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testing-mwaa-workflow

aws/agent-toolk…-for-aws

Tests Amazon MWAA workflow execution end-to-end: trigger a run and monitor it to completion for Provisioned (Python DAG, via Airflow REST API) and Serverless (YAML workflow, via StartWorkflowRun). Verifies the artifact is deployed and parse-ready, triggers with confirmation, polls to terminal state, and on failure delegates diagnosis to debugging-mwaa-workflow and artifact/redeploy fixes to authoring-mwaa-workflow, then retests up to a capped number of attempts. Triggers on: test my DAG, test my workflow, run my DAG, trigger a test run, does my DAG work, smoke-test the pipeline, verify my workflow runs, execute my DAG to check it. Not applicable to writing or deploying a new workflow (handled by authoring-mwaa-workflow), or for diagnosing why a run failed or root-causing an error (handled by debugging-mwaa-workflow).

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安装命令

npx skills add https://github.com/aws/agent-toolkit-for-aws --skill testing-mwaa-workflow

SKILL.md

Testing 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.

Test Amazon MWAA workflow execution end-to-end: trigger a run, poll it to a terminal state, and on failure drive a capped debug -> fix -> retest loop. This skill triggers and reads state only; it delegates every diagnosis to debugging-mwaa-workflow and every mutation (artifact edit, redeploy, environment create/update) to authoring-mwaa-workflow. Routes by flavor, then runs one shared spine.

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.

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