Aws Serverless logo

Aws Serverless

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aws
aws-serverless

Builds, deploys, manages, debugs, configures, and optimizes serverless applications on AWS using Lambda, API Gateway, Step Functions, EventBridge, and SAM/CDK. Covers cold starts, CORS debugging, event source mappings, troubleshooting, concurrency, SnapStart, Powertools, function URLs, EventBridge Scheduler, Lambda layers, and production readiness. Triggers on mentions of Lambda, API Gateway, Step Functions, SAM templates, CDK serverless stacks, DynamoDB stream triggers, SQS event sources, cold starts, timeouts, 502/504 errors, throttling, concurrency, CORS, Powertools, or any event-driven architecture on AWS, even without the word "serverless." Does not apply to EC2, ECS/Fargate containers, or Amplify hosting.

Overview

Publisheraws
Repositoryagent-toolkit-for-aws
Skill nameaws-serverless
Stars
2.7K
Forks
311
Bundled files
10
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aws on GitHub. Read the source before you install it.

Installation

Install the Aws Serverless AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git /tmp/agent-toolkit-for-aws
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit-for-aws/plugins/aws-core/skills/aws-serverless .claude/skills/aws-serverless
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aws Serverless in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aws Serverless on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aws Serverless is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AWS Serverless

Domain expertise for building serverless applications on AWS: Lambda, API Gateway, Step Functions, EventBridge, event source mappings, concurrency, cold starts, deployment, and troubleshooting.

Works best with the AWS MCP server — run CLI commands, query CloudWatch, validate configs directly. All guidance also works with standard AWS CLI access.

Specialized skills — check these first

These cover capabilities and procedures the general references below do not. Several are specialized features or step-by-step tested procedures you would otherwise miss. Route to the matching skill before falling back to the references.

Advanced Lambda compute (easy to overlook)

Use this skillWhen the workload involves
aws-lambda-microvmsStrong tenant isolation, sandboxed/untrusted code execution (AI agent code sandboxes, REPLs, notebooks, CI runners), long-lived sessions, suspend/resume with preserved state, port-listening servers (gRPC, WebSocket, custom TCP), Firecracker microVMs, snapshot-resumable compute, up to 8-hour lifetimes
aws-lambda-durable-functionsDurable execution, checkpoint-and-replay, long-running multi-step workflows written as plain code (TS/Python/Java), automatic state persistence, saga pattern in code, human-in-the-loop callbacks, executions up to 1 year, context.step/context.wait/context.invoke, withDurableExecution, durable-execution-sdk
aws-lambda-managed-instancesLambda Managed Instances (LMI), capacity providers, EC2-backed Lambda, steady high-volume traffic (50M+ req/mo) wanting Savings Plans / Reserved Instance pricing, PerExecutionEnvironmentMaxConcurrency, CapacityProviderConfig, multi-concurrent execution environments

Workflow orchestration

Route here when the user wants to coordinate multiple steps, services, or functions. Triggers include "orchestration", "workflow", "state machine", "multi-step coordination", "coordinate Lambda functions", "durable execution", "pipeline with retries", or intent to build saga/compensation, human-in-the-loop approval, fan-out, or long-running async coordination.

When starting a new orchestration or multi-step workflow, you MUST surface the choice between AWS Step Functions and AWS Lambda Durable Functions before implementing — do not silently pick one. Route on the signals below. When the request names only a generic pattern (saga/compensation, human-in-the-loop, fan-out, or "workflow orchestration") with no technology, present both options and the one-line tradeoff, then let the user decide. Do not lead with the tradeoff caveats when the signals already point to one service.

