Provides patterns to deploy ECS tasks and services with GitHub Actions CI/CD. Use when building Docker images, pushing to ECR, updating ECS task definitions, deploying ECS services, integrating with CloudFormation stacks, configuring AWS OIDC authentication for GitHub Actions, and implementing production-ready container deployment pipelines. Supports ECS deployments with proper security (OIDC or IAM keys), multi-environment support, blue/green deployments, ECR private repositories with image scanning, and CloudFormation infrastructure updates.
npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill aws-cloudformation-task-ecs-deploy-gh
Comprehensive skill for deploying ECS containers using GitHub Actions CI/CD pipelines with CloudFormation infrastructure management.
Deploy containerized applications to Amazon ECS using GitHub Actions workflows. This skill covers the complete deployment pipeline: authentication with AWS (OIDC recommended), building Docker images, pushing to Amazon ECR, updating task definitions, and deploying ECS services. Integrate with CloudFormation for infrastructure-as-code management and implement production-grade deployment strategies.
Follow these steps to set up ECS deployment with GitHub Actions:
name: Deploy to ECS
on:
push:
branches: [main]
jobs:
deploy:
runs-on: ubuntu-latest
permissions:
id-token: write
contents: read
steps:
- uses: actions/checkout@v4
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::123456789012:role/github-actions-ecs-role
aws-region: us-east-1
- name: Login to ECR
uses: aws-actions/amazon-ecr-login@v2
- name: Build and push image
env:
ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
ECR_REPOSITORY: my-app
IMAGE_TAG: ${{ github.sha }}
run: |
docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG .
docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
- name: Verify image push
run: |
docker pull $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
echo "Image $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG verified"
- name: Update task definition
uses: aws-actions/amazon-ecs-render-task-definition@v1
id: render-task
with:
task-definition: task-definition.json
container-name: my-app
image: ${{ steps.login-ecr.outputs.registry }}/my-app:${{ github.sha }}
- name: Validate task definition
run: |
# Validate JSON syntax
cat ${{ steps.render-task.outputs.task-definition }} | jq empty && echo "Task definition JSON is valid"
# Verify container image matches expected
CONTAINER_IMAGE=$(cat ${{ steps.render-task.outputs.task-definition }} | jq -r '.containerDefinitions[0].image')
EXPECTED_IMAGE="${{ steps.login-ecr.outputs.registry }}/my-app:${{ github.sha }}"
if [ "$CONTAINER_IMAGE" = "$EXPECTED_IMAGE" ]; then
echo "Container image matches expected: $CONTAINER_IMAGE"
else
echo "ERROR: Container image mismatch. Expected: $EXPECTED_IMAGE, Got: $CONTAINER_IMAGE"
exit 1
fi
- name: Deploy to ECS
uses: aws-actions/amazon-ecs-deploy-task-definition@v1
with:
task-definition: ${{ steps.render-task.outputs.task-definition }}
service: my-service
cluster: my-cluster
wait-for-service-stability: true
See references/workflow-examples.md for complete workflow examples including multi-environment and blue/green deployments.
jobs:
deploy:
strategy:
matrix:
environment: [dev, staging, prod]
steps:
- uses: actions/checkout@v4
- name: Configure AWS credentials
uses: aws-actions/configure-aws-credentials@v4
with:
role-to-assume: arn:aws:iam::${{ matrix.env_account }}:role/github-actions-ecs-role
aws-region: ${{ matrix.region }}
- name: Deploy to ${{ matrix.environment }}
run: |
ECR_REGISTRY=${{ env.ECR_REGISTRY }}
docker build -t $ECR_REGISTRY/my-app:${{ github.sha }} .
docker push $ECR_REGISTRY/my-app:${{ github.sha }}
- name: Deploy with CodeDeploy
run: |
aws deploy create-deployment \
--application-name my-app \
--deployment-group-name ${{ matrix.environment }} \
--deployment-config-name CodeDeployDefault ECSAllAtOnce \
--revision "{\"revisionType\":\"AppSpecContent\",\"appSpecContent\":{\"content\":\"$(cat appspec.yml)\",\"filename\":\"appspec.yml\"}}"
aws deploy wait deployment-successful --deployment-id $(aws deploy list-deployments --application-name my-app --query 'deployments[0]' --output text)
See references/workflow-examples.md for additional patterns including ECR lifecycle policies, task definition templates, and CloudFormation stack updates.
See references/best-practices.md for detailed security, performance, and cost optimization guidelines.
id-token: write permissioncloudformation:UpdateStack permissionSee references/best-practices.md for complete troubleshooting guide with debug commands.
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 giuseppe-trisciuoglio/aws-cloudformation-task-ecs-deploy-gh 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.