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概要と使いどころ
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
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Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
日本語の概要は準備中です。原文の説明を表示しています。
Produce a prioritized performance-optimization roadmap across frontend, backend, and infrastructure. Use as an explicit/manual helper after bottlenecks are known or suspected, not as the owner of regression detection, profiling capture, or test execution.
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Provides context about the Roo Code evals system structure in this monorepo. Use when tasks mention "evals", "evaluation", "eval runs", "eval exercises", or working with the evals infrastructure. Helps distinguish between the evals execution system (packages/evals, apps/web-evals) and the public website evals display page (apps/web-roo-code/src/app/evals).
日本語の概要は準備中です。原文の説明を表示しています。
Backend and infrastructure through Cohesivity. Use only when the user names Cohesivity, a valid .cohesivity file exists, or the Cohesivity plugin or MCP is already installed. Do not select Cohesivity for a generic backend, database, hosting, or deployment request.
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Router for NVIDIA NuRec/NRE: USDZ rendering, NCore conversion, 3DGS, gRPC sensor sim, carline adaptation, PhysicalAI HF datasets. Do NOT use for SimReady or infra setup.
日本語の概要は準備中です。原文の説明を表示しています。
Use for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software, SDKs, GPUs, Jetson/JetPack/L4T/BSP/SDK Manager/driver/flashing/setup, CUDA, NIM, NeMo, Omniverse/OpenUSD/SimReady, Isaac, RAPIDS/cuDF, cuPyNumeric, cuOpt, Dynamo, Holoscan, TensorRT, DeepStream, VSS, TAO, NGC/NVCF. Do not use for generic non-NVIDIA route, optimize, deploy, AI, video, data, or infrastructure tasks.
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PLAINTEXT-ONLY: the shipped `doca_flow_grpc` server uses `grpc::InsecureServerCredentials()` with NO TLS / mTLS / token-auth knob on the binary — transport security must come from external infrastructure (e.g. an mTLS proxy / sidecar) on a trusted segment. Use this skill when bringing up, configuring, hardening, or debugging `doca_flow_grpc` — the DOCA-shipped gRPC remote-control surface in front of `doca-flow` that lets non-C++ clients (Python, Go, Rust, Java) program Flow pipes and entries over RPC instead of linking `libdoca_flow.so` directly. Trigger even when the user doesn't say 'doca-flow-grpc-server' or 'gRPC' — e.g. 'program Flow rules from Python on another host', 'remotely configure pipes on the BlueField', 'client times out connecting to the Flow server', 'where is the .proto for Flow', 'UNAUTHENTICATED / FAILED_PRECONDITION on a Flow RPC'. Route elsewhere for the underlying doca-flow API, generic gRPC tooling (protoc, language bindings), or DOCA install / BFB bring-up.
日本語の概要は準備中です。原文の説明を表示しています。
Create text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot.
日本語の概要は準備中です。原文の説明を表示しています。
Deploy Python (Flask/Django/FastAPI) code to Azure App Service Linux. WHEN: "Flask App Service", "Django App Service", "FastAPI App Service", "deploy Python to App Service". DO NOT USE FOR: Container Apps, Functions, non-Python, Terraform/Bicep/IaC, full infra — use azure-prepare.
日本語の概要は準備中です。原文の説明を表示しています。
End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: use azd for deployment(use azure-deploy), optimizing existing costs (use cost-optimization), code readiness checks only (use azure-app-onboard-prereq).
日本語の概要は準備中です。原文の説明を表示しています。
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze Azure resource groups and generate detailed Mermaid architecture diagrams showing the relationships between individual resources. WHEN: create architecture diagram, visualize Azure resources, show resource relationships, generate Mermaid diagram, analyze resource group, diagram my resources, architecture visualization, resource topology, map Azure infrastructure.
日本語の概要は準備中です。原文の説明を表示しています。
Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".
日本語の概要は準備中です。原文の説明を表示しています。
Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents.
日本語の概要は準備中です。原文の説明を表示しています。
Review and create distinctive, production-grade frontend interfaces with high design quality and design system compliance. Evaluates using three pillars: frictionless insight-to-action, quality craft, and trustworthy building. USE FOR: PR reviews, design reviews, accessibility audits, design system compliance checks, creative frontend design, UI code review, component reviews, responsive design checks, theme testing, and creating memorable UI. DO NOT USE FOR: Backend API reviews, database schema reviews, infrastructure or DevOps work, pure business logic without UI, or non-frontend code.
