AWS Identity and Access Management for users, roles, policies, and permissions. Use when creating IAM policies, configuring cross-account access, setting up service roles, troubleshooting permission errors, or managing access control.
npx skills add https://github.com/itsmostafa/aws-agent-skills --skill iam
AWS Identity and Access Management (IAM) enables secure access control to AWS services and resources. IAM is foundational to AWS security—every AWS API call is authenticated and authorized through IAM.
Entities that can make requests to AWS: IAM users, roles, federated users, and applications.
JSON documents defining permissions. Types:
Identities with permissions that can be assumed by trusted entities. No permanent credentials—uses temporary security tokens.
Define which principals can assume a role. Configured via the role's trust policy.
AWS CLI:
# Create the trust policy
cat > trust-policy.json << 'EOF'
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "Service": "lambda.amazonaws.com" },
"Action": "sts:AssumeRole"
}
]
}
EOF
# Create the role
aws iam create-role \
--role-name MyLambdaRole \
--assume-role-policy-document file://trust-policy.json
# Attach a managed policy
aws iam attach-role-policy \
--role-name MyLambdaRole \
--policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
boto3:
import boto3
import json
iam = boto3.client('iam')
trust_policy = {
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {"Service": "lambda.amazonaws.com"},
"Action": "sts:AssumeRole"
}
]
}
# Create role
iam.create_role(
RoleName='MyLambdaRole',
AssumeRolePolicyDocument=json.dumps(trust_policy)
)
# Attach managed policy
iam.attach_role_policy(
RoleName='MyLambdaRole',
PolicyArn='arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole'
)
cat > policy.json << 'EOF'
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"dynamodb:GetItem",
"dynamodb:PutItem",
"dynamodb:Query"
],
"Resource": "arn:aws:dynamodb:us-east-1:123456789012:table/MyTable"
}
]
}
EOF
aws iam create-policy \
--policy-name MyDynamoDBPolicy \
--policy-document file://policy.json
# In Account B (trusted account), create role with trust for Account A
cat > cross-account-trust.json << 'EOF'
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::111111111111:root" },
"Action": "sts:AssumeRole",
"Condition": {
"StringEquals": { "sts:ExternalId": "unique-external-id" }
}
}
]
}
EOF
# From Account A, assume the role
aws sts assume-role \
--role-arn arn:aws:iam::222222222222:role/CrossAccountRole \
--role-session-name MySession \
--external-id unique-external-id
| Command | Description |
|---------|-------------|
| aws iam create-role | Create a new IAM role |
| aws iam create-policy | Create a customer managed policy |
| aws iam attach-role-policy | Attach a managed policy to a role |
| aws iam put-role-policy | Add an inline policy to a role |
| aws iam get-role | Get role details |
| aws iam list-roles | List all roles |
| aws iam simulate-principal-policy | Test policy permissions |
| aws sts assume-role | Assume a role and get temporary credentials |
| aws sts get-caller-identity | Get current identity |
--query: Filter output with JMESPath--output table: Human-readable output--no-cli-pager: Disable pager for scripting${aws:username}) for dynamic policiesSymptom: AccessDeniedException or UnauthorizedAccess
Debug steps:
aws sts get-caller-identityaws iam list-attached-role-policies --role-name MyRole aws iam simulate-principal-policy \
--policy-source-arn arn:aws:iam::123456789012:role/MyRole \
--action-names dynamodb:GetItem \
--resource-arns arn:aws:dynamodb:us-east-1:123456789012:table/MyTable
Symptom: AccessDenied when calling AssumeRole
Causes:
sts:AssumeRole permission on the callerFix: Review and update the role's trust relationship.
Solution: Use multiple policies, reference resources by prefix/wildcard, or use tags-based access control.
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 itsmostafa/iam from the repository into ~/.claude/skills for personal
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same name cannot sit side by side — one of them will be ignored.