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Found 385 Skills
Remote command execution and file transfer on SageMaker HyperPod cluster nodes via AWS Systems Manager (SSM). This is the primary interface for accessing HyperPod nodes — direct SSH is not available. Use when any skill, workflow, or user request needs to execute commands on cluster nodes, upload files to nodes, read/download files from nodes, run diagnostics, install packages, or perform any operation requiring shell access to HyperPod instances. Other HyperPod skills depend on this skill for all node-level operations.
Use when the user asks about chaos engineering, fault injection, resilience testing, or HA verification for a SPECIFIC AWS service (e.g., RDS, EKS, MSK, ElastiCache, DynamoDB, S3, Lambda, OpenSearch, etc.). Triggers on "chaos testing on [service]", "fault injection for [service]", "how to test HA of [service]", "FIS scenarios/actions for [service]", "[service] failover testing", "[service] resilience testing", "[service] 混沌测试", "[service] 故障注入", "[service] 高可用验证", "对 [service] 做混沌实验", "test my [service]", "verify my [service] is resilient". Use this skill even when the user phrases it casually like "test my RDS" or "how resilient is my MSK cluster".
This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.
Grafana Cloud private network connectivity — AWS PrivateLink, Azure Private Link, and GCP Private Service Connect. Send telemetry (metrics, logs, traces, profiles) to Grafana Cloud without traversing the public internet. Eliminates cloud egress costs, meets compliance requirements (PCI-DSS, HIPAA). Use when setting up secure private telemetry ingestion from AWS/Azure/GCP, reducing egress costs, or meeting data residency/compliance requirements.
AWS SDK for Python (boto3/botocore) development patterns. You MUST use this skill when writing Python code that uses AWS services via boto3 or botocore. This includes creating service clients or resources, configuring sessions and credentials, handling errors with ClientError, using paginators and waiters, S3 file transfers and presigned URLs, DynamoDB table operations, and any boto3/botocore client configuration. Use this skill whenever Python code imports boto3 or botocore, or when the user asks about AWS operations in Python.
Troubleshoots and debugs AWS Clean Rooms collaboration issues related to IAM roles, S3 bucket policies, KMS keys, Lake Formation permissions, and CloudWatch logging for custom ML model training and inference jobs. Use when a customer reports permission failures, access errors, or log publishing issues in Clean Rooms.
Infrastructure-as-Code patterns for data engineering using Terraform to provision AWS resources (S3, EC2, IAM)
Selecting and implementing AWS services and architectural patterns. Use when designing AWS cloud architectures, choosing compute/storage/database services, implementing serverless or container patterns, or applying AWS Well-Architected Framework principles.
Monitor and manage R2 to AWS Glacier Deep Archive migration. Use when checking transfer status, resuming transfers, or managing the archive migration.
AWS, Azure, and GCP cloud services and best practices
Use when implementing secrets management, using Vault, AWS Secrets Manager, handling credentials in CI/CD, or asking about "secrets", "Vault", "credentials", "secret rotation", "API keys", "external secrets operator"
Build and run LLM-as-judge evaluation pipelines using Amazon Bedrock Evaluation Jobs with pre-computed inference datasets. Use when setting up automated model evaluation, designing test scenarios, collecting pre-computed responses, configuring custom metrics, creating AWS infrastructure, running evaluation jobs, parsing results, and iterating on findings.