AWS Labs Skills

245 skills · showing 61–120 · open in filtered browse

Cao Session ManagementInteract with CAO (CLI Agent Orchestrator) — launch multi-agent sessions, check status, send follow-up instructions, unblock stuck terminals, or shut down sessions. Use when working with CAO sessions in any capacity.awslabs/cli-agent-orchestrator1,374
Cao Supervisor ProtocolsSupervisor-side orchestration patterns for assign, handoff, and idle inbox delivery in CAOawslabs/cli-agent-orchestrator1,374
Cao Supervisor ProtocolsSupervisor-side orchestration patterns for assign, handoff, and idle inbox delivery in CAOawslabs/cli-agent-orchestrator1,374
Cao Supervisor ProtocolsSupervisor-side orchestration patterns for assign, handoff, and idle inbox delivery in CAOawslabs/cli-agent-orchestrator1,374
Cao Worker ProtocolsWorker-side callback and completion rules for assigned and handed-off tasks in CAOawslabs/cli-agent-orchestrator1,374
Cao Worker ProtocolsWorker-side callback and completion rules for assigned and handed-off tasks in CAOawslabs/cli-agent-orchestrator1,374
Cao Worker ProtocolsWorker-side callback and completion rules for assigned and handed-off tasks in CAOawslabs/cli-agent-orchestrator1,374
Cao WorkflowAuthor and run CAO Python workflow scripts — multi-step, parameterized, fan-out orchestrations executed by `cao workflow run`. Use when the user wants a repeatable multi-step job (e.g. data analysis over many files, a review pipeline, a parameterized batch). Authoring ends at a validated script file; running it is a separate, user-approved step.awslabs/cli-agent-orchestrator1,374
Cao WorkflowAuthor and run CAO Python workflow scripts — multi-step, parameterized, fan-out orchestrations executed by `cao workflow run`. Use when the user wants a repeatable multi-step job (e.g. data analysis over many files, a review pipeline, a parameterized batch). Authoring ends at a validated script file; running it is a separate, user-approved step.awslabs/cli-agent-orchestrator1,374
Convert Web AppThis skill should be used when the user asks to "add MCP App support to my web app", "turn my web app into a hybrid MCP App", "make my web page work as an MCP App too", "wrap my existing UI as an MCP App", "convert iframe embed to MCP App", "turn my SPA into an MCP App", or needs to add MCP App support to an existing web application while keeping it working standalone. Provides guidance for analyzing existing web apps and creating a hybrid web + MCP App with server-side tool and resource registrawslabs/cli-agent-orchestrator1,374
Create Mcp AppThis skill should be used when the user asks to "create an MCP App", "add a UI to an MCP tool", "build an interactive MCP View", "scaffold an MCP App", or needs guidance on MCP Apps SDK patterns, UI-resource registration, MCP App lifecycle, or host integration. Provides comprehensive guidance for building MCP Apps with interactive UIs.awslabs/cli-agent-orchestrator1,374
Mcp Apps BuilderLoad the official MCP Apps builder skills (create-mcp-app, migrate-oai-app, add-app-to-server, convert-web-app) from github.com/modelcontextprotocol/ext-apps. Use when the user says "build an MCP App", "add a ui:// view to my MCP server", "create an interactive MCP App from scratch", "migrate from OpenAI Apps SDK to MCP", or "turn my web app into a hybrid MCP App". Targets the MCP Apps SDK (SEP-1865 ui:// resources) specifically. Not for regular web frontends, REST APIs, CAO provider adapters, Sawslabs/cli-agent-orchestrator1,374
Mcp Apps BuilderLoad the official MCP Apps builder skills (create-mcp-app, migrate-oai-app, add-app-to-server, convert-web-app) from github.com/modelcontextprotocol/ext-apps. Use when the user says "build an MCP App", "add a ui:// view to my MCP server", "create an interactive MCP App from scratch", "migrate from OpenAI Apps SDK to MCP", or "turn my web app into a hybrid MCP App". Targets the MCP Apps SDK (SEP-1865 ui:// resources) specifically. Not for regular web frontends, REST APIs, CAO provider adapters, Sawslabs/cli-agent-orchestrator1,374
Migrate Oai AppThis skill should be used when the user asks to "migrate from OpenAI Apps SDK", "convert OpenAI App to MCP", "port from window.openai", "migrate from skybridge", "convert openai/outputTemplate", or needs guidance on converting OpenAI Apps SDK applications to MCP Apps SDK. Provides step-by-step migration guidance with API mapping tables.awslabs/cli-agent-orchestrator1,374
