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Plan AWS architectures, compare services, and review security using documentation verified through AWS MCP tools, which connect agents to external information. Get implementation examples and trade-offs tailored to the team's existing tools, experience, and constraints.
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Configure MCP connections, which link AI agents to tools, for AWS documentation search and API access. Check existing settings, choose between documentation-only and full API access, verify available tools, and troubleshoot connection or credential problems.
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Check the AWS profile, region, account ID, and caller identity before starting AWS work. Read local configuration and validate credentials, then flag missing settings, expired access, or possible IAM restrictions associated with SSO authentication.
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Automate page interactions and screenshots with agent-browser running in AWS Bedrock AgentCore cloud browsers. Use standard browser commands, preserve login state across sessions with persistent profiles, and watch active sessions through an AWS Console link.
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Set up a Python project with AWS CDK, a toolkit for defining cloud infrastructure in code. Follow guidance on prerequisites, AWS credentials, virtual environments, dependencies, template generation, deployment preparation, and reviewing changes before deployment.
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Configure AWS Verified Access for VPN-free access to internal applications. Combine user identity and device security checks, write Cedar access policies, and use Terraform and AWS RAM to deploy and share access controls across AWS accounts.
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Audit AWS S3 buckets for public exposure, excessive permissions, and gaps in encryption or versioning settings. Use AWS CLI, Prowler, and access analysis to compile findings, prioritize issues, and plan or apply configuration fixes.
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Design AWS infrastructure around application needs, traffic, budget, and team experience. Generate templates that define infrastructure as code, compare service choices and estimated costs, and guide deployment, monitoring, and cost reduction.
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Ask plain-language questions about AWS resources, settings, and costs, and receive readable tables using read-only AWS CLI commands. Inspect services and filter results by region, state, or tags without creating, changing, or deleting resources.
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Set up and operate Amazon GuardDuty to detect threats in AWS. Configure protection features, centralize findings across accounts, investigate suspicious activity, and build automated responses with EventBridge and Lambda, including containment of potentially compromised resources.
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Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.
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Migrate workloads from Google Cloud Platform to AWS — plus AI and agentic workloads from any provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, migrate App Engine to Elastic Beanstalk, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform, app code, or billing exports, then clarify, design, estimate costs, generate artifacts, and collect feedback. Clarify must finish before Design, Estimate, or Generate. Do not use for: Azure migrations (see azure-to-aws), on-premises migrations, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, or GCP-to-GCP refactoring.
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AWS の構成図を draw.io の XML（.drawio）で描く。公式の AWS4 アイコンを使い、タイトルや凡例や番号バッジのような装飾を足さず、どのサービスがどこにあって何とつながっているかだけで読める図にする。AWS 構成図、アーキテクチャ図、インフラ構成図、ネットワーク構成図、VPC の図を描いてほしいとき、draw.io で AWS の図を作りたいとき、既存の構成図を直したいときは必ずこの Skill を使う。Use this whenever the user asks for an AWS architecture diagram, cloud infrastructure diagram, VPC diagram, or a draw.io file of AWS resources, even if they don't say "draw.io". Mermaid、PlantUML、Python の diagrams ライブラリなど、ほかの形式を指定されたときは使わない。Do not use it when the user explicitly asks for Mermaid, PlantUML, or the Python diagrams library.
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Analyze AWS spending by service, region, and resource tags to find unused resources and oversized configurations. Get specific cost reduction recommendations, estimated savings, and guidance for budgets and ongoing cost monitoring.
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Build and refactor AWS infrastructure with TypeScript or Python using AWS CDK, a toolkit for defining cloud resources in code. Get guidance on resource selection, Lambda packaging, stack organization, and checks before deployment.
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Scan code, configuration files, and Git history for exposed AWS credentials with TruffleHog. Checks whether detected keys remain active and guides key replacement, incident investigation, and automated checks before commits and during CI/CD.
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Plan, deploy, and manage AWS infrastructure across computing, storage, networking, and serverless services. Get guidance on access permissions, backups, auditing, and cost management using AWS CLI and tools that define infrastructure in code.
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Migrates vibe-coded web applications to AWS. Handles the full workflow from analysis through migration to deployment, producing deployable AWS Blocks infrastructure code. Supports full-stack apps built with vibe-coding platforms (Lovable, Bolt.new, Replit) and frontend web applications and websites: React, Vue, Angular, Next.js, Nuxt, Astro, SvelteKit, Gatsby, Vite, Svelte, Solid, Docusaurus, and others (static sites, SPAs, and SSR frameworks with static export). Triggers on: launch with AWS, launch on AWS, deploy to AWS, migrate to AWS, host my app on AWS, move my app to AWS, transfer my app to AWS. Activates when the user wants to migrate a vibe-coded app or frontend web app to AWS, even if they don't say 'migrate' explicitly.
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Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
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Plan AWS infrastructure changes by checking application requirements and existing configuration, then selecting guidance from an external skill collection. Produces a reviewable plan covering access permissions, networking, cost assumptions, validation, and rollback.
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Review AWS infrastructure code and deployed resources against the six Well-Architected pillars. Produce risk-ranked findings, remediation examples, and an architecture diagram, then create GitHub issues for tracking improvements after user confirmation.
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Use CloudFox to inspect AWS and Azure resources through read-only queries. Identify exposed endpoints, secrets, permissions, and role trust relationships, then organize findings into files for reviewing configuration weaknesses and potential paths to broader access.
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Analyze AWS CloudTrail logs against normal activity patterns to spot unusual API calls, IP addresses, errors, and sensitive changes. Produce a JSON report with anomaly scores, suspicious users, and suggested investigation steps.
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Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.
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