AWS Serverless Eda
zxkane/aws-skills
AWS serverless and event-driven architecture expert based on Well-Architected Framework.
Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments.
$ npx skills add aws/agent-toolkit-for-aws --skill debugging-mwaa-workflow -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aws/agent-toolkit-for-aws debugging-mwaa-workflow --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/aws-data-analytics/skills/debugging-mwaa-workflow .claude/skills/debugging-mwaa-workflow && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "debugging-mwaa-workflow" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/debugging-mwaa-workflow into .claude/skills/debugging-mwaa-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debugging-mwaa-workflow", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/debugging-mwaa-workflowType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aws/agent-toolkit-for-aws --skill debugging-mwaa-workflow -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aws/agent-toolkit-for-aws debugging-mwaa-workflow --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/aws-data-analytics/skills/debugging-mwaa-workflow .agents/skills/debugging-mwaa-workflow && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "debugging-mwaa-workflow" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/debugging-mwaa-workflow into .agents/skills/debugging-mwaa-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debugging-mwaa-workflow", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill debugging-mwaa-workflow -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aws/agent-toolkit-for-aws debugging-mwaa-workflow --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/aws-data-analytics/skills/debugging-mwaa-workflow .cursor/skills/debugging-mwaa-workflow && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "debugging-mwaa-workflow" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/debugging-mwaa-workflow into .cursor/skills/debugging-mwaa-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debugging-mwaa-workflow", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aws/agent-toolkit-for-aws.git --path plugins/aws-data-analytics/skills/debugging-mwaa-workflow--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aws/agent-toolkit-for-aws --skill debugging-mwaa-workflow -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aws/agent-toolkit-for-aws debugging-mwaa-workflow --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/aws-data-analytics/skills/debugging-mwaa-workflow .gemini/skills/debugging-mwaa-workflow && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "debugging-mwaa-workflow" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/debugging-mwaa-workflow into .gemini/skills/debugging-mwaa-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debugging-mwaa-workflow", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aws/agent-toolkit-for-aws debugging-mwaa-workflowInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aws/agent-toolkit-for-aws --skill debugging-mwaa-workflow -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/aws-data-analytics/skills/debugging-mwaa-workflow .github/skills/debugging-mwaa-workflow && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "debugging-mwaa-workflow" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/debugging-mwaa-workflow into .github/skills/debugging-mwaa-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debugging-mwaa-workflow", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aws/agent-toolkit-for-aws --skill debugging-mwaa-workflow -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aws/agent-toolkit-for-aws debugging-mwaa-workflow --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/aws-data-analytics/skills/debugging-mwaa-workflow .opencode/skills/debugging-mwaa-workflow && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "debugging-mwaa-workflow" agent skill from https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/debugging-mwaa-workflow into .opencode/skills/debugging-mwaa-workflow/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "debugging-mwaa-workflow", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
debugging-mwaa-workflowDiagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments.
Debugging Mwaa Workflow is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments. Provisioned uses aws mwaa invoke-rest-api, CloudWatch log groups, and get-environment; Serverless uses aws mwaa-serverless API (GetWorkflowRun, ListWorkflowRuns, GetTaskInstance) and CloudWatch logs. Covers failed runs and tasks, DAGs not appearing, import errors, worker OOM, IAM denials, and dependency drift. Triggers on: DAG failed, task failed, workflow run failed, MWAA error…
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/failure-catalog.md`, `references/provisioned-diagnostics.md` and `references/serverless-diagnostics.md`).
It sits in Backend & APIs, covering Serverless, Debugging and Root cause analysis. It works with Amazon Web Services, Python and Model Context Protocol. The repository describes itself as: Official, AWS-supported MCP servers, skills, and plugins to help AI agents build on AWS. The licence is Apache-2.0.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit bd49cc8. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
awsFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use aws, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Debugging Mwaa Workflow loads about 2.3k tokens when it runs, and up to ~7.5k if it reads all its reference files. Until then it costs about 213 tokens; SKILL.md has 988 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from aws/agent-toolkit-for-aws at commit bd49cc8, republished under its Apache-2.0 licence (© aws). 988 words, ~2,316 tokens.
.claude/skills/debugging-mwaa-workflow/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.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.
Diagnose and root-cause Amazon MWAA workflow failures, then report root cause, impact, and recommended remediation. Routes by flavor, then runs a shared 4-step diagnostic spine.
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:
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/failure-catalog.md") and read the returned content. Do NOT
file_read these paths locally — they do not exist on disk..kiro/skills/debugging-mwaa-workflow/ or
~/.claude/skills/debugging-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.
aws mwaa get-environment means the
environment is Provisioned.workflow/... ARN or any aws mwaa-serverless context means the
environment is Serverless. A bare run identifier does NOT indicate
flavor — Provisioned DAG runs also have run ids.Provisioned: list failed DAG runs and task instances via
aws mwaa invoke-rest-api (paths /dags/{id}/dagRuns and
/dags/{id}/dagRuns/{run_id}/taskInstances). If invoke-rest-api errors
(RestApiClientException), fall back to the Scheduler and DAGProcessing log
groups. Get version and config from aws mwaa get-environment. See
references/provisioned-diagnostics.md.
Serverless: aws mwaa-serverless list-workflow-runs, then
get-workflow-run. Read RunDetail.ErrorMessage — an empty TaskInstances
with a parser message is a definition error; Workflow execution failed with
populated TaskInstances is a task-execution failure. See
references/serverless-diagnostics.md.
Pull the real exception past boilerplate:
Provisioned: read the Task log group first, then Worker/Scheduler/ DAGProcessing as the symptom directs.
Serverless: list-task-instances then get-task-instance to get each
task's LogStream, then read that stream in CloudWatch.