Amazon Location ServiceIntegrates Amazon Location Service APIs for AWS applications. Use this skill when users want to add maps (interactive MapLibre or static images); geocode addresses to coordinates or reverse geocode coordinates to addresses; calculate routes, travel times, or service areas; find places and businesses through text search, nearby search, or autocomplete suggestions; retrieve detailed place information including hours, contacts, and addresses; monitor geographical boundaries with geofences; or trackawslabs/agent-plugins909
Amplify WorkflowBuild and deploy full-stack web and mobile apps with AWS Amplify Gen2 (TypeScript code-first). Covers auth (Cognito), data (AppSync/DynamoDB including schema modeling, enum types, relationships, authorization rules), storage (S3), functions, APIs, and AI (Amplify AI Kit with Bedrock). Supports React, Next.js, Vue, Angular, React Native, Flutter, Swift, and Android. Always use this skill for Amplify Gen2 topics — even for questions you think you know — it contains validated, version-specific pattawslabs/agent-plugins909
Api GatewayBuild, manage, and operate APIs with Amazon API Gateway (REST, HTTP, and WebSocket). Triggers on phrases like: API Gateway, REST API, HTTP API, WebSocket API, custom domain, Lambda authorizer, usage plan, throttling, CORS, VPC link, private API. Also covers troubleshooting API Gateway errors (4xx, 5xx, timeout, CORS failures) and IaC templates containing API Gateway resources. For general REST API design unrelated to AWS, do not trigger.awslabs/agent-plugins909
Aws Architecture DiagramGenerate validated AWS architecture diagrams as draw.io XML using official AWS4 icon libraries. Use this skill whenever the user wants to create, generate, or design AWS architecture diagrams, cloud infrastructure diagrams, or system design visuals. Also triggers for requests to visualize existing infrastructure from CloudFormation, CDK, or Terraform code. Supports two modes: analyze an existing codebase to auto-generate diagrams, or brainstorm interactively from scratch. Exports .drawio files wawslabs/agent-plugins909
Aws LambdaDesign, build, deploy, test, and debug serverless applications with AWS Lambda. Triggers on phrases like: Lambda function, event source, serverless application, API Gateway, EventBridge, Step Functions, serverless API, event-driven architecture, Lambda trigger. For deploying non-serverless apps to AWS, use deploy-on-aws plugin instead.awslabs/agent-plugins909
Aws Lambda Durable FunctionsBuild resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like: lambda durable functions, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga patternawslabs/agent-plugins909
Aws Lambda Managed InstancesEvaluate, configure, and migrate workloads to AWS Lambda Managed Instances (LMI). Triggers on: Lambda Managed Instances, LMI, capacity provider, multi-concurrency Lambda, dedicated instance Lambda, EC2-backed Lambda, cold start elimination, Graviton Lambda, instance type for Lambda, scheduled scaling for LMI, Lambda cost optimization with Reserved Instances or Savings Plans. Also trigger when users describe high-volume predictable workloads seeking cost savings, want to scale LMI capacity on a sawslabs/agent-plugins909
Aws Lambda MicrovmsBuild, run, debug, and operate applications on AWS Lambda MicroVMs — Firecracker-isolated, snapshot-resumable serverless compute environments that run inside a container with up to 8-hour lifetimes. Triggers on: Lambda MicroVMs, Firecracker isolation, snapshot-resumable compute, suspend/resume, sandboxed or untrusted code execution, AI/agent code-execution sandboxes, interactive code playgrounds and notebooks (Jupyter, REPLs), reinforcement-learning environments, multi-tenant CI executors and buawslabs/agent-plugins909
Aws Serverless DeploymentAWS SAM and AWS CDK deployment for serverless applications. Triggers on phrases like: use SAM, SAM template, SAM init, SAM deploy, CDK serverless, CDK Lambda construct, NodejsFunction, PythonFunction, SAM and CDK together, serverless CI/CD pipeline. For general app deployment with service selection, use deploy-on-aws plugin instead.awslabs/agent-plugins909
Aws Step FunctionsBuild workflows with AWS Step Functions state machines using the JSONata query language. Covers Amazon States Language (ASL) structure, state types, variables, data transformation, error handling, AWS service integration, and migrating from the JSONPath to the JSONata query language.awslabs/agent-plugins909
Aws TransformMigrate, modernize, and upgrade codebases to AWS. Run analysis on repos for tech debt, security vulnerabilities, and modernization opportunities. Transforms .NET Framework to .NET 8/10, mainframe COBOL to Java, VMware VMs to EC2, SQL Server to Aurora, and upgrades Java/Python/Node.js versions and AWS SDKs. Use when the user says "migrate .NET to AWS", "upgrade Java to 17/21", "modernize COBOL", "modernize mainframe", "move VMware to EC2", "convert SQL Server to Aurora", "upgrade Python version",awslabs/agent-plugins909
