MicrosoftSkilldeploying-scalable-agents
microsoft/ai-agents-for-beginners/translations/pcm/.agents/skills/deploying-scalable-agents/SKILL.md
Take one working agent prototype go scalable, observable production
Claude skills and agent skills
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MicrosoftSkill
NVIDIA-AI-BlueprintsSkillvss-deploy-detection-tracking-2d
NVIDIA-AI-Blueprints/video-search-and-summarization/skills/deployment/vss-deploy-detection-tracking-2d/SKILL.md
Use this skill when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice. Trigger when the user says things like 'deploy rtvi-cv', 'start warehouse 2d', 'add a stream', 'check rtvi-cv health', or 'stop the perception container'. Not for VLM, embedding, or analytics — use the matching vss-* skill.
vellum-aiSkilldeploy-fullstack-vercel
vellum-ai/vellum-assistant/skills/deploy-fullstack-vercel/SKILL.md
Build and deploy a full-stack app (React frontend + Python/FastAPI backend) or a Vellum app to Vercel as a serverless demo with seeded data
AWSSkilldeploying-custom-domain-rest-api
aws/agent-toolkit-for-aws/skills/specialized-skills/serverless-skills/deploying-custom-domain-rest-api/SKILL.md
Deploys a Regional REST API with a custom domain name, a Lambda backend function, and a request-based Lambda authorizer using AWS CLI. Covers ACM certificate provisioning, API Gateway REST API creation, Lambda function deployment, request authorizer setup, custom domain configuration, base path mapping, and Route 53 DNS record creation. Trigger keywords: custom domain, REST API, Lambda, Route 53, API Gateway, regional endpoint, request authorizer, base path mapping.
MicrosoftSkilldeploying-scalable-agents
microsoft/ai-agents-for-beginners/translations/tl/.agents/skills/deploying-scalable-agents/SKILL.md
Dalhin ang isang gumaganang prototype ng agent sa isang scalable, observable
AWSSkillaws-ai-ml
aws/agent-toolkit-for-aws/plugins/aws-core/skills/aws-ai-ml/SKILL.md
Selects, deploys, and customizes AI models on Amazon SageMaker. Fine-tuning (SFT, DPO, RLVR, RLAIF), model selection, dataset preparation, evaluation, deployment to SageMaker endpoints or Bedrock, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing/selecting which base model to customize or fine-tune from SageMaker Hub, finding a model to deploy without fine-tuning, transforming datasets for training, checking data readiness, evaluating model quality, deploying to endpoints, benchmarking or optimizing inference, setting up IAM roles and S3 buckets for training jobs, or managing a SageMaker Managed MLflow app. Also use to check endpoint health, diagnose failures, debug latency or errors, or view container logs and CloudWatch metrics. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.
NVIDIA-AI-BlueprintsSkillvss-build-vision-ai
NVIDIA-AI-Blueprints/video-search-and-summarization/skills/vss-build-vision-ai/SKILL.md
Add agent-ready vision capabilities — dense captioning, detection, search, alerting, summarization — to an agent or application through a customizable, self-contained vision stack built on the NVIDIA VSS Blueprint. Use this skill when a developer or agent wants to give their app vision: pick capabilities via guided intake ("build a vision agent", "add vision capabilities") or describe them in natural language ("create a profile for streaming dense captioning", "add agentic search to my base deployment", "deploy warehouse 3d"). Route, compose, configure, and deploy stock base, alerts, LVS, search developer profiles, or the warehouse industry profile and lean custom combinations expressed as delta overlays using one current developer profile as the Foundation. Not for operating a stack that is already deployed — searching, asking about a video, summarizing, managing alerts, or generating a report — and not for deploying a single microservice on its own; use the matching vss-* skill for those.
BankrBotSkillclanker
BankrBot/skills/clanker/SKILL.md
Deploy ERC20 tokens on Base, Ethereum, Arbitrum, and other EVM chains using the Clanker SDK. Use when the user wants to deploy a new token, create a memecoin, set up token vesting, configure airdrops, manage token rewards, claim LP fees, or update token metadata. Supports V4 deployment with vaults, airdrops, dev buys, custom market caps, vanity addresses, and multi-chain deployment.
OpenAISkillphysical-ai-infrastructure-setup-and-resilient-scaling
openai/plugins/plugins/nvidia/skills/physical-ai-infrastructure-setup-and-resilient-scaling/SKILL.md
Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log summarization or workload-only operations unless infrastructure setup, scaling, validation, or recovery is requested.
