Provides AWS CloudFormation patterns for ElastiCache Redis or Memcached infrastructure, including subnet groups, parameter groups, security controls, and cross-stack outputs. Use when designing cache tiers, high-availability replication groups, encryption settings, or reusable CloudFormation templates for application caching.
npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill aws-cloudformation-elasticache
Use this skill to model ElastiCache infrastructure with CloudFormation without turning SKILL.md into a full service manual.
Focus on the delivery decisions that matter most:
Use the bundled references/ documents for larger production templates and service-specific detail.
Use this skill when:
AWS::ElastiCache::CacheCluster and AWS::ElastiCache::ReplicationGroupTypical trigger phrases include cloudformation elasticache, redis replication group, memcached cluster, cache subnet group, and export redis endpoint.
Use:
ReplicationGroup for production Redis-style deployments that need failover, replicas, or shardingCacheCluster for Memcached or simple single-node cache scenariosDo not start with resource YAML before deciding whether the application needs durability, read replicas, cluster mode, or just an ephemeral cache.
Create and wire:
Keep the cache private unless there is a very unusual and well-reviewed reason not to.
For production-style Redis deployments, decide on:
For lower environments, document when a cheaper single-node configuration is acceptable.
Parameterize only the settings that truly vary between environments, such as node type, subnet IDs, or snapshot retention.
Export outputs that other stacks need, typically:
Before deployment:
Parameters:
CacheNodeType:
Type: String
Default: cache.t4g.small
Resources:
CacheSubnetGroup:
Type: AWS::ElastiCache::SubnetGroup
Properties:
Description: Private subnets for the cache tier
SubnetIds: !Ref PrivateSubnetIds
CacheSecurityGroup:
Type: AWS::EC2::SecurityGroup
Properties:
GroupDescription: Application access to Redis
VpcId: !Ref VpcId
RedisReplicationGroup:
Type: AWS::ElastiCache::ReplicationGroup
Properties:
ReplicationGroupDescription: Application Redis cluster
Engine: redis
CacheNodeType: !Ref CacheNodeType
NumNodeGroups: 1
ReplicasPerNodeGroup: 1
AutomaticFailoverEnabled: true
MultiAZEnabled: true
CacheSubnetGroupName: !Ref CacheSubnetGroup
SecurityGroupIds:
- !Ref CacheSecurityGroup
TransitEncryptionEnabled: true
AtRestEncryptionEnabled: true
Outputs:
RedisPrimaryEndpoint:
Description: Primary endpoint used by the application stack
Value: !GetAtt RedisReplicationGroup.PrimaryEndPoint.Address
Export:
Name: !Sub "${AWS::StackName}-RedisPrimaryEndpoint"
Keep outputs small and stable so consumer stacks do not break unnecessarily.
references/examples.md instead of expanding the root skill endlessly.references/examples.mdreferences/reference.mdaws-cloudformation-vpcaws-cloudformation-securityaws-cloudformation-ecsaws-cloudformation-lambdaAssess 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-elasticache 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.