Dataset EvaluationValidates dataset formatting and quality for SageMaker model fine-tuning (SFT, DPO, or RLVR). Use when the user says "is my dataset okay", "evaluate my data", "check my training data", "I have my own data", or before starting any fine-tuning job. Detects file format, checks schema compliance against the selected model and technique, and reports whether the data is ready for training or evaluation.awslabs/agent-plugins909
Dataset TransformationGenerates code that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR/RLAIF, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.awslabs/agent-plugins909
DeployDeploy applications to AWS. Triggers on phrases like: deploy to AWS, host on AWS, run this on AWS, AWS architecture, estimate AWS cost, generate infrastructure. Analyzes any codebase and deploys to optimal AWS services.awslabs/agent-plugins909
Directory ManagementManages project directory setup and artifact organization. Use when starting a new project, resuming an existing one, or when a PLAN.md needs to be associated with a project directory. Creates the project folder structure (specs/, scripts/, notebooks/, manifests/, agent_memory/) and resolves project naming.awslabs/agent-plugins909
Document ServiceThis skill should be used when the user asks to "analyze this codebase", "document this service", "generate technical docs", "I inherited this code", "help me understand this system", "create docs for this project", "what does this system look like", "onboard me to this codebase", "this codebase has no docs", "visualize the architecture from code", or any explicit request to produce structured documentation or architecture diagrams from an existing codebase. Specifically optimized for AWS workloawslabs/agent-plugins909
DsqlBuild with Aurora DSQL — manage schemas, execute queries, handle migrations, diagnose query plans, diagnose cluster performance, load data, and develop applications with a serverless, distributed SQL database. Covers IAM auth, multi-tenant patterns, MySQL-to-DSQL and PostgreSQL-to-DSQL schema conversion, foreign key constraints, OCC retry patterns, ORM migration (Django/EF Core/Hibernate/Rails/SQLAlchemy), DDL operations, query plan explainability, system diagnostics via CloudWatch AAS, SQL compawslabs/agent-plugins909
Elastic BeanstalkDeploy to AWS Elastic Beanstalk. Triggers on: elastic beanstalk, EB, managed EC2 platform, web app with managed patching, worker on EC2, Heroku alternative, don't want to manage servers or container orchestration, migrate from Heroku, managed operational lifecycle. Covers Elastic Beanstalk on EC2 for web and worker applications.awslabs/agent-plugins909
FinetuningGenerates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt creation.awslabs/agent-plugins909
Finetuning TechniqueSelects a fine-tuning technique (SFT, DPO, RLVR, or RLAIF) for the user's use case and validates it against the selected model's available recipes. Use when the user has decided to finetune and needs to choose a technique, or when the technique needs to be validated against a model. Requires a base model to already be selected (via model-selection skill).awslabs/agent-plugins909
Hyperpod Cluster DebuggerDiagnose and remediate cluster-wide HyperPod (EKS or Slurm) problems — creation / deployment failures (CloudFormation, EFA health check, lifecycle scripts, capacity), EKS access, node replacement, CloudFormation nested-stack errors, post-maintenance rollback state, dangling nodes, autoscaler conflicts. Includes `--validate` pre-flight. Read-only.awslabs/agent-plugins909
Hyperpod Issue ReportGenerate comprehensive issue reports from HyperPod clusters (EKS and Slurm) by collecting diagnostic logs and configurations for troubleshooting and AWS Support cases. Use when users need to collect diagnostics from HyperPod cluster nodes, generate issue reports for AWS Support, investigate node failures or performance problems, document cluster state, or create diagnostic snapshots. Triggers on requests involving issue reports, diagnostic collection, support case preparation, or cluster troubleawslabs/agent-plugins909
Hyperpod NcclDiagnose NCCL failures and adjacent training-pod failures on HyperPod GPU clusters (EKS or Slurm) — training hangs, AllReduce / collective-op timeouts, EFA or libfabric errors, rendezvous failures, EFA TCP fallback, /dev/shm or memlock issues, NCCL version mismatch across pods, container OOM / exit-137 / OOMKilled, GPU OOM (CUDA out of memory), CrashLoopBackOff / Pending pods, MASTER_ADDR DNS, NetworkPolicy blocking. Not for single-node hardware faults (→ hyperpod-node-debugger § G) or cluster-cawslabs/agent-plugins909