Use this skillWhen the workload involves
aws-step-functionsOrchestration whose primary work is calling AWS services directly; coordinating non-Lambda compute (ECS/Fargate, Glue, SageMaker, Batch) through native managed integrations; a visual, auditable workflow definition required for compliance, cross-team operational observability, or as a shared contract between teams that do not share a codebase (ASL is the specification, not application code); authoring or editing state machines and Amazon States Language (ASL) — state types, JSONata data transformation, Retry/Catch error handling, .sync/waitForTaskToken service integrations, Distributed Map, TestState unit testing, JSONPath-to-JSONata migration
aws-lambda-durable-functionsCode-first orchestration in-process when already building on Lambda (context.step/context.wait/context.invoke, withDurableExecution); many fine-grained steps per execution where cumulative Step Functions Standard state-transition cost may be significant — compare Step Functions pricing (Standard vs Express) against Lambda invocation cost at the expected volume before choosing; orchestration steps written in a general-purpose language within the same application codebase (share modules, data types, and test suites with application code); teams applying standard software-engineering practices (unit tests, code review, type checking) to orchestration logic without learning a declarative workflow language

Tradeoff (use when either fits): Durable Functions keeps orchestration in your Lambda codebase; Step Functions externalizes it into a managed, visual state machine with built-in service integrations.

Security: Both services persist workflow state and payloads — Step Functions records full input/output in execution history (viewable in the console and, if logging is enabled, CloudWatch Logs). As a baseline, enable execution logging (CloudTrail) and CloudWatch alarms on execution failures, and use least-privilege per-workflow execution roles. Do not pass secrets, tokens, or PII through workflow state; reference them by Secrets Manager/ARN pointer, and apply a customer-managed KMS key to encrypt state when the data is sensitive.

Step-by-step task procedures (tested CLI SOPs)

Use this skillFor the task
connecting-lambda-to-api-gatewayWire an existing Lambda to a new REST/HTTP API: proxy integration, permissions, CORS, throttling, access logging, deployment
connecting-lambda-to-dynamodbConnect Lambda to DynamoDB: IAM execution role, read/write permissions, stream event source mapping
creating-api-gateway-stageCreate an API Gateway stage with CloudWatch logging, X-Ray tracing, throttling, WAF association, and authorization
deploying-custom-domain-rest-apiDeploy a Regional REST API with custom domain: ACM cert, Lambda backend, request authorizer, base path mapping, Route 53 DNS
debugging-lambda-timeoutsSystematically diagnose a timing-out Lambda: config, CloudWatch logs/metrics, VPC, cold starts, memory, downstream calls
processing-s3-uploads-with-step-functionsDeploy an event-driven workflow: S3 upload → EventBridge → Step Functions → Lambda (small files) or Fargate (large files), with VPC/ECR/ECS/IAM

Routing (general references in this skill)

User needRead
Building a new serverless app — pattern selectionarchitecture.md
Lambda config, cold starts, SnapStart, memory, VPC, layers, Function URLslambda.md
Concurrency (reserved, provisioned, ESM controls)concurrency.md
Event sources (SQS, DynamoDB Streams, SNS, Kinesis), filtering, batch failuresevent-sources.md
Step Functions, EventBridge rules/pipes/schedulerorchestration.md
API Gateway quotas, authorizers, WebSocketapi-gateway.md
SAM/CDK resource types and fast iterationdeployment.md
Production readiness, observability, anti-patternsproduction.md
Debugging an error (exact string → cause → fix)troubleshooting.md
Powertools handler templatepowertools-handler.py

Note: Reference files contain specific runtime versions, quotas, and feature matrices that change. When precision matters (production, runtime choice, quotas), confirm against current AWS documentation. The references focus on values and gotchas that are easy to get wrong — not on basics.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aws Serverless AI skill do?

Builds, deploys, manages, debugs, configures, and optimizes serverless applications on AWS using Lambda, API Gateway, Step Functions, EventBridge, and SAM/CDK. Covers cold starts, CORS debugging, event source mappings, troubleshooting, concurrency, SnapStart, Powertools, function URLs, EventBridge Scheduler, Lambda layers, and production readiness. Triggers on mentions of Lambda, API Gateway, Step Functions, SAM templates, CDK serverless stacks, DynamoDB stream triggers, SQS event sources, cold starts, timeouts, 502/504 errors, throttling, concurrency, CORS, Powertools, or any event-driven...

Why use Aws Serverless on TypingMind?

Because you install it once and use it with any model. Aws Serverless is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aws Serverless in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-core/skills/aws-serverless. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aws Serverless?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aws Serverless?

As many as you like. As long as a model supports skills, you can use Aws Serverless with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aws Serverless AI skill free?

Yes. It is published on GitHub by aws under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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