy a fix after testing.
- When you require guidance on operator compatibility and retry strategies for MWAA.
Advantages
- Reduces configuration errors by enforcing schema compliance and operator allowlists.
- Ensures production safety through explicit confirmation of state-mutating operations.
- Simplifies complex deployment steps into a guided, repeatable process.
- Maintains consistency across different MWAA deployment models.
- Integrates seamlessly with testing and debugging workflows for a complete lifecycle.
Usage examples
- “Create a new Python DAG for my provisioned MWAA environment that runs a daily data sync.”
- The agent will guide you through gathering context, selecting operators, and deploying the DAG to your S3 bucket.
- “I need to deploy a new YAML workflow to MWAA Serverless.”
- The agent will validate your YAML schema, check operator support, and execute the CreateWorkflow command.
- “Deploy my DAG and run a test immediately.”
- The agent will deploy the artifact and automatically invoke the testing-mwaa-workflow skill to verify the run.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/authoring-mwaa-workflow/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip authoring-mwaa-workflow.zip -d .agents/skills
Documentation by AWS
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+sleepuntil 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_skilltool: The skill is not installed on the local filesystem. You MUST fetch each reference viaretrieve_skillwith thefileparameter (e.g.file="references/authoring-provisioned-dag.md") and read the returned content. Do NOTfile_readthese 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:
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 viaaws mwaa get-environment) → go to Path A.A Serverless workflow ARN (
arn:aws:airflow-serverless:<region>:<account>:workflow/<name>) → go to Path B.
Both-path keywords present? If the request contains keywords from both paths and no resolvable target, disambiguate by intent:
PythonOperatoris supported on Serverless, so an operator-levelpythoncue (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.
Exactly one path keyword? Treat only deployment-target terms as Path A signals:
provisioned,Python DAG, or "a.pyfor my environment" → go to Path A.yamlorserverless→ go to Path B. Operator-level Python mentions are not Path A signals.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)?
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
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.
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 pollCREATING->AVAILABLE(~20-40 min). The user may instead supply an existing environment.Serverless:
CreateWorkflow(new) orUpdateWorkflow(redeploy); the YAML is validated synchronously here. If the workflow usesPythonOperator/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 /
UpdateWorkflowpath, reused when testing-mwaa-workflow delegates an ARTIFACT or ENVIRONMENT fix.
If "Deploy and unpause" selected — deploy per step 2, then unpause. Do not trigger a run or invoke testing-mwaa-workflow.
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, andupdate-workflowmutate 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/Resourcepairs the artifact needs — never*FullAccessorservice:*.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".
Files
- ATTRIBUTION.mdAdded by AutoGineer926 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer67 B
- SKILL.md11.0 KiB
- references/authoring-provisioned-dag.md13.9 KiB
- references/authoring-serverless-workflow.md7.4 KiB
- references/dag-patterns.md7.9 KiB
- references/deploying-mwaa.md10.0 KiB
- references/serverless-code-packaging.md3.8 KiB
- references/yaml-schema.md4.7 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server, Needs Python
Security
What our scanner noticed· Low risk
- Instructions that use the network
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- AWS
- Publisher
- AWS (Official)
- Source
- aws/agent-toolkit-for-aws · plugins/aws-data-analytics/skills/authoring-mwaa-workflow · commit 0a218f8
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- NOTICE (added by AutoGineer from NOTICE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- aws/agent-toolkit-for-aws
AutoGineer is not affiliated with or endorsed by AWS or AWS.
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