Provision and manage RDS databases. Configure backups, replication, and security. Use when deploying managed relational databases on AWS.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill aws-rds
Deploy and manage Amazon RDS relational databases with production-grade backups, replication, monitoring, and security.
rds:*, ec2:DescribeSecurityGroups, ec2:DescribeSubnets, kms:*, cloudwatch:*# Create a subnet group spanning two AZs
aws rds create-db-subnet-group \
--db-subnet-group-name production-db-subnets \
--db-subnet-group-description "Production database subnets" \
--subnet-ids subnet-private-a subnet-private-b
# List subnet groups
aws rds describe-db-subnet-groups \
--query "DBSubnetGroups[].{Name:DBSubnetGroupName,VPC:VpcId,Status:SubnetGroupStatus}" \
--output table
# Create a PostgreSQL 16 Multi-AZ instance
aws rds create-db-instance \
--db-instance-identifier production-api-db \
--db-instance-class db.r6g.large \
--engine postgres \
--engine-version 16.4 \
--master-username appadmin \
--manage-master-user-password \
--allocated-storage 100 \
--max-allocated-storage 500 \
--storage-type gp3 \
--storage-encrypted \
--kms-key-id alias/rds-key \
--vpc-security-group-ids sg-db-access \
--db-subnet-group-name production-db-subnets \
--db-name appdb \
--backup-retention-period 14 \
--preferred-backup-window "03:00-04:00" \
--preferred-maintenance-window "sun:05:00-sun:06:00" \
--multi-az \
--auto-minor-version-upgrade \
--deletion-protection \
--copy-tags-to-snapshot \
--monitoring-interval 60 \
--monitoring-role-arn arn:aws:iam::123456789012:role/rds-monitoring-role \
--enable-performance-insights \
--performance-insights-retention-period 7 \
--enable-cloudwatch-logs-exports '["postgresql","upgrade"]' \
--tags '[
{"Key":"Environment","Value":"production"},
{"Key":"Team","Value":"backend"},
{"Key":"Backup","Value":"daily"}
]'
# Wait for instance to become available
aws rds wait db-instance-available --db-instance-identifier production-api-db
# Get connection endpoint
aws rds describe-db-instances \
--db-instance-identifier production-api-db \
--query "DBInstances[0].Endpoint.{Address:Address,Port:Port}" \
--output table
# When using --manage-master-user-password, RDS stores the password in Secrets Manager
aws rds describe-db-instances \
--db-instance-identifier production-api-db \
--query "DBInstances[0].MasterUserSecret.SecretArn" \
--output text
# Retrieve the secret value
aws secretsmanager get-secret-value \
--secret-id arn:aws:secretsmanager:us-east-1:123456789012:secret:rds-db-secret-abc123 \
--query SecretString --output text
# Create a custom parameter group
aws rds create-db-parameter-group \
--db-parameter-group-name production-pg16 \
--db-parameter-group-family postgres16 \
--description "Production PostgreSQL 16 parameters"
# Set performance parameters
aws rds modify-db-parameter-group \
--db-parameter-group-name production-pg16 \
--parameters \
"ParameterName=max_connections,ParameterValue=200,ApplyMethod=pending-reboot" \
"ParameterName=shared_buffers,ParameterValue={DBInstanceClassMemory/4},ApplyMethod=pending-reboot" \
"ParameterName=effective_cache_size,ParameterValue={DBInstanceClassMemory*3/4},ApplyMethod=pending-reboot" \
"ParameterName=work_mem,ParameterValue=65536,ApplyMethod=immediate" \
"ParameterName=maintenance_work_mem,ParameterValue=524288,ApplyMethod=immediate" \
"ParameterName=random_page_cost,ParameterValue=1.1,ApplyMethod=immediate" \
"ParameterName=log_min_duration_statement,ParameterValue=1000,ApplyMethod=immediate" \
"ParameterName=log_statement,ParameterValue=ddl,ApplyMethod=immediate" \
"ParameterName=idle_in_transaction_session_timeout,ParameterValue=60000,ApplyMethod=immediate"
# Apply parameter group to the instance
aws rds modify-db-instance \
--db-instance-identifier production-api-db \
--db-parameter-group-name production-pg16 \
--apply-immediately
# Create a read replica in the same region
aws rds create-db-instance-read-replica \
--db-instance-identifier production-api-db-read1 \
--source-db-instance-identifier production-api-db \
--db-instance-class db.r6g.large \
--availability-zone us-east-1b \
--enable-performance-insights \
--monitoring-interval 60 \
--monitoring-role-arn arn:aws:iam::123456789012:role/rds-monitoring-role
# Create a cross-region read replica for DR
aws rds create-db-instance-read-replica \
--db-instance-identifier dr-api-db-read \
--source-db-instance-identifier arn:aws:rds:us-east-1:123456789012:db:production-api-db \
--db-instance-class db.r6g.large \
--region us-west-2 \
--storage-encrypted \
--kms-key-id alias/rds-dr-key
# Promote a read replica to standalone (for DR failover)
aws rds promote-read-replica \
--db-instance-identifier dr-api-db-read
# Check replication lag
aws cloudwatch get-metric-statistics \
--namespace AWS/RDS \
--metric-name ReplicaLag \
--dimensions Name=DBInstanceIdentifier,Value=production-api-db-read1 \
--start-time "$(date -u -d '1 hour ago' +%Y-%m-%dT%H:%M:%SZ)" \
--end-time "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \
--period 300 \
--statistics Average \
--output table
# Create a manual snapshot
aws rds create-db-snapshot \
--db-instance-identifier production-api-db \
--db-snapshot-identifier production-api-db-pre-migration-$(date +%Y%m%d)
# Wait for snapshot to complete
aws rds wait db-snapshot-available \
--db-snapshot-identifier production-api-db-pre-migration-20260324