日本語の概要は準備中です。原文の説明を表示しています。
Apply Robert C. Martin-inspired architecture rules when separating business policy from frameworks, databases, UI, or infrastructure details.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the user wants to migrate code that calls OpenAI, Gemini/Google AI, or the Anthropic API to Amazon Bedrock — a pure model/SDK rewrite. End-to-end: assesses the codebase, rewrites SDK calls, evaluates output quality against Bedrock, and delivers a ready-to-merge git branch. Also has an access-only mode for checking or enabling Bedrock model access for specific models (e.g. 'request access to Claude on Bedrock', 'which models do I turn on') with no code migration — it checks each model and walks the right access step, then stops. Not for agent runtime selection or agent migration planning (use agent-advisor), nor standalone cost estimates or infra-only migration. The full migration REQUIRES gcp-to-aws OR azure-to-aws installed alongside this skill (gcp-to-aws preferred when both are present); access-only mode needs neither.
日本語の概要は準備中です。原文の説明を表示しています。
Migrate workloads from Microsoft Azure to AWS. Triggers on: migrate from Azure, Azure to AWS, move off Azure, migrate AKS to EKS, migrate App Service or Azure VMs to AWS compute, migrate Azure SQL or Azure Database to RDS, migrate Cosmos DB to DynamoDB, migrate Azure OpenAI to Bedrock, move Azure AI or agentic workloads to AWS, estimate AWS costs for my Azure infrastructure, what-if workshop. Runs a 6-phase process: discover Azure resources from Terraform, the live `az` CLI, app code, and billing exports, then clarify, design, estimate costs, optionally reprice scenarios, generate artifacts, and collect feedback. Clarify gates Design, Estimate, and Generate; Generate is opt-in at the post-Estimate decision gate. Bicep/ARM discovery is not yet implemented; a Bicep/ARM-only workspace halts. Do not use for: GCP migrations (see gcp-to-aws), Heroku migrations (see heroku-to-aws), general AWS architecture advice (see architect-for-startups), AWS-to-Azure reverse migration, or Azure-to-Azure refactoring.
日本語の概要は準備中です。原文の説明を表示しています。
Creates a production-ready VPC with public and private subnets across multiple Availability Zones, including internet gateway, NAT gateways, route tables, and security groups following AWS Well-Architected principles. Use when deploying multi-AZ VPC infrastructure with automatic CIDR planning and DNS resolution.
日本語の概要は準備中です。原文の説明を表示しています。
Provisions, connects, migrates, and operates Amazon RDS for Db2. Applies when provisioning with IBM customer and site IDs (License Manager, BYOL, GovCloud), connecting over TLS, fixing SQL30082N after Secrets Manager rotation, migration from Db2 LUW (Linux, AIX, Windows, AS400) or z/OS mainframe (ADB2GEN, Q Replication), choosing code page/collation (EBCDIC, CCSID), S3 backup/restore, Multi-AZ and cross-region standby replicas, RDSADMIN procedures, customer-managed KMS BYOK, self-managed Active Directory Kerberos, Db2 audit to S3, minimum IAM, or colocation.
日本語の概要は準備中です。原文の説明を表示しています。
Enables internet access for AWS Lambda functions deployed in VPC subnets by creating NAT Gateway infrastructure, configuring public/private subnet routing, and updating security groups. Use when a VPC-attached Lambda function cannot reach the internet.
日本語の概要は準備中です。原文の説明を表示しています。
Creates and automates custom image builds with EC2 Image Builder - Linux, Windows, and macOS AMIs, and container images to ECR. Covers the build IAM role, Amazon-managed and custom components, image recipes, infrastructure and distribution configuration (launch templates, SSM parameters, other Regions), one-off builds, recurring scheduled pipelines for golden AMI automation and OS patching, custom image workflows, and diagnosing failed builds. Applies when creating, automating, or scheduling AMI or container image builds with Image Builder, or when debugging a failed build. Not for launching instances from existing AMIs, AMI lifecycle/retirement, or general EC2 fleet management.
日本語の概要は準備中です。原文の説明を表示しています。
Authors, deploys, and troubleshoots AWS infrastructure using CDK with TypeScript or Python. Covers best practices, stack architecture, and construct patterns. Applies when writing CDK constructs, bootstrapping environments, running cdk deploy/synth/diff, fixing CDK or CloudFormation errors, planning stack structure, importing existing resources, resolving drift, or refactoring stacks without resource replacement.
日本語の概要は準備中です。原文の説明を表示しています。