DevOps 与云
部署、CI/CD、云平台和基础设施
Skills 列表
hyperframes-cli
HyperFrames CLI 开发循环。用于运行 npx hyperframes init、add、catalog、capture、lint、validate、inspect、layout、snapshot、preview、play、render、publish、lambda、doctor、browser、info、upgrade、skills、compositions、docs、benchmark、telemetry、transcribe、tts 或 remove-background 时，或排查 HyperFrames 构建/渲染环境问题时。AWS Lambda 云端渲染的入口点（`hyperframes lambda deploy / render / progress / destroy / policies`）。
heygen-com
vercel-cli-with-tokens
使用基于令牌的身份验证在 Vercel 上部署和管理项目。当使用 Vercel CLI 通过访问令牌而非交互式登录时使用——例如“部署到 Vercel”、“设置 Vercel”、“向 Vercel 添加环境变量”。
vercel-labs
cmux-backend
Backend TypeScript and Cloud VM development rules for cmux. Use when editing web/app/api, web/services, backend scripts, Cloud VM lifecycle, provider integrations, Postgres, Stack Auth pricing gates, migrations, or provider image build scripts.
manaflow-ai
baoyu-diagram
创建专业的深色主题SVG图表，涵盖架构图、流程图、时序图、结构图、思维导图、时间线、说明/概念图等多种类型。当用户请求任何技术或概念图表、系统可视化、流程、数据流、组件关系、网络拓扑、决策树、组织架构图、状态机或任何结构/逻辑/过程的可视化表示时，使用此技能。当用户说“画个图”“画一个架构图”“diagram”“flowchart”“sequence diagram”“draw me a ...”或上传内容并要求可视化时，也会触发。输出始终是一个独立的.svg文件。
jimliubaoyu-image-gen
AI image generation with OpenAI GPT Image 2, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream, Replicate and Agnes APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.
jimliu
vercel-cli
Deploy, manage, inspect, and troubleshoot Vercel projects from the command line. Use for Vercel deployments, build failures, projects and teams, environment variables, domains and DNS, logs, metrics, Speed Insights, Core Web Vitals, request traces, usage, activity, alerts, firewall rules, cache, cron jobs, deploy hooks, Edge Config, feature flags, integrations, connectors, Blob storage, Container Registry (VCR), microfrontends, rolling releases, custom environments, Sandbox, agent/MCP setup, OAuth apps, preview access, local development, or `vercel api` fallback.
vercel
gke-basics
Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, configuring Workload Identity, or deciding between Autopilot and Standard modes. Don't use for specialized GKE networking (use gke-networking), advanced security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).
googleagent-platform-alert-configuration
Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (e.g., Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.
googlebigtable-basics
Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.
googleagent-platform-prompt-management
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
googlecloud-run-basics
Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).
google
ml-pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.
jeffallanchaos-engineer
Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates. Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems, fault injection, Chaos Monkey, Litmus Chaos.
jeffallansalesforce-developer
Writes and debugs Apex code, builds Lightning Web Components, optimizes SOQL queries, implements triggers, batch jobs, platform events, and integrations on the Salesforce platform. Use when developing Salesforce applications, customizing CRM workflows, managing governor limits, bulk processing, or setting up Salesforce DX and CI/CD pipelines.
jeffallansre-engineer
Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems. Use when defining SLIs/SLOs, managing error budgets, building reliable systems at scale, incident management, chaos engineering, toil reduction, or capacity planning.
jeffallancloud-architect
Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP. Use when designing cloud architectures, planning migrations, or optimizing multi-cloud deployments. Invoke for Well-Architected Framework, cost optimization, disaster recovery, landing zones, security architecture, serverless design.
jeffallanmicroservices-architect
Designs distributed system architectures, decomposes monoliths into bounded-context services, recommends communication patterns, and produces service boundary diagrams and resilience strategies. Use when designing distributed systems, decomposing monoliths, or implementing microservices patterns — including service boundaries, DDD, saga patterns, event sourcing, CQRS, service mesh, or distributed tracing.
jeffallanterraform-engineer
Use when implementing infrastructure as code with Terraform across AWS, Azure, or GCP. Invoke for module development (create reusable modules, manage module versioning), state management (migrate backends, import existing resources, resolve state conflicts), provider configuration, multi-environment workflows, and infrastructure testing.
jeffallandevops-engineer
Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.
jeffallankubernetes-specialist
在部署或管理 Kubernetes 工作负载时使用。调用以创建部署清单、配置 Pod 安全策略、设置服务账户、定义网络隔离规则、调试 Pod 崩溃、分析资源限制、检查容器日志或调整工作负载大小。用于 Helm Chart、RBAC 策略、NetworkPolicy、存储配置、性能优化、GitOps 流水线以及多集群管理。
jeffallan
mcp-builder
**所有MCP服务器工作的必读内容** - mcp-use框架的最佳实践和模式。 **在进行任何MCP服务器工作之前，请先阅读此内容**，包括： - 创建新的MCP服务器 - 修改现有的MCP服务器（添加/更新工具、资源、提示、组件） - 调试MCP服务器问题或错误 - 审查MCP服务器代码的质量、安全性或性能 - 回答关于MCP开发或mcp-use模式的问题 - 对server.tool()、server.resource()、server.prompt()或组件进行任何更改 此技能包含关键的架构决策、安全模式和常见陷阱。 在实现MCP功能之前，请务必查阅相关的参考文件。
mcp-usechatgpt-app-builder
**所有MCP服务器工作的必读内容**——mcp-use框架最佳实践与模式。 **在进行任何MCP服务器工作之前，请先阅读本文**，包括： - 创建新的MCP服务器 - 修改现有MCP服务器（添加/更新工具、资源、提示、组件） - 调试MCP服务器问题或错误 - 审查MCP服务器代码的质量、安全性或性能 - 回答关于MCP开发或mcp-use模式的问题 - 对server.tool()、server.resource()、server.prompt()或组件进行任何更改 本技能包含关键的架构决策、安全模式和常见陷阱。 在实现MCP功能之前，请务必查阅相关的参考文件。
mcp-usemcp-apps-builder
**所有MCP服务器工作的必读内容**——mcp-use框架最佳实践与模式。 **在进行任何MCP服务器工作之前，请先阅读本文**，包括： - 创建新的MCP服务器 - 修改现有的MCP服务器（添加/更新工具、资源、提示、组件） - 调试MCP服务器问题或错误 - 审查MCP服务器代码的质量、安全性或性能 - 回答关于MCP开发或mcp-use模式的问题 - 对server.tool()、server.resource()、server.prompt()或组件进行任何更改 本技能包含关键的架构决策、安全模式和常见陷阱。 在实现MCP功能之前，请务必查阅相关的参考文件。
mcp-useportless
Set up and use portless for named local dev server URLs (e.g. https://myapp.localhost instead of http://localhost:3000). Use when integrating portless into a project, configuring dev server names, setting up the local proxy, working with .localhost domains, or troubleshooting port/proxy issues.
vercel-labs