Agent Toolkit for AWS is a collection of AWS-supported MCP servers, skills, plugins, commands, and hooks that help AI coding agents build, deploy, and manage applications on AWS. It is used by developers working with AWS services through agents such as Claude Code, Codex, Cursor, and Kiro. The catalogue entries are the toolkit's own agent extensions for AWS development and operations.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aws/agent-toolkit-for-aws --skill authoring-mwaa-workflowgit clone --depth 1 https://github.com/aws/agent-toolkit-for-awsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aws/agent-toolkit-for-aws/authoring-mwaa-workflow)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/authoring-mwaa-workflow"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/authoring-mwaa-workflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/authoring-mwaa-workflow"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/authoring-mwaa-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00196 | $0.02690 |
| Opus 5.5 | $0.00078 | $0.01076 |
| Sonnet 5.5 | $0.00039 | $0.00538 |
| Haiku 4.5 | $0.00020 | $0.00269 |
Grade A, and why
authoring-mwaa-workflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 22d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
- A provisioned environment (ARN like
- 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)?
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 22d ago First seen · 198 lines · 196 tokens per session scan A 4cfbda2407de
authoring-mwaa-workflow is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,927 stars, last pushed today), licensed Apache-2.0. It adds 196 tokens to every session and 2,690 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-30.
Other skills, from other repositories
gke-compute-classes
Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto…
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running security audits (auditcluster.sh), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (gVisor), enforcing Pod…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for generating K8s YAML manifests (use gke-manifest-generation) or disaster recovery…
cloud-architect
Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP. Use when designing cloud architectures, planning migrations, or optimizing multi-cloud deployments. Invoke for Well-Architected Framework, cost…
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
azure-mgmt-botservice-dotnet
Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: "Bot Service", "BotResource", "Azure Bot", "DirectLine channel", "Teams channel", "bot management .NET", "create bot".