MicrosoftSkillcapacity
microsoft/skills/.github/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model/capacity/SKILL.md
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
GoogleSkillgoogle-agents-cli-scaffold
google/agents-cli/skills/google-agents-cli-scaffold/SKILL.md
This skill should be used when the user wants to "create an agent project", "start a new ADK project", "build me a new agent", "add CI/CD to my project", "add deployment", "enhance my project", or "upgrade my project". Part of the agents-cli skills suite. Covers `agents-cli scaffold create`, `scaffold enhance`, and `scaffold upgrade` commands, template options, deployment targets, and the prototype-first workflow. Do NOT use for writing agent code (ADK projects: use google-agents-cli-adk-code) or deployment operations (use google-agents-cli-deploy).
Hugging FaceSkillhf-cloud-sagemaker-production-defaults
huggingface/skills/skills/hf-cloud-sagemaker-production-defaults/SKILL.md
Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.
Google CloudSkillge-demo-generator
GoogleCloudPlatform/generative-ai/search/gemini-enterprise/ge-demo-generator/skills/ge-demo-generator/SKILL.md
Synthesizes and deploys complete, domain-specific Gemini Enterprise demo environments directly to Google Cloud. Use when the user asks to create an AI agent demo for any customer domain (e.g. 'example.com', 'example.co.jp', 'example.de', 'example.fr' - any company, any industry, any region) or business goal, generate realistic BigQuery/Firestore sample datasets, create external demo files (PDF, Excel, scanned images), stage them in Cloud Storage and upload them to the deploying account's Google Drive, scaffold ADK multi-agent architectures with MCP tools and A2UI cards, deploy to Cloud Run, publish to Gemini Enterprise, and generate 7 structured demo prompts in any language. Confirms the requirements interactively and presents a demo architecture & data model plan (Mermaid ER diagram, external file lineage, target project) for approval before anything is deployed. Also triggered by /ge-demo-generator.
NVIDIA-AI-BlueprintsSkillvss-deploy-detection-tracking-3d
NVIDIA-AI-Blueprints/video-search-and-summarization/skills/deployment/vss-deploy-detection-tracking-3d/SKILL.md
Use when deploying or operating standalone RTVI-CV-3D / MV3DT multi-camera 3D tracking for calibrated MP4/file inputs and live RTSP streams: missing-calibration handoff to AMC skills, the 4-camera sample dataset, camera config, BEV Fusion, live OSD or saved grid/BEV outputs, bundled brokers, basic external MQTT/Kafka brokers, verification, and teardown. Trigger for generic MV3DT, RTVI-CV-3D, multi-view 3D tracking, multi-cam tracking, or sample MV3DT dataset requests. Explicit warehouse blueprint/profile MV3DT requests route to vss-build-vision-ai; single-camera 2D tracking routes to the 2D tracking or DeepStream skills. Not for full warehouse blueprint deployment, single-camera 2D tracking, camera calibration itself, or VSS summarization, Q&A, and RAG workflows.
NVIDIASkillvss-deploy-video-embedding
NVIDIA/skills/skills/vss-deploy-video-embedding/SKILL.md
Use this skill when deploying, operating, or integrating the VSS 3.2 GA RT-Embed Video Embedding microservice. Covers Docker Compose bring-up, GPU and storage prerequisites, the `/v1` REST API (file uploads, text and video embeddings, live RTSP streams, health and metrics), Redis/Kafka/OTel integration, common failure modes, and teardown.
MicrosoftSkillfinetuning
microsoft/skills/.github/plugins/azure-skills/skills/microsoft-foundry/finetuning/SKILL.md
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
NVIDIASkilldeploy-airgapped
NVIDIA/deepops/skills/deploy-airgapped/SKILL.md
Prepare mirrors and transfer artifacts, configure DeepOps, deploy Slurm or Kubernetes GPU clusters without Internet access, and validate them with machine-readable gates. Use for disconnected, restricted-egress, offline, or air-gapped DeepOps installations and for diagnosing missing package, file, chart, or container artifacts.
GoogleSkillgoogle-cloud-global-frontend-configuration
google/skills/skills/cloud/google-cloud-global-frontend-configuration/SKILL.md
Guides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).
GoogleSkillgke-inference
google/skills/skills/cloud/gke-inference/SKILL.md
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).
OpenAISkillagents-sdk
openai/plugins/plugins/openai-developers/skills/agents-sdk/SKILL.md
Build, run, deploy, and evaluate OpenAI Agents SDK apps from Codex. Use when the user asks to create or adapt an Agents SDK app, build from a prompt or Codex thread, prepare a runnable agent prototype, add a focused eval harness, or deploy locally through the Agents SDK Deployment Manager.
About skills
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