Provides AWS CloudFormation patterns for IAM roles, policies, managed policies, permission boundaries, and trust relationships. Use when modeling least-privilege access, cross-account assumptions, service roles, or reusable IAM stacks that other CloudFormation templates consume.
npx skills add https://github.com/giuseppe-trisciuoglio/developer-kit --skill aws-cloudformation-iam
Use this skill to model IAM with CloudFormation in a way that stays secure, auditable, and maintainable.
The most important design concerns are:
Do not treat SKILL.md as a full IAM encyclopedia. Use the bundled references for larger policy examples and service-specific variants.
Identify who or what assumes the role (service principal, cross-account principal, or federated identity), then write the trust policy with explicit principals and conditions before adding permissions.
Use inline policies for role-specific access; use managed policies for shared patterns across principals. Scope actions and resources tightly, and use conditions where possible.
Use permission boundaries when teams create or extend roles in their own stacks, when guardrails are needed around privileged services (IAM, KMS, Organizations), or to separate maximum allowed permissions from application-specific policies.
Name roles and policies consistently so stack outputs and audits remain easy to trace.
For cross-account roles: trust only the exact source account or principal, add sts:ExternalId conditions when appropriate, keep permission and trust policies separate, and export only the ARNs that consuming accounts need.
Before rollout, use these commands to verify the template and IAM behavior:
# Validate CloudFormation template syntax
aws cloudformation validate-template --template-body file://template.yaml
# Preview changes before applying
aws cloudformation create-change-set \
--stack-name <stack-name> \
--template-body file://template.yaml \
--change-set-type CREATE
# Simulate whether a principal can perform specific actions
aws iam simulate-principal-policy \
--policy-source-arn arn:aws:iam::123456789012:role/LambdaExecutionRole \
--action-names dynamodb:GetItem dynamodb:PutItem
# Check for wildcards in IAM policies within the template
aws cloudformation list-stack-resources --stack-name <stack-name>
After deployment, confirm policy attachments and stack outputs match the intended security model.
Resources:
LambdaExecutionRole:
Type: AWS::IAM::Role
Properties:
AssumeRolePolicyDocument:
Version: "2012-10-17"
Statement:
- Effect: Allow
Principal:
Service: lambda.amazonaws.com
Action: sts:AssumeRole
Policies:
- PolicyName: DynamoDbWritePolicy
PolicyDocument:
Version: "2012-10-17"
Statement:
- Effect: Allow
Action:
- dynamodb:GetItem
- dynamodb:PutItem
Resource: !GetAtt OrdersTable.Arn
Resources:
PartnerReadRole:
Type: AWS::IAM::Role
Properties:
AssumeRolePolicyDocument:
Version: "2012-10-17"
Statement:
- Effect: Allow
Principal:
AWS: arn:aws:iam::123456789012:role/partner-reader
Action: sts:AssumeRole
Condition:
StringEquals:
sts:ExternalId: partner-contract-001
Keep the trust relationship narrow and pair it with a separate read-only permission policy.
references/ instead of bloating the root skill.references/examples.mdreferences/reference.mdaws-cloudformation-securityaws-cloudformation-ec2aws-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-iam 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.