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Found 1,936 Skills
Deploy and configure Mercury Agent, a soul-driven AI agent with permission-hardened tools, token budgets, and multi-channel access
Production deployment principles and decision-making. Safe deployment workflows, rollback strategies, and verification. Teaches thinking, not scripts.
Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says "deploy model", "serve model", "start vLLM server", "launch SGLang", "TRT-LLM deploy", "AutoDeploy", "benchmark throughput", "serve checkpoint", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint. Do NOT use for quantizing models (use ptq) or evaluating accuracy (use evaluation).
Access and test Vercel deployments protected by Vercel Authentication, SSO, or Deployment Protection. Use when curl, agent-browser, Playwright, or another automated request reaches a Vercel login or protection page; when a protected preview or production URL returns 401 or 403; when TRUSTED_SOURCES_ENVIRONMENT_MISMATCH appears; or when choosing between `vercel curl` and the `x-vercel-trusted-oidc-idp-token` header.
Deploy Slidev presentations to the web. Use this skill for GitHub Pages, Netlify, Vercel, and Docker deployments.
Kubernetes Deployment 管理
Use when viewing service runtime or build logs. Use when user says "show logs", "why did deploy fail", "check build output", or "debug runtime error".
Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.
Provides comprehensive patterns for deploying Next.js applications to production. Use when configuring Docker containers, setting up GitHub Actions CI/CD pipelines, managing environment variables, implementing preview deployments, or setting up monitoring and logging for Next.js applications. Covers standalone output, multi-stage Docker builds, health checks, OpenTelemetry instrumentation, and production best practices.
Deploy telecine services to GCP Cloud Run via Pulumi, publish elements packages to npm, publish skills docs, rollback, scale resources, manage secrets, and debug failed deployments.
Deploy projects to Vercel with automatic configuration. Sets project name from folder name, deploys with --yes flag, and disables Vercel Authentication (SSO protection) post-deploy via API. Use when deploying to Vercel, running "deploy to vercel", "vercel deploy", or any Vercel deployment task. Handles both preview and production deployments.
Use when polling deployment health after a merge to verify the deployed SHA matches and apply a status label.