Provides advanced AWS CLI patterns for managing EC2, Lambda, S3, DynamoDB, RDS, VPC, IAM, and CloudWatch. Generates bulk operation scripts, automates cross-service workflows, validates security configurations, and executes JMESPath queries for complex filtering. Triggers on "aws cli help", "aws command line", "aws scripting", "aws automation", "aws batch operations", "aws bulk operations", "aws cli pagination", "aws multi-region", "aws profiles", "aws cli troubleshooting".
npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill aws-cli-beast
Advanced AWS CLI patterns for speed, precision, and security-first automation. Covers JMESPath queries, bulk operations, waiters, cross-account access, and destructive operation safety.
| Category | Services | Commands |
|----------|----------|----------|
| Compute | EC2, Lambda | describe-instances, invoke, publish-version |
| Storage | S3 | sync, cp, mb, rb, presign |
| Database | DynamoDB, RDS | query, scan, batch-write-item |
| Networking | VPC, Route53 | describe-vpcs, describe-security-groups |
| Security | IAM | simulate-principal-policy, get-policy-version |
| Observability | CloudWatch | get-metric-statistics, filter-log-events |
--dryrun or --dry-run--query with JMESPath to filter before transfer--max-results and parallelize with xargs--profile and --region for multi-account operationsMANDATORY for any destructive operation:
# S3 sync with delete - MUST dry-run first
aws s3 sync s3://source/ s3://dest/ --delete --dryrun
# Review output, then remove --dryrun only if satisfied
# Bulk EC2 stop - validate targets first
aws ec2 describe-instances \
--filters "Name=tag:Environment,Values=development" \
--query 'Reservations[].Instances[?State.Name==`running`].InstanceId' \
--output text
# Confirm list, then pipe to stop command
# IAM policy attachment - simulate first
aws iam simulate-principal-policy \
--policy-source-arn arn:aws:iam::123456789012:user/myuser \
--action-names s3:DeleteObject \
--resource-arns arn:aws:s3:::my-bucket/*
compute-mastery.md - EC2, Lambda, Spot Fleets, ASGdata-ops-beast.md - S3 multipart, DynamoDB batch, RDS snapshotsnetworking-security-hardened.md - VPC Flow Logs, IAM policies, security groupsautomation-patterns.md - Shell aliases, JMESPath templates, CI/CD integration"Stop all development instances"
# 1. Dry-run: identify targets
aws ec2 describe-instances \
--filters "Name=tag:Environment,Values=development" \
"Name=instance-state-name,Values=running" \
--query 'Reservations[].Instances[].InstanceId' \
--output text
# 2. Confirm IDs, then execute
aws ec2 describe-instances \
--filters "Name=tag:Environment,Values=development" \
"Name=instance-state-name,Values=running" \
--query 'Reservations[].Instances[].InstanceId' \
--output text | xargs aws ec2 stop-instances --instance-ids
"Migrate data between buckets with SSE"
# 1. Dry-run migration
aws s3 sync s3://source-bucket/ s3://dest-bucket/ \
--sse AES256 \
--storage-class GLACIER \
--exclude "*.tmp" \
--dryrun
# 2. Enable versioning on destination
aws s3api put-bucket-versioning \
--bucket dest-bucket \
--versioning-configuration Status=Enabled
# 3. Execute after review
aws s3 sync s3://source-bucket/ s3://dest-bucket/ \
--sse AES256 \
--storage-class GLACIER \
--exclude "*.tmp"
"Find overprivileged IAM users"
aws iam list-users --query 'Users[].UserName' --output text | \
while read user; do
echo "Checking $user..."
aws iam simulate-principal-policy \
--policy-source-arn "arn:aws:iam::123456789012:user/$user" \
--action-names DeleteItem,DeleteTable,DeleteFunction \
--resource-arns "*" \
--query 'EvaluationResults[?EvalDecision==`allowed`]'
done
"Deploy Lambda to all regions"
for region in us-east-1 us-west-2 eu-west-1; do
echo "Deploying to $region..."
aws lambda update-function-code \
--function-name my-function \
--zip-file fileb://function.zip \
--region $region \
--publish
aws lambda wait function-active \
--function-name my-function \
--region $region
done
"Get running instances with specific tags as table"
aws ec2 describe-instances \
--query 'Reservations[].Instances[?State.Name==`running`].[InstanceId,Tags[?Key==`Name`].Value[0]|[0],PrivateIpAddress]' \
--output table
--output json for programmatic processing10. Enable MFA for security-sensitive operations
--max-throttle and exponential backoffaws service-quotas for current limits--max-results for consistency--no-paginate with jq for full dataset processingaws configure or environment variablesaws iam create-access-keyAssess 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-cli-beast 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.