Hyperpod Node DebuggerDiagnose and remediate per-node issues on a HyperPod cluster (EKS or Slurm) — a specific node is unhealthy, unresponsive, stuck, or needs replacing. Covers on-node EFA, GPU / accelerator hardware (XID, ECC, NVLink, row-remap, DCGM), Slurm node down/drained, disk and memory pressure, per-node lifecycle-script failures, SSM agent, container runtime, kernel panics, pod networking. Read-only. Not for cluster-wide provisioning (→ hyperpod-cluster-debugger), NCCL (→ hyperpod-nccl), or MFU (→ hyperpod-awslabs/agent-plugins909
Hyperpod Performance DebuggerDiagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput. Read-only. Surfaces host-side signals (Xid, ECC, NVLink, EFA reachability, FSx saturation) and routes to the appropriate sibling skill (hyperpod-node-debugger, hyperpod-nccl, hyperpod-version-checker, hyperpod-issue-report) for any remediation. Triggers on uneven NCCL across nodes, straggler node, FSx slow, checkpoint slow, dataloader slow, filesystem bottleneck,awslabs/agent-plugins909
Hyperpod Slurm DebuggerDiagnostic-only skill for Slurm scheduler and node-daemon issues on Amazon SageMaker HyperPod Slurm clusters. Scope mirrors the HyperPod troubleshooting guide. Invoke when the user reports a Slurm node stuck in down/drain, "Node unexpectedly rebooted" after auto-repair, slurmd not running, jobs stuck PENDING with REASON=Resources while sinfo shows idle nodes, jobs stuck COMPLETING after node replacement, GRES/GPU counts wrong, scontrol ping failing, slurmctld unresponsive, an Action:Reboot/Replaawslabs/agent-plugins909
Hyperpod SsmRemote 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 alawslabs/agent-plugins909
Hyperpod Version CheckerCheck and compare software component versions on SageMaker HyperPod cluster nodes - NVIDIA drivers, CUDA toolkit, cuDNN, NCCL, EFA, AWS OFI NCCL, GDRCopy, MPI, Neuron SDK (Trainium/Inferentia), Python, and PyTorch. Use when checking component versions, verifying CUDA/driver compatibility, detecting version mismatches across nodes, planning upgrades, documenting cluster configuration, or troubleshooting version-related issues on HyperPod. Triggers on requests about versions, compatibility, componawslabs/agent-plugins909
Model DeploymentGenerates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.awslabs/agent-plugins909
Model EvaluationGenerates python code that evaluates SageMaker models. Supports two evaluation types: LLM-as-Judge and Custom Scorer. Use when the user says "evaluate my model", "run a benchmark", "test model performance", "how did my model perform", "compare models", or other similar requests.awslabs/agent-plugins909
Model SelectionSelects a base model for the user's use case by querying SageMaker Hub. Use when the user asks which model to use, wants to select or change their base model, mentions a model name or family (e.g., "Llama", "Mistral", "Nova"), or wants to evaluate a base model — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, presents benchmarks and licenses, and confirms selection.awslabs/agent-plugins909
PlanningDiscovers user intent and generates a structured, step-by-step plan for model customization workflows. This skill must always be activated alongside any other skill when the user's request relates to model customization — including fine-tuning, training, building, customizing, reviewing data, or getting advice on approach, regardless of domain. Do not skip this skill even if the immediate ask is narrow (e.g., reviewing data format or a single workflow step), because planning discovers the full sawslabs/agent-plugins909
Sdk Getting StartedValidates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role. Use when the user says "set up", "getting started", "check my environment", "configure SDK", or as the first step in any plan involving SageMaker/Bedrock training, evaluation, or deployment.awslabs/agent-plugins909
Use Case SpecificationCreates a reusable use case specification file that defines the business problem, stakeholders, and measurable success criteria for model customization, as recommended by the AWS Responsible AI Lens. Use as the default first step in any model customization plan. Skip only if the user explicitly declines or already has a use case specification to reuse. Captures problem statement, primary users, and LLM-as-a-Judge success tenets.awslabs/agent-plugins909