Then categorize in priority order — infra, then drift, then code-data —
using references/failure-catalog.md. The
category determines the Step 3 checks.
Run the context checks for the matched category from references/failure-catalog.md. Do not stop at the surface exception: a SIGKILL is an OOM story, a fresh import error on unchanged code is a drift story, a sensor timeout is an upstream-health story.
Report in this exact structure:
Root Cause: <one-line diagnosis with the evidence that proves it>
Impact: <what failed, which runs, blast radius>
Immediate Fix: <the smallest change that unblocks>
Prevention: <the change that stops recurrence>
Commands: <exact read-only commands run, plus remediation commands for the user to run>Run only read-only operations. Present state-mutating remediation (clear/rerun/backfill for Provisioned; start-workflow-run or fix-and-redeploy for Serverless) as commands for the user to run, with the impact stated. Never execute them autonomously (production safety).
For the fix-and-redeploy path, use authoring-mwaa-workflow to regenerate a
compliant artifact.
create-web-login-token, invoke-rest-api, or any Airflow REST
path for Serverless.aws mwaa invoke-rest-api (not
create-web-login-token + curl). invoke-rest-api reaches VPC-only web
servers without network access.GetWorkflowRun.RunDetail.ErrorMessage distinguishes a definition error
(empty TaskInstances) from a task-execution failure (Workflow execution failed, populated TaskInstances). Read it before pulling task logs./aws/mwaa-serverless/{workflow-id}/
but can be a custom group; confirm via get-workflow LoggingConfiguration
before assuming the path.scheduler.dag_dir_list_interval, or dag_processor.refresh_interval
on Airflow 3.x) not yet elapsed, a dag_id collision, or S3-sync delay — and
is rarely a broken DAG. Check GET /importErrors and GET /dags/{dag_id} via
invoke-rest-api (and the DAGProcessing logs); see the failure catalog's
"DAG not appearing in the UI" checklist before concluding the code is wrong.PythonOperator/BashOperator tasks run custom code from a
--code package. A run that fails to extract the package or hits
ModuleNotFoundError is a packaging problem (wrong-platform wheel, missing
dep, bad layout), not a YAML definition error. See the failure catalog's
serverless custom-code section.| Error | Cause | Fix |
|---|---|---|
RestApiClientException (Provisioned) | Mis-scoped execution role or service error | Fall back to Scheduler/DAGProcessing log groups |
ResourceNotFoundException on get-workflow-run | Wrong workflow ARN or run id | Re-list with list-workflow-runs |
| Task log stream empty (Serverless) | Wrong log group assumed | Read LoggingConfiguration from get-workflow |
| No task logs but run FAILED | Definition/parse error | Read RunDetail.ErrorMessage; fix the YAML |
AccessDenied is a genuine permission gap,
recommend the minimal Action/Resource from the error — never a wildcard;
distinguish it from a nonexistent-resource typo (do not broaden IAM then).© aws, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files (references) in plugins/aws-data-analytics/skills/debugging-mwaa-workflow of aws/agent-toolkit-for-aws.
Open the folder on GitHubat commit bd49cc8
Debugging Mwaa Workflow next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Debugging Mwaa Workflow this skillaws/agent-toolkit-for-aws | 2.8k | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| AWS Serverless Edazxkane/aws-skills | 367 | 4 repos | ~3.2k | Automated safety check: Pass | MIT | |
| AWS Lambda Durable Functionsawslabs/agent-plugins | 912 | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| AWS Cdk Developmentzxkane/aws-skills | 367 | 2 repos | ~2.5k | Automated safety check: Pass | MIT | |
| QA Find Bugs MCPbex-co/beancount-io | 294 | — | ~3k | Automated safety check: Pass | MIT | |
| LangBot Plugin Developmentlangbot-app/LangBot | 18k | — | ~3.9k | Automated safety check: Pass | Apache-2.0 |
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Categories
Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments. Debugging Mwaa Workflow is an agent skill from aws/agent-toolkit-for-aws, published by the product's own GitHub organization. Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments.
Debugging Mwaa Workflow fits situations like: workflow run failed; why did my workflow fail; DAG not showing up; MWAA import error.
Run `npx skills add aws/agent-toolkit-for-aws --skill debugging-mwaa-workflow -a claude-code`. Or copy the skill folder (plugins/aws-data-analytics/skills/debugging-mwaa-workflow in aws/agent-toolkit-for-aws) into .claude/skills/debugging-mwaa-workflow in your project. Claude Code loads it when a task matches its description.
Run `npx skills add aws/agent-toolkit-for-aws --skill debugging-mwaa-workflow -a codex`. Or copy the skill folder (plugins/aws-data-analytics/skills/debugging-mwaa-workflow in aws/agent-toolkit-for-aws) into .agents/skills/debugging-mwaa-workflow in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aws/agent-toolkit-for-aws --skill debugging-mwaa-workflow -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/debugging-mwaa-workflow, .gemini/skills/debugging-mwaa-workflow, .github/skills/debugging-mwaa-workflow and .opencode/skills/debugging-mwaa-workflow in your project.
Going by SKILL.md and its folder, Debugging Mwaa Workflow needs the command-line tools its instructions call (aws). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Debugging Mwaa Workflow is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.2k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Debugging Mwaa Workflow: AWS Serverless Eda (zxkane/aws-skills, 367 stars), AWS Lambda Durable Functions (awslabs/agent-plugins, 912 stars), AWS Cdk Development (zxkane/aws-skills, 367 stars) and QA Find Bugs MCP (bex-co/beancount-io, 294 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
aws (a GitHub organization, an official publisher) maintains it in aws/agent-toolkit-for-aws, which has 2,816 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on October 7, 2026.
Source: aws/agent-toolkit-for-aws on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.