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Found 651 Skills
This skill should be used when the user requests to add a new third-party API service to the AWS billing/quota monitoring system. It handles the complete onboarding process including adapter creation, Lambda deployment, CloudWatch alarms, Dashboard updates, and verification. Triggers on requests mentioning "add service monitoring", "monitor API balance", "setup quota alerts", "add to billing dashboard", or similar service integration requests.
Cloud CLI patterns for GCP and AWS. Use when running bq queries, gcloud commands, aws commands, or making decisions about cloud services. Covers BigQuery cost optimization and operational best practices.
Implement comprehensive cloud security across AWS, Azure, and GCP with IAM, encryption, network security, compliance, and threat detection.
AWS, GCP, Azure services and cloud-native development
Convert an AWS CloudFormation stack or template to Pulumi. This skill MUST be loaded whenever a user requests migration or conversion of CloudFormation to Pulumi.
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.
Optimize cloud storage across AWS S3, Azure Blob, and GCP Cloud Storage with compression, partitioning, lifecycle policies, and cost management.
Configure and deploy load balancers (HAProxy, AWS ELB/ALB/NLB) for distributing traffic, session management, and high availability.
Expert cloud architecture covering AWS, GCP, Azure, multi-cloud strategy, cost optimization, and cloud-native design.
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.
Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.
Use when architecting OCI solutions, migrating from AWS/Azure, designing multi-AD deployments, or avoiding common OCI anti-patterns. Covers VCN sizing mistakes, Cloud Guard gotchas, free tier specifics, OCI terminology confusion, and multi-AD patterns.