Api Gateway logo

Api Gateway

Organization
awslabs
api-gateway

Build, manage, and operate APIs with Amazon API Gateway (REST, HTTP, and WebSocket). Triggers on phrases like: API Gateway, REST API, HTTP API, WebSocket API, custom domain, Lambda authorizer, usage plan, throttling, CORS, VPC link, private API. Also covers troubleshooting API Gateway errors (4xx, 5xx, timeout, CORS failures) and IaC templates containing API Gateway resources. For general REST API design unrelated to AWS, do not trigger.

Overview

Publisherawslabs
Repositoryagent-plugins
Skill nameapi-gateway
Stars
917
Forks
162
Bundled files
18
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.

  • 18 bundled files

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

  • Open source

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

Installation

Install the Api Gateway 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.
    https://github.com/awslabs/agent-plugins/tree/main/plugins/aws-serverless/skills/api-gateway
  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/awslabs/agent-plugins.git /tmp/agent-plugins
mkdir -p .claude/skills
cp -r /tmp/agent-plugins/plugins/aws-serverless/skills/api-gateway .claude/skills/api-gateway
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Api Gateway 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 Api Gateway 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 Api Gateway 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.

Amazon API Gateway Development

Expert guidance for building, managing, governing, and operating APIs with Amazon API Gateway. Covers REST APIs (v1), HTTP APIs (v2), and WebSocket APIs.

How to Use This Skill

When answering API Gateway questions:

  1. Read the relevant reference file(s) before responding, do not rely solely on this summary
  2. For tasks spanning multiple concerns (e.g., "private API with mTLS and custom domain"), read all relevant references
  3. When the user needs IaC templates, consult references/sam-cloudformation.md or references/sam-service-integrations.md and provide complete, working SAM/CloudFormation YAML
  4. Always mention relevant pitfalls and limits that affect the user's design

Quick Decision: Which API Type?

Choose the right API type first. This decision affects every downstream choice.

REST API is the full-featured API management platform for enterprises. It provides the governance, security, monetization, and operational controls that organizations need to build, publish, and manage APIs at scale, including usage plans with per-consumer throttling and quotas, API keys, request validation, WAF integration, resource policies, caching, canary deployments, and private endpoints.

HTTP API is the lightweight, low-cost proxy optimized for simpler API workloads. It offers ~70% lower cost and lower latency but trades away the API management features. Choose HTTP API when you need a fast, lightweight proxy to Lambda or HTTP backends and don't require the enterprise controls above.

FactorREST API (v1)HTTP API (v2)WebSocket API
PositioningFull API managementLow-cost proxyReal-time bidirectional
CostHigher~70% cheaperPer-message pricing
LatencyHigherLowerPersistent connection
Max timeout50ms-29s (up to 300s Regional/Private)30s hard limit29s
Payload10 MB10 MB128 KB message / 32 KB frame
API Management
Usage plans/API keysYesNoNo
Request validationYes (JSON Schema draft 4)NoNo
CachingYes (0.5-237 GB)NoNo
Custom gateway responsesYesNoNo
VTL mapping templatesYesNo (parameter mapping only)Yes
Security & Governance
WAFYesNo (use CloudFront + WAF)No
Resource policiesYesNoNo
Private endpointsYesNoNo
mTLSYes (Regional custom domain only)Yes (Regional custom domain only)Via CloudFront viewer mTLS
Auth
Lambda authorizerYes (TOKEN + REQUEST)Yes (REQUEST only, simple + IAM policy format)Yes (REQUEST on $connect only)
JWT authorizerNo (use Cognito authorizer)Yes (native)No
Cognito authorizerYes (native)Use JWT authorizerNo
Operations
Canary deploymentsYesNoNo
Response streamingYesNoNo
X-Ray tracingYesNoNo
Execution loggingYesNoYes
Custom domain sharingNot with WebSocketNot with WebSocketNot with REST/HTTP

Use REST API when: you are building APIs for external consumers, partners, or multi-tenant platforms; need to enforce per-consumer rate limits and quotas; require request validation, caching, or WAF at the API layer; need private endpoints, resource policies, or canary deployments; or are building an API product with monetization and governance requirements.

Use HTTP API when: you are building lightweight APIs or simple backend proxies; cost and latency are the primary concerns; you don't need per-consumer throttling, request validation, caching, or WAF at the API layer; and native JWT authorization with OIDC/OAuth 2.0 meets your auth needs. Accept the hard 30s timeout and lack of API management features. For WAF, edge caching, or edge compute, place a CloudFront distribution in front of the HTTP API.

Use WebSocket API when you need: persistent bidirectional connections for real-time use cases (chat, notifications, live dashboards).

Instructions

Step 1: Design the API

Before implementation, gather requirements systematically. Consult references/requirements-gathering.md for the full requirements workflow covering endpoints, auth, data models, performance, security, and deployment needs.

