Provides AWS CloudFormation patterns for DynamoDB tables, GSIs, LSIs, auto-scaling, and streams. Use when creating DynamoDB tables with CloudFormation, configuring primary keys, local/global secondary indexes, capacity modes (on-demand/provisioned), point-in-time recovery, encryption, TTL, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references.
npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill aws-cloudformation-dynamodb
Provides production-ready NoSQL database infrastructure patterns using AWS CloudFormation templates with DynamoDB tables, GSIs, LSIs, auto-scaling, encryption, TTL, and streams.
Covers DynamoDB tables, primary keys, secondary indexes (GSI/LSI), capacity modes, auto-scaling, encryption, TTL, streams, and best practices for parameters, outputs, and cross-stack references.
Creating DynamoDB tables, configuring keys and indexes, setting capacity modes, implementing auto-scaling, enabling encryption/TTL/streams, and organizing CloudFormation templates.
Follow these steps to create DynamoDB tables with CloudFormation:
aws cloudformation validate-template before deployment10. Deploy Stack: Use aws cloudformation create-stack or update-stack
11. Monitor Events: Check aws cloudformation describe-stack-events for failures or ROLLBACK status
12. Handle Rollback: On failure, review events for resource errors, fix the template, and re-deploy
| Resource Type | Purpose |
|---------------|---------|
| AWS::DynamoDB::Table | Create DynamoDB table |
| AWS::ApplicationAutoScaling::ScalableTarget | Auto scaling configuration |
| AWS::ApplicationAutoScaling::ScalingPolicy | Scaling policies |
| AWS::KMS::Key | KMS key for encryption |
| AWS::IAM::Role | IAM roles for auto scaling |
| BillingMode | PAY_PER_REQUEST or PROVISIONED |
| SSESpecification | Server-side encryption |
DynamoDBTable:
Type: AWS::DynamoDB::Table
Properties:
TableName: !Sub "${AWS::StackName}-table"
BillingMode: PAY_PER_REQUEST
AttributeDefinitions:
- AttributeName: pk
AttributeType: S
KeySchema:
- AttributeName: pk
KeyType: HASH
DynamoDBTable:
Type: AWS::DynamoDB::Table
Properties:
TableName: !Sub "${AWS::StackName}-table"
BillingMode: PAY_PER_REQUEST
AttributeDefinitions:
- AttributeName: pk
AttributeType: S
- AttributeName: gsi-pk
AttributeType: S
KeySchema:
- AttributeName: pk
KeyType: HASH
GlobalSecondaryIndexes:
- IndexName: gsi-index
KeySchema:
- AttributeName: gsi-pk
KeyType: HASH
Projection:
ProjectionType: ALL
SessionTable:
Type: AWS::DynamoDB::Table
Properties:
TableName: !Sub "${AWS::StackName}-sessions"
BillingMode: PAY_PER_REQUEST
AttributeDefinitions:
- AttributeName: sessionId
AttributeType: S
KeySchema:
- AttributeName: sessionId
KeyType: HASH
TimeToLiveSpecification:
AttributeName: expiresAt
Enabled: true
ScalableTarget:
Type: AWS::ApplicationAutoScaling::ScalableTarget
Properties:
MaxCapacity: 100
MinCapacity: 5
ResourceId: !Sub "table/${DynamoDBTable}"
RoleARN: !GetAtt AutoScalingRole.Arn
ScalableDimension: dynamodb:table:ReadCapacityUnits
ServiceNamespace: dynamodb
See references/complete-examples.md for more complete examples including encryption, streams, auto scaling, and production tables.
AWSTemplateFormatVersion: 2010-09-09
Description: DynamoDB table with GSI and auto-scaling
Parameters:
TableName:
Type: String
Default: my-table
BillingMode:
Type: String
Default: PAY_PER_REQUEST
Resources:
DynamoDBTable:
Type: AWS::DynamoDB::Table
Properties:
TableName: !Ref TableName
BillingMode: !Ref BillingMode
Outputs:
TableName:
Value: !Ref DynamoDBTable
TableArn:
Value: !GetAtt DynamoDBTable.Arn
See references/advanced-configuration.md for detailed Parameters, Mappings, Conditions, Outputs, IAM roles, and cross-stack references.
10. Use Conditions for environment-specific configurations
Table already exists: Use unique table names or stack deletion policy
GSI creation fails: Verify attribute definitions include GSI attributes
Auto-scaling not working: Check IAM role permissions and service-linked role
TTL not expiring: Ensure TTL attribute is Number type, not String
Streams not enabled: Can only enable streams during table creation
Encryption errors: Verify KMS key exists in same region as table
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.
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