# Restore from snapshot (creates a new instance)
aws rds restore-db-instance-from-db-snapshot \
--db-instance-identifier production-api-db-restored \
--db-snapshot-identifier production-api-db-pre-migration-20260324 \
--db-instance-class db.r6g.large \
--db-subnet-group-name production-db-subnets \
--vpc-security-group-ids sg-db-access
# Point-in-time recovery (restore to a specific second)
aws rds restore-db-instance-to-point-in-time \
--source-db-instance-identifier production-api-db \
--target-db-instance-identifier production-api-db-pitr \
--restore-time "2026-03-24T10:30:00Z" \
--db-instance-class db.r6g.large \
--db-subnet-group-name production-db-subnets
# Copy snapshot to another region
aws rds copy-db-snapshot \
--source-db-snapshot-identifier arn:aws:rds:us-east-1:123456789012:snapshot:production-api-db-pre-migration-20260324 \
--target-db-snapshot-identifier production-api-db-dr-copy \
--region us-west-2 \
--kms-key-id alias/rds-dr-key
# Delete old snapshots
aws rds delete-db-snapshot --db-snapshot-identifier old-snapshot-name
# Set CPU utilization alarm
aws cloudwatch put-metric-alarm \
--alarm-name rds-production-cpu-high \
--alarm-description "RDS CPU > 80% for 5 minutes" \
--metric-name CPUUtilization \
--namespace AWS/RDS \
--dimensions Name=DBInstanceIdentifier,Value=production-api-db \
--statistic Average \
--period 300 \
--threshold 80 \
--comparison-operator GreaterThanThreshold \
--evaluation-periods 1 \
--alarm-actions arn:aws:sns:us-east-1:123456789012:db-alerts
# Set free storage space alarm (alert below 10 GB)
aws cloudwatch put-metric-alarm \
--alarm-name rds-production-storage-low \
--alarm-description "RDS free storage < 10GB" \
--metric-name FreeStorageSpace \
--namespace AWS/RDS \
--dimensions Name=DBInstanceIdentifier,Value=production-api-db \
--statistic Average \
--period 300 \
--threshold 10737418240 \
--comparison-operator LessThanThreshold \
--evaluation-periods 1 \
--alarm-actions arn:aws:sns:us-east-1:123456789012:db-alerts
# Check current database connections
aws cloudwatch get-metric-statistics \
--namespace AWS/RDS \
--metric-name DatabaseConnections \
--dimensions Name=DBInstanceIdentifier,Value=production-api-db \
--start-time "$(date -u -d '1 hour ago' +%Y-%m-%dT%H:%M:%SZ)" \
--end-time "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \
--period 300 \
--statistics Average Maximum \
--output table
resource "aws_db_subnet_group" "main" {
name = "production-db-subnets"
subnet_ids = [aws_subnet.private_a.id, aws_subnet.private_b.id]
tags = {
Environment = "production"
}
}
resource "aws_db_parameter_group" "postgres16" {
name = "production-pg16"
family = "postgres16"
parameter {
name = "max_connections"
value = "200"
}
parameter {
name = "shared_buffers"
value = "{DBInstanceClassMemory/4}"
apply_method = "pending-reboot"
}
parameter {
name = "log_min_duration_statement"
value = "1000"
}
}
resource "aws_db_instance" "main" {
identifier = "production-api-db"
engine = "postgres"
engine_version = "16.4"
instance_class = "db.r6g.large"
allocated_storage = 100
max_allocated_storage = 500
storage_type = "gp3"
storage_encrypted = true
kms_key_id = aws_kms_key.rds.arn
db_name = "appdb"
username = "appadmin"
manage_master_user_password = true
multi_az = true
db_subnet_group_name = aws_db_subnet_group.main.name
vpc_security_group_ids = [aws_security_group.db.id]
parameter_group_name = aws_db_parameter_group.postgres16.name
backup_retention_period = 14
backup_window = "03:00-04:00"
maintenance_window = "sun:05:00-sun:06:00"
copy_tags_to_snapshot = true
deletion_protection = true
skip_final_snapshot = false
final_snapshot_identifier = "production-api-db-final"
performance_insights_enabled = true
performance_insights_retention_period = 7
monitoring_interval = 60
monitoring_role_arn = aws_iam_role.rds_monitoring.arn
enabled_cloudwatch_logs_exports = ["postgresql", "upgrade"]
tags = {
Environment = "production"
Team = "backend"
}
}
resource "aws_db_instance" "read_replica" {
identifier = "production-api-db-read1"
replicate_source_db = aws_db_instance.main.identifier
instance_class = "db.r6g.large"
performance_insights_enabled = true
monitoring_interval = 60
monitoring_role_arn = aws_iam_role.rds_monitoring.arn
}
| Problem | Cause | Fix |
|---|---|---|
| Cannot connect to RDS | Security group blocks traffic | Verify SG allows app subnet CIDR on DB port |
| Storage full | Auto-scaling not enabled or limit reached | Set --max-allocated-storage; increase manually |
| High replication lag | Write-heavy workload or replica undersized | Upgrade replica instance class; reduce write volume |
| Parameter change not applied | Requires reboot for static params | Reboot with --force-failover during maintenance window |
| Snapshot restore slow | Large database size | Use larger instance class for restore; consider PITR |
| Performance Insights empty | Not enabled or instance type unsupported | Enable PI; check instance class supports it |
| Multi-AZ failover happened | Hardware or AZ failure | Check RDS events; review failover logs |
| Connection count maxed out | Application connection leak | Implement connection pooling (PgBouncer/RDS Proxy) |
| Master password unknown | Using managed secret | Retrieve from Secrets Manager ARN in instance details |
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 bagelhole/aws-rds 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.