Key design decisions:

  1. API type: Use the decision table above
  2. Endpoint type: Edge-optimized (default for global clients; optimizes TCP connections via CloudFront POPs but does not cache at the edge), Regional (same-region clients, or global clients needing their own CloudFront distribution for edge caching, edge compute, granular WAF control, or geo-based routing), Private (VPC-only access, REST API only)
  3. Topology: Centralized (single domain, path-based routing) vs Distributed (subdomains per service)
  4. Authentication: See references/authentication.md for the decision tree

Step 2: Implement the API

Consult these references based on what you're building:

  • Architecture patterns: references/architecture-patterns.md: topology, multi-tenant SaaS, hybrid workloads, private APIs, multi-region, streaming
  • WebSocket API: references/websocket.md: route selection, @connections management, session management, client resilience, SAM templates, limits, multi-region
  • Service integrations: references/service-integrations.md: direct AWS service integrations (EventBridge, SQS, SNS, DynamoDB, Kinesis, Step Functions, S3), HTTP proxy, mock, VTL mapping templates, binary media types, Lambda sync/async invocation
  • Custom domains and routing: references/custom-domains-routing.md: base path mappings, routing rules, header-based versioning
  • Security: references/security.md: mTLS (API Gateway native + CloudFront viewer mTLS), TLS policies, resource policies, WAF, HttpOnly cookies, CRL checks
  • SAM/CloudFormation: references/sam-cloudformation.md: IaC patterns, OpenAPI extensions, VTL reference, binary data
  • SAM service integration templates: references/sam-service-integrations.md: EventBridge, SQS, DynamoDB CRUD, Kinesis, Step Functions (REST + WebSocket) templates

Step 3: Configure Performance and Scaling

  • Throttling: Account-level default is 10,000 rps / 5,000 burst (adjustable; request increases via AWS Support). Configure stage-level and method-level throttling via usage plans. See references/performance-scaling.md
  • Caching (REST only): Default TTL 300s, max 3600s. Only GET methods cached by default. Max cached response 1 MB
  • Edge caching (all API types): For edge caching, place a self-managed CloudFront distribution in front of a Regional API. CloudFront reduces latency, backend load, AND cost (cached responses never reach API Gateway). Also enables edge compute (CloudFront Functions, Lambda@Edge) and granular cache behaviors per path. Use a Regional endpoint, not edge-optimized, when pairing with your own CloudFront distribution
  • Scaling: API Gateway scales automatically but plan the entire stack (Lambda concurrency, DynamoDB capacity)

Step 4: Set Up Observability

Always configure access logging. For REST and WebSocket APIs, also enable execution logging (ERROR level for production, INFO only for debugging). HTTP API does not support execution logging; use access logs with enhanced observability variables instead.

Consult the observability references based on what you need:

  • Logging setup, log formats, retention: references/observability-logging.md
  • Metrics, alarms, metric filters, X-Ray tracing: references/observability-metrics-alarms.md
  • Log analysis and insights, analytics pipeline, cross-account, control plane logs: references/observability-analytics.md

Step 5: Deploy

  • Use Infrastructure as Code (SAM, CDK, CloudFormation, Terraform) for production
  • Canary deployments (REST only): Route a percentage of traffic to test new versions
  • Blue/green deployments: Use custom domain API mappings to switch between environments with zero downtime
  • Routing rules (preferred for new domains): Declarative header/path-based routing on custom domains for versioning, A/B testing, gradual rollouts, and cell-based routing
  • See references/deployment.md for detailed patterns

Step 6: Apply Governance

For organization-wide API standards, see references/governance.md covering:

  • Preventative controls (SCPs, IAM policies)
  • Proactive controls (CloudFormation Hooks, Guard rules)
  • Detective controls (AWS Config rules, EventBridge)
  • Specific enforcement examples for security, observability, and management

Response Format

When responding to API Gateway questions, structure your answer as:

  1. Recommendation: Lead with the recommended approach and why
  2. Code: Include SAM/CloudFormation YAML or code when the user needs implementation (always read the relevant reference file first)
  3. Pitfalls: Warn about relevant gotchas from the pitfalls below or from references/pitfalls.md
  4. Limits: Mention any service limits that constrain the design

Troubleshooting Quick Reference

When diagnosing API Gateway errors, consult references/troubleshooting.md for detailed resolution steps. Here are the most common issues:

