クラウド、CI/CD、監視、デプロイ（6,719 件）
概要と使いどころ
Create a structured on-call log by pulling alerts from monitoring and writing a summary to your team's wiki. Requires a monitoring MCP (recommended: Datadog) and a wiki MCP (Confluence or Notion).
日本語の概要は準備中です。原文の説明を表示しています。
Quick reference for Terraform conventions including file organization, naming, modules, state, security, and anti-patterns. Use when writing or reviewing Terraform code.
日本語の概要は準備中です。原文の説明を表示しています。
CI/CD pipeline patterns for GitHub Actions, PR automation, and deployment workflows. Use when setting up CI, fixing broken pipelines, automating PR checks, or configuring deployment.
日本語の概要は準備中です。原文の説明を表示しています。
Create or extend reusable Terraform modules with proper structure, interfaces, and documentation. Use when building new infrastructure modules or extending existing ones.
日本語の概要は準備中です。原文の説明を表示しています。
PR review checklist for Terraform changes covering structure, state safety, security, naming, modules, variables, providers, and CI/CD. Use when reviewing Terraform PRs or doing pre-merge checks.
日本語の概要は準備中です。原文の説明を表示しています。
Generate complete service-level Terraform infrastructure with modules, environments, and CI/CD. Use when adding Terraform to a new service or bootstrapping infrastructure from scratch.
日本語の概要は準備中です。原文の説明を表示しています。
Investigate a monitoring alert end-to-end. Pulls metrics, logs, traces, and recent code changes to identify root cause. Works with any monitoring MCP.
日本語の概要は準備中です。原文の説明を表示しています。
Use when creating new skills, editing existing skills, or verifying skills work before deployment
日本語の概要は準備中です。原文の説明を表示しています。
Scrape web pages using Scrapling with anti-bot bypass (like Cloudflare Turnstile), stealth headless browsing, spiders framework, adaptive scraping, and JavaScript rendering. This skill should be used when asked to scrape, crawl, or extract data from websites; web_fetch fails; the site has anti-bot protections; write Python code to scrape/crawl; write spiders.
日本語の概要は準備中です。原文の説明を表示しています。
Home Assistant YAML configuration and automation. The most common skill for general HA questions. Covers automations, blueprints, scripts, scenes, template sensors, Lovelace dashboards, Mushroom cards, packages, helpers, presence detection, voice Assist, calendar automation, Jinja2 templates, notification patterns, energy monitoring, Matter devices, automation debugging and traces, dashboard sections view, and trigger-based template entities.
日本語の概要は準備中です。原文の説明を表示しています。
Aurora Smart Home orchestrator — routing layer for all smart home skills. Use this skill when the user asks ANY smart home question and you need to decide which skill to invoke, or when a task spans multiple skills (e.g., "build a sensor that shows on a dashboard and triggers automations"). Invoke aurora FIRST before reaching for a specific skill — it will route to the right specialist(s) and recommend the correct Claude model to keep token usage efficient. Trigger on: smart home, Home Assistant, ESPHome, automation, IoT, dashboard, ESP32, Node-RED, or any request about controlling or monitoring devices at home.
日本語の概要は準備中です。原文の説明を表示しています。
Prove a deploy landed — the served revision is the commit you meant, its env is present, and the product does its job — instead of trusting the deploy command's exit code. Also catches server drift before a pull or deploy. Use after any deploy, rollout, restart or server-side fix ("задеплой", "выкати", "проверь на проде", "почему не обновилось"). Loaded by devops, l3-support, infra-provisioner.
日本語の概要は準備中です。原文の説明を表示しています。
Scaffold-time observability so a shipped product is not blind in prod from day one — error capture (Sentry), request-id structured logging, and /healthz + /readyz endpoints. stack-baseline pins Sentry but nothing wires it; this is the wiring. Loaded by app-scaffolder (bake into the scaffold), infra-provisioner (prod env + probes), and consumed by l3-support (traces) and devops (deploy gate).
日本語の概要は準備中です。原文の説明を表示しています。
Standardized cost-estimation framework for great_cto plans. Forces explicit LLM cost, infra cost, human-supervision time, and the (defensible) human-equivalent comparison. Output format is parsable by the board's /api/cost path — must follow exactly.
日本語の概要は準備中です。原文の説明を表示しています。
Explain what the Cognee plugin captures, recalls, stores and sends, where its data and logs live, and how to inspect or turn off each behavior; check its local or cloud connection and settings.
日本語の概要は準備中です。原文の説明を表示しています。
Design local AI engines for iOS. Use when wiring Apple Foundation Models, local classifiers, extraction, summarization, grounded answers, and cloud fallbacks.
日本語の概要は準備中です。原文の説明を表示しています。
Chooses PaaS hosting for apps, agents, APIs, and workers. Use when picking Vercel, Fly.io, Railway, Render, Cloudflare, Netlify, Heroku, Cloud Run, or container PaaS.
日本語の概要は準備中です。原文の説明を表示しています。
Guides Cloudflare Wrangler CLI usage for Workers, bindings, deploys, local dev, and config. Use when running or reviewing wrangler commands.
日本語の概要は準備中です。原文の説明を表示しています。
Designs DevOps and platform engineering systems. Use when planning Kubernetes, Terraform, GitOps, CI/CD, observability, incident response, or cloud-native operations.
日本語の概要は準備中です。原文の説明を表示しています。
AI agent architecture, graph and loop composition, protocol choice, evaluation, and observability. Use when scoping or reviewing systems before implementation.
日本語の概要は準備中です。原文の説明を表示しています。
Builds production voice bots and IVR with Python STT/TTS pipelines. Use when designing telephony, streaming audio, latency budgets, or voice quality monitoring.
日本語の概要は準備中です。原文の説明を表示しています。
Sizes models and token budgets using Kaplan/Chinchilla scaling laws. Use when reasoning about compute-optimal N and D, tokens-per-parameter ratios, or over-training tradeoffs.
日本語の概要は準備中です。原文の説明を表示しています。
Runs production ML and LLM systems. Use when deploying, canarying, rolling back, monitoring drift or feature skew, promoting champions, migrating providers, or handling incidents.
日本語の概要は準備中です。原文の説明を表示しています。
Designs coding-agent observability and evals. Use when measuring traces, replay, checkpoint lineage, tool grading, regression, or cost. Not for general LLM evals (ai-evals).
日本語の概要は準備中です。原文の説明を表示しています。