Ash McpRun security scans with the ASH (Automated Security Helper) MCP server. Use this skill whenever the user asks to scan for vulnerabilities, run a security check, find CVEs, audit dependencies, check for secrets, run SAST or SCA, scan IaC (Terraform/CloudFormation/Kubernetes), check for hardcoded credentials, or mentions ASH, Bandit, Semgrep, Checkov, Grype, Syft, or detect-secrets. Also trigger when the user wants to find security issues, harden a codebase, or asks "is my code secure". Do NOT triawslabs/automated-security-helper695
Ash McpRun security scans with the ASH (Automated Security Helper) MCP server. Use this skill whenever the user asks to scan for vulnerabilities, run a security check, find CVEs, audit dependencies, check for secrets, run SAST or SCA, scan IaC (Terraform/CloudFormation/Kubernetes), check for hardcoded credentials, or mentions ASH, Bandit, Semgrep, Checkov, Grype, Syft, or detect-secrets. Also trigger when the user wants to find security issues, harden a codebase, or asks "is my code secure". Do NOT triawslabs/automated-security-helper695
Ash McpRun security scans with the ASH (Automated Security Helper) MCP server. Use this skill whenever the user asks to scan for vulnerabilities, run a security check, find CVEs, audit dependencies, check for secrets, run SAST or SCA, scan IaC (Terraform/CloudFormation/Kubernetes), check for hardcoded credentials, or mentions ASH, Bandit, Semgrep, Checkov, Grype, Syft, or detect-secrets. Also trigger when the user wants to find security issues, harden a codebase, or asks "is my code secure". Do NOT triawslabs/automated-security-helper695
Ash McpRun security scans with the ASH (Automated Security Helper) MCP server. Use this skill whenever the user asks to scan for vulnerabilities, run a security check, find CVEs, audit dependencies, check for secrets, run SAST or SCA, scan IaC (Terraform/CloudFormation/Kubernetes), check for hardcoded credentials, or mentions ASH, Bandit, Semgrep, Checkov, Grype, Syft, or detect-secrets. Also trigger when the user wants to find security issues, harden a codebase, or asks "is my code secure". Do NOT triawslabs/automated-security-helper695
Ash McpRun security scans with the ASH (Automated Security Helper) MCP server. Use this skill whenever the user asks to scan for vulnerabilities, run a security check, find CVEs, audit dependencies, check for secrets, run SAST or SCA, scan IaC (Terraform/CloudFormation/Kubernetes), check for hardcoded credentials, or mentions ASH, Bandit, Semgrep, Checkov, Grype, Syft, or detect-secrets. Also trigger when the user wants to find security issues, harden a codebase, or asks "is my code secure". Do NOT triawslabs/automated-security-helper695
Bash TestingPatterns for unit testing bash scripts using bats-core, including AWS CLI mocking, jq/sed/awk testing, and cross-platform portabilityawslabs/awsome-distributed-ai483
Build Slurm ImageBuild the Slurm compute node container image via CodeBuild or local Docker, generate SSH keys, and render Helm values using setup.shawslabs/awsome-distributed-ai483
Deploy InfrastructureDeploy HyperPod EKS infrastructure using deploy.sh via CloudFormation or Terraform, including AZ resolution, stack outputs, and kubeconfig setupawslabs/awsome-distributed-ai483
Deploy Slurm ClusterInstall cert-manager, AWS LB Controller (Pod Identity), MariaDB, Slurm operator, and Slurm cluster on HyperPod EKS using install.sh, including subnet tagging and NLB configuration for SSH accessawslabs/awsome-distributed-ai483
Deployment PreflightValidate all prerequisites (CLI tools, AWS credentials, environment variables, kubectl context) before running slinky-slurm deployment scriptsawslabs/awsome-distributed-ai483
Hyperpod Devops Agent SolutionHow this Agent Space monitors SageMaker HyperPod — the design and intent of the HyperPod x DevOps Agent solution (event-driven webhook bridge, Lambda-gated periodic audit, triage/RCA skills, email notifications). Read this to understand WHY a HyperPod investigation was created and what the monitoring pipeline does. For the concrete resource/topology map (ARNs, IDs, log groups), see the understanding-agent-space skill instead.awslabs/awsome-distributed-ai483
Hyperpod Incident RcaRoot-cause analysis for a SageMaker HyperPod incident, after triage has decided to PROCEED. Runs at the INCIDENT_RCA stage. Reads describe-cluster, list-cluster-nodes, list-cluster-events, and HMA CloudWatch streams; reconstructs a timeline; classifies as Suppress / Monitor / Escalate / Resolved against time budgets and recurrence statistics from the HyperPod mental model. Produces a human-readable verdict report with recommended operator actions. The complementary INCIDENT_TRIAGE skill `hyperpoawslabs/awsome-distributed-ai483