DevOps 与云

excalidraw-diagram-generator

35Kdevops-cloud

根据自然语言描述生成 Excalidraw 图表。当用户要求“创建图表”、“制作流程图”、“可视化流程”、“绘制系统架构”、“创建思维导图”或“生成 Excalidraw 文件”时使用。支持流程图、关系图、思维导图和系统架构图。输出可直接在 Excalidraw 中打开的 .excalidraw JSON 文件。

github

claude-automation-recommender

33Kdevops-cloud

分析代码库并推荐适合的 Claude Code 自动化方案（包含 Hooks、Subagents、Skills、Plugins 以及 MCP 服务端）。当用户询问自动化推荐、希望优化其 Claude Code 配置、提及改进 Claude Code 工作流、询问如何为新项目首次配置 Claude Code，或想了解应该使用哪些 Claude Code 功能时使用。

anthropics

hyperframes-cli

29Kdevops-cloud

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

28Kdevops-cloud

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

25Kdevops-cloud

cmux 的后端 TypeScript 与云端虚拟机（Cloud VM）开发规范。适用于修改 web/app/api、web/services、后端脚本、云端 VM 生命周期、云厂商集成、Postgres、Stack Auth 付费门槛限制、数据库迁移（migrations）以及云厂商镜像构建脚本等场景。

manaflow-ai

baoyu-diagram

23Kdevops-cloud

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。

jimliu

baoyu-image-gen

22Kdevops-cloud

支持使用 OpenAI GPT Image 2、Azure OpenAI、Google、OpenRouter、DashScope（阿里通义万象）、Z.AI GLM-Image、MiniMax、即梦（Jimeng）、豆包（Seedream）、Replicate 及 Agnes API 进行 AI 图像生成。支持文生图、参考图（垫图）、宽高比设置，以及基于已保存 Prompt 文件的批量生成。默认串行生成；当用户已有多个 Prompt 或需要稳定的多图吞吐量时，推荐使用批量并行生成。适用于用户要求生成、创建或绘制图片的场景。

jimliu

context-mode

Use context-mode tools (ctx_execute, ctx_execute_file) instead of Bash/cat when processing large outputs. Triggers: "analyze logs", "summarize output", "process data", "parse JSON", "filter results", "extract errors", "check build output", "analyze dependencies", "process API response", "large file analysis", "page snapshot", "browser snapshot", "DOM structure", "inspect page", "accessibility tree", "Playwright snapshot", "run tests", "test output", "coverage report", "git log", "recent commits", "diff between branches", "list containers", "pod status", "disk usage", "fetch docs", "API reference", "index documentation", "call API", "check response", "query results", "find TODOs", "count lines", "codebase statistics", "security audit", "outdated packages", "dependency tree", "cloud resources", "CI/CD output". Also triggers on ANY MCP tool output that may exceed 20 lines. Subagent routing is handled automatically via PreToolUse hook.

mksglu

google-cloud-solution-hybrid-search-alloydb

Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.

gke-alert-configuration

Configures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.

google-cloud-solution-multi-agent-security

Designs, deploys, and secures Google Cloud Agent Gateway solutions. Use when the user needs to configure multi-agent security, ingress (CLIENT_TO_AGENT), or egress (AGENT_TO_ANYWHERE) patterns involving Model Armor, IAP, and Agent Registry. Don't use for general Cloud Load Balancing or basic VPC setup not related to Agent Gateways.

cloud-build-basics

Teaches the fundamentals of Google Cloud Build (GCB). Covers core concepts, API enablement, console navigation to the Build History page, and the end-to-end workflow for creating and manually running a basic build trigger. Do not use for managing private pools or complex pipeline architectures.

application-design-center-design-deploy

Processes GCP infrastructure design and deployment workflows within Application Design Center (ADC). Use when: - Designing GCP infrastructure with Terraform. - Validating local HCL. - Performing best-practice plan scans. - Importing templates to Application Design Center (ADC). - Deploying templates. - Troubleshooting deployment failures. Boundaries: - Only use for GCP-specific cloud infrastructure. - Only use for Terraform coding within the ADC context.

google-cloud-storage-fuse

Mounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when interacting with gcsfuse: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre).

gke-ai-troubleshooting-jobset-interruption

Diagnoses GKE JobSet interruptions, restarts, and preemptions for AI/ML training workloads autonomously. Use when troubleshooting JobSet restart loops, spot VM preemptions, node readiness failures, host VM issues, or coordinator worker crashes. Don't use for general GKE cluster creation, basic workload deployment, or non-JobSet application issues.

gke-manifest-generation

Generates and updates secure, production-ready Kubernetes YAML manifests optimized for GKE Autopilot and GKE Standard clusters. Use when creating or modifying GKE deployment manifests, configuring container security contexts, setting CPU/memory resource limits, defining readiness/liveness/startup probes, mounting secrets and volumes, configuring GKE Gateway API routes, targeting Spot VMs, or deploying AI model inference workloads (vLLM, TGI, Gemma). Don't use for live cluster operations, pod troubleshooting (use gke-workload-troubleshooting), or cluster infrastructure provisioning (use gke-cluster-creation).

gke-cost-optimization

Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).

google-cloud-solution-build-deploy-agents

Designs, builds, and deploys AI agents or multi-agent systems on Google Cloud. Provides an interactive workflow to gather requirements, recommend a tailored architecture, and generate deployment instructions. Use when designing or implementing agentic systems on Google Cloud. Don't use for general Google Cloud solution architecture (use google-cloud-solution-architecture instead) or for narrow tasks targeting a single product without agent context.