ErrorMost Common CauseQuick Fix
400 Bad RequestProtocol mismatch (HTTP/HTTPS) with ALBMatch protocol to listener type
401 UnauthorizedWrong token type (ID vs access) or missing identity sourcesCheck token type matches scope config; verify all identity sources sent
403 Missing Auth TokenStage name in URL when using custom domainRemove stage name from URL path
403 from VPCPrivate DNS on VPC endpoint intercepts ALL API callsUse custom domain names for public APIs
403 Access DeniedResource policy + auth type mismatch or missing redeploymentReview policy, check auth type, redeploy API
403 mTLSCertificate issuer not in truststore or weak signature algorithmVerify CA in truststore, use SHA-256+
429 Too Many RequestsAccount/stage/method throttle limits exceededImplement jittered exponential backoff; request limit increase
500 Internal ErrorMissing Lambda invoke permission (especially with stage variables)Add resource-based policy to Lambda function
502 Bad GatewayLambda response not in required proxy formatReturn {statusCode, headers, body} from Lambda
504 TimeoutBackend exceeds 29s (REST, increasable) or 30s (HTTP, hard). HTTP API body says "Service Unavailable" but status is 504Optimize backend, request timeout increase (REST Regional/Private), or switch to async invocation
CORS errorsMissing CORS headers on Gateway Responses (4XX/5XX)Add CORS headers to DEFAULT_4XX and DEFAULT_5XX gateway responses
SSL/PKIX errorsIncomplete certificate chain on backendProvide full cert chain; use insecureSkipVerification only for testing

Critical Pitfalls

  1. REST API default timeout is 29 seconds (increasable up to 300s for Regional/Private endpoints via quota request). Lambda continues running but client gets 504. Request a timeout increase, or consider async patterns (SQS, EventBridge) for better user experience on long operations
  2. HTTP API hard timeout is 30 seconds. Returns {"message":"Service Unavailable"} while Lambda continues
  3. /ping and /sping are reserved paths. Do not use for API resources
  4. Execution log events truncated at 1,024 bytes. Use access logs for complete data
  5. 413 REQUEST_TOO_LARGE is the only gateway response that cannot be customized. Use DEFAULT_4XX as a catch-all to add CORS headers for all 4xx errors including 413
  6. maxItems/minItems not validated in REST API request validation
  7. Root-level security in OpenAPI is ignored. Must set per-operation
  8. JWT authorizer public keys cached 2 hours. Account for this in key rotation
  9. Management API rate limit: 10 rps / 40 burst. Heavy automation can hit this
  10. Always redeploy REST API after configuration changes. Changes don't take effect until deployed
  11. Edge-optimized endpoints do NOT cache at the edge — they only optimize TCP connections via CloudFront POPs. If you need edge caching, edge compute (CloudFront Functions, Lambda@Edge), or granular CloudFront control, use a Regional API with your own CloudFront distribution instead

For additional pitfalls (header handling, URL encoding, caching charges, canary deployments, usage plans), see references/pitfalls.md.

IaC Framework Selection

Default: CDK TypeScript

Override syntax:

  • "use SAM" → Generate SAM/CloudFormation YAML templates
  • "use CloudFormation" → Generate CloudFormation YAML templates
  • "use Terraform" → Generate Terraform HCL

When not specified, ALWAYS use CDK TypeScript.

Error Scenarios

MCP Server Unavailable

  • Inform user: "AWS Serverless MCP not responding"
  • Ask: "Proceed without MCP support?"
  • DO NOT continue without user confirmation

Service Limits Quick Reference

See references/service-limits.md for the complete table. Most numeric quotas below are default values and adjustable; check with your AWS account team and the latest quotas page before using them for architectural decisions. Key limits:

ResourceREST APIHTTP APIWebSocket
Payload size10 MB10 MB128 KB
Integration timeout50ms-29s (up to 300s Regional/Private)30s hard29s
APIs per region600 Regional/Private; 120 Edge-optimized600600
Stages per API101010
Routes/resources per API300300300
Custom domains (public)120120120
Account throttle10,000 rps / 5,000 burstSameSame (shared quota)
API keys per region10,000N/AN/A
Usage plans per region300N/AN/A
Cache sizes0.5 GB - 237 GBN/AN/A

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 Api Gateway AI skill do?

Build, manage, and operate APIs with Amazon API Gateway (REST, HTTP, and WebSocket). Triggers on phrases like: API Gateway, REST API, HTTP API, WebSocket API, custom domain, Lambda authorizer, usage plan, throttling, CORS, VPC link, private API. Also covers troubleshooting API Gateway errors (4xx, 5xx, timeout, CORS failures) and IaC templates containing API Gateway resources. For general REST API design unrelated to AWS, do not trigger.

Why use Api Gateway on TypingMind?

Because you install it once and use it with any model. Api Gateway 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 Api Gateway in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/awslabs/agent-plugins/tree/main/plugins/aws-serverless/skills/api-gateway. 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 Api Gateway?

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 Api Gateway?

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

Is the Api Gateway AI skill free?

Yes. It is published on GitHub by awslabs 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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