Deploy to AWS Elastic Beanstalk. Triggers on: elastic beanstalk, EB, managed EC2 platform, web app with managed patching, worker on EC2, Heroku alternative, don't want to manage servers or container orchestration, migrate from Heroku, managed operational lifecycle. Covers Elastic Beanstalk on EC2 for web and worker applications.
npx skills add https://github.com/awslabs/agent-plugins --skill elastic-beanstalk
Deploy web and worker applications to production on AWS with full lifecycle
management. Elastic Beanstalk is an application management service: the user
provides application code, AWS manages everything underneath (deployment, scaling,
patching, monitoring, health response).
Elastic Beanstalk is the right choice when:
health monitoring, rollback, deployments) after initial setup
the user signals low infrastructure involvement
Elastic Beanstalk is NOT the right choice when:
model (event-driven functions, stateless, cold starts, 15-min max execution)
rather than just eliminating server management
backend API separately if present)
ECS and EKS are infrastructure management services: the user defines and
operates the deployment infrastructure (task definitions, services, clusters,
scaling policies) and owns ongoing operational decisions. Elastic Beanstalk is
an application management service: the user provides source code or a Docker
image, and AWS provisions and operates the production environment on an ongoing
basis. The result is the same reliability, but with lower ongoing maintenance
cost because operational responsibility stays with the provider.
Both models support IaC (CDK, CloudFormation, Terraform). The distinction is not
about tooling — it is about who manages the lifecycle after deployment.
Lambda/serverless is a different axis entirely. "Don't want to manage servers"
does not mean "wants serverless" — Elastic Beanstalk also eliminates server
management while preserving the standard application programming model
(long-running processes, persistent connections, threads, local state).
Serverless imposes a specific programming model: stateless functions, cold
starts, event-driven invocation, and a 15-minute execution ceiling. Route to
Lambda only when the user explicitly asks for serverless or the workload is
natively event-driven (e.g., S3 triggers, API Gateway request/response with
no session state).
This skill is invoked after the deploy skill selects Elastic Beanstalk as the
deployment target. The deploy skill handles codebase analysis and cost estimation.
This skill handles EB-specific configuration:
| Setting | Dev | Production |
| ------------------------- | --------------------------------- | --------------------------------- |
| Environment type (web) | Load-balanced (min=1, max=1) | Load-balanced, Multi-AZ |
| Environment type (worker) | Auto Scaling group (min=1, max=1) | Auto Scaling group (min=2, max=4) |
| Instance | t3.small | t3.medium or larger |
| Deployments | All-at-once | Rolling with additional batch |
| Health reporting | Enhanced | Enhanced |
| Managed updates | Enabled (weekly) | Enabled (maintenance window) |
| HTTPS (web only) | ACM certificate + ALB | ACM certificate + ALB |
Default to dev unless user says "production" or "prod".
Always use load-balanced environments for web server types. This ensures
instances stay in private subnets behind an ALB, HTTPS terminates via ACM
automatically, and scaling up later is a config change rather than an environment
type migration. Dev deployments with min=max=1 cause brief downtime on deploy
(single instance, all-at-once). If zero-downtime dev is needed, use min=1 max=2
with rolling.
Worker environments do not have load balancers — they receive work from SQS and
are scaled via Auto Scaling group settings.
| Signal in Codebase | Environment Type |
| ----------------------------------------------------- | ---------------------------------------- |
| HTTP listener, web framework, API routes | Web server |
| Queue-based consumer, SQS processing, no HTTP serving | Worker |
| HTTP serving + queue-based background processing | Web server + separate Worker environment |
Worker environments receive work via an SQS queue managed by Elastic Beanstalk.
EB's SQS daemon sends HTTP POST requests to the application at a configurable
path (default: POST /). The application must expose this HTTP endpoint to
process each message — no SQS SDK integration required.
Worker environments also support periodic tasks via cron.yaml for scheduled
jobs (alternative to EventBridge + Lambda when the user is already using EB).
If the app uses in-process background threads or async tasks (not queue-based),
a single web server environment is sufficient — do not create a separate Worker.
Default: AWS CLI — no extra tooling to install. The agent orchestrates
the multi-step workflow:
aws elasticbeanstalk create-storage-location → returns the S3 bucket(idempotent — returns existing bucket if already created)
aws elasticbeanstalk create-applicationaws elasticbeanstalk create-application-versionaws elasticbeanstalk create-environment with --option-settings (web:--tier Name=WebServer,Type=Standard, worker: --tier Name=Worker,Type=SQS/HTTP)
aws elasticbeanstalk wait environment-updatedupdate-environmentResolve the --solution-stack-name by running
aws elasticbeanstalk list-available-solution-stacks and filtering for the
detected platform (e.g., ".NET" + "Amazon Linux 2023"). Alternatively, use
--platform-arn from aws elasticbeanstalk list-platform-versions.
Use .ebextensions/ and platform hooks for customization.
for full command documentation.
Override: CDK (TypeScript) when the user has an existing CDK project, wants
repeatable IaC, or explicitly requests it:
CfnApplication, CfnEnvironment, CfnConfigurationTemplateOverride: Terraform when the user's repo already has Terraform:
aws_elastic_beanstalk_application, aws_elastic_beanstalk_environmentCDK and Terraform templates are scannable by cfn-nag/checkov pre-deploy.
Apply these automatically:
AWS SDK client usage to determine required actions (e.g.,
AmazonBedrockRuntimeClient → bedrock:InvokeModel,
AmazonS3Client → s3:GetObject/s3:PutObject on specific buckets)
See the deploy skill's security defaults
for encryption, VPC placement, and IAM patterns.
Elastic Beanstalk has no service fee. Cost = underlying AWS resources.
Query the awspricing MCP server for region-accurate estimates. Approximate
us-east-1 pricing:
| Configuration | Estimated Monthly Cost |
| --------------------------------------------- | ---------------------- |
| Dev web (1x t3.small + ALB) | ~$35-40 |
| Dev worker (1x t3.small, no ALB) | ~$15-20 |
| Production web (4x t3.medium + ALB, Multi-AZ) | ~$150-200 |
Add RDS/Aurora costs separately if database is included.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take awslabs/elastic-beanstalk from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.