インフラ・DevOpsのスキル

Automates Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.

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Docker containerization expert with deep knowledge of multi-stage builds, image optimization, container security, Docker Compose orchestration, and production deployment patterns. Use PROACTIVELY for Dockerfile optimization, container issues, image size problems, security hardening, networking, and orchestration challenges.

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SurrealDB Surrealism WASM extension development. Write Rust functions, compile to WASM, deploy as database modules. Part of the surreal-skills collection.

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Dokku is a self-hosted Platform-as-a-Service built on Docker that provides Heroku-like git-push deployment on your own infrastructure. With 32,000+ GitHub stars, it supports buildpacks, Dockerfiles, custom domains, SSL via Let's Encrypt, and a rich plugin ecosystem for databases, caching, and storage.

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用 Serenity(@aleabitoreddit)的"供应链卡点逆向"投资逻辑分析股票/板块——帮你判断该怎么分析、往哪一层挖、什么会证实或证伪、值不值得投。把市场当物理系统而非代码列表,先建 thesis 再谈标的。当用户聊美股/AI 供应链/光模块(CPO/硅光/InP)/半导体/内存/NeoCloud/电力液冷/机器人等 AI 全产业链任意细分的投资分析,或主动 /serenity 时使用。

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Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Cursor, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 69 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "deep paper review", "auto research", "switch model", "multi-agent", "coding session", "session inbox", "cursor agent", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness".

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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Set up the Graphsignal Profiler for inference workloads — vLLM, SGLang, PyTorch, and dstack services. Use when the user wants GPU profiling, tracing, or monitoring for inference, asks about `graphsignal-run` or `graphsignal.watch()`, or asks about CUPTI / Prometheus / OTLP setup.

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Trigger this skill when building applications with Gemma or for general knowledge inquiries related to Gemma models (e.g. prompt structure, capabilities). Covers model selection, development workflows, and deployment best practices.

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Generate an interactive bash wizard that walks a human through steps only they can perform. Use when provisioning infrastructure, setting up credentials or CI secrets, walking an unfamiliar third-party dashboard, or running a one-off migration or cutover. Don't invoke this for steps the agent can perform itself.

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Installs and runs DeerFlow 2.0, ByteDance's open-source "super agent harness" that gives a lead agent sub-agents, long-term memory, a sandbox for running code, and a library of skills for deep research, reports and slide decks. Use when a user wants to set up DeerFlow with Docker or locally, configure models in config.yaml, choose a sandbox mode, call it from Python with DeerFlowClient, or debug a broken setup with make doctor. Triggers include "set up DeerFlow", "run a deep research agent", "super agent harness", "deer-flow config.yaml".

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Patterns for designing, building, and operating microservices architectures. Covers service decomposition, inter-service communication, resilience patterns, data consistency, and observability in distributed systems. Use when ", " mentioned.

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Use when reducing model size, improving inference speed, or deploying to edge devices - covers quantization, pruning, knowledge distillation, ONNX export, and TensorRT optimizationUse when ", " mentioned.

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Production deployment patterns for MCP servers including Docker, cloud platforms, monitoring, and scalabilityUse when "mcp deployment, deploy mcp server, mcp docker, mcp production, mcp monitoring, mcp, deployment, docker, production, monitoring, scaling" mentioned.

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Site reliability specialist for Prometheus metrics, distributed tracing, alerting strategies, and SLO designUse when "observability, monitoring, prometheus, grafana, alerting, slo, sli, metrics, tracing, logging, on-call, incident, observability, prometheus, grafana, tracing, jaeger, alerting, slo, sli, metrics, logging, sre, ml-memory" mentioned.

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Expert at making systems observable and debuggable. Covers structured logging, metrics collection, distributed tracing, error tracking, and alerting. Knows how to find the needle in the haystack when production breaks at 3 AM. Use when "observability, logging, metrics, tracing, monitoring, error tracking, Sentry, Datadog, OpenTelemetry, debugging production, observability, logging, metrics, tracing, monitoring, sentry, prometheus, opentelemetry" mentioned.

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Expert at diagnosing and fixing performance bottlenecks across the stack. Covers Core Web Vitals, database optimization, caching strategies, bundle optimization, and performance monitoring. Knows when to measure vs optimize. Use when "slow page load, performance optimization, core web vitals, bundle size, lighthouse score, database slow, memory leak, optimize performance, speed up, reduce load time, performance, optimization, core-web-vitals, caching, profiling, bundle-size, database" mentioned.

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Expert in NFT development - minting infrastructure, metadata standards, marketplaces, royalties, and collection management across EVM and SolanaUse when "nft, erc721, erc1155, mint, collection, metadata, opensea, metaplex, pfp, generative art, nft, erc721, erc1155, metaplex, opensea, royalties, metadata, ipfs" mentioned.

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Kubernetes is the operating system for the cloud. It schedules containers, handles networking, manages storage, and keeps your apps running. But K8s has a steep learning curve - abstractions on abstractions on abstractions. This skill covers the core concepts (Pods, Deployments, Services), production patterns (health checks, resource limits, HPA), and the gotchas that catch everyone (namespace confusion, service discovery, secret management). 2025 reality: You probably don't need to run your own cluster. Use managed K8s (EKS, GKE, AKS) unless you have a very good reason not to. Focus on writing good manifests, not managing etcd. Use when "kubernetes, k8s, kubectl, deployment manifest, helm chart, kustomize, pod, service yaml, ingress, horizontal pod autoscaler, hpa, kubernetes, k8s, containers, orchestration, devops, cloud-native, helm, kustomize" mentioned.

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Infrastructure and platform specialist for Kubernetes, Terraform, GitOps, and cloud-native architectureUse when "kubernetes, k8s, terraform, infrastructure, deployment, helm, argocd, gitops, service mesh, istio, cloud platform, kubernetes, terraform, gitops, argocd, helm, istio, aws, gcp, azure, infrastructure, platform, devops, ml-memory" mentioned.

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Use when designing system integrations, implementing API gateways, event-driven architectures, ESB patterns, or hybrid cloud connectivityUse when ", " mentioned.

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Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when "langfuse, llm observability, llm tracing, prompt management, llm evaluation, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.

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World-class infrastructure automation - Terraform, Pulumi, CloudFormation, and the battle scars from managing infrastructure that handles production trafficUse when "terraform, pulumi, cloudformation, infrastructure, iac, state file, remote backend, s3 backend, dynamodb lock, terraform plan, terraform apply, terraform destroy, module, workspace, provider, resource, state drift, import, aws, gcp, azure, infrastructure, terraform, pulumi, cloudformation, iac, devops, aws, gcp, azure, cloud" mentioned.

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Go is the language of infrastructure. From Docker to Kubernetes to the entire cloud-native ecosystem, Go powers the systems that run the internet. It's not about what you can build - it's about what you won't break at 3 AM. This skill covers idiomatic Go patterns, error handling, concurrency with goroutines and channels, HTTP servers, microservice architecture, and the standard library that makes Go so powerful. Key insight: Go's simplicity is a feature. Fight the urge to abstract. Embrace boring, readable code. 2025 lesson: The teams succeeding with Go are the ones who resist overengineering. A main.go with 500 lines beats a "clean architecture" with 50 packages. Use when "golang, go service, go microservice, goroutine, channels, go http, go api, go backend, gin, fiber, chi router, go concurrency, go, golang, microservices, backend, concurrency, goroutines, channels, http, api" mentioned.

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