クラウド、CI/CD、監視、デプロイ（6,714 件）
概要と使いどころ
Guide users from coverage analysis to monitor creation. USE WHEN user asks what should I monitor, where are my gaps, improve coverage, or wants a systematic approach to monitoring across their data estate.
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。
Diagnoses pipeline performance issues -- slow jobs, expensive queries, latency trends -- using Monte Carlo's cross-platform observability. Uses a tiered investigation approach: discover problems, bridge to affected tables, then drill into root causes. Activates when a user asks about slow pipelines, expensive queries, or performance regressions.
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。
Troubleshoots Monte Carlo AI agent alerts and traces — eval score drops, latency/token spikes, trajectory and validation breaches. Not for data incidents (monte-carlo-analyze-root-cause) or monitor creation (monte-carlo-monitoring-advisor).
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。
Reinforces an AI agent by turning Monte Carlo's reinforcement loop diagnosis into code fixes. Reads the daily reinforcement loop report for an agent's workflows, ranks the diagnosed issues, proposes what to fix, and — with the user's approval at each step — opens a pull request. Activates on "fix my agent", "improve my agent's health", "reinforce my agent", "what should I fix in my agent". Not for investigating a specific agent alert or trace (monte-carlo-troubleshoot-agent-traces), creating agent monitors (monte-carlo-monitoring-advisor), or instrumenting a new agent (monte-carlo-instrument-agent).
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。
Connect warehouses, BI tools (Tableau, Looker, Power BI) and custom connectors to Monte Carlo with API v2 tools: show what exists, reference credentials, create, validate and fix connections. Use to onboard platforms or generate Terraform or scripts.
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。
Analyze data coverage, create monitors for warehouse tables and AI agents. Covers coverage gaps, use-case analysis, data monitor creation, and agent observability.
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。
Instrument a new AI agent in a Python codebase for Monte Carlo Agent Observability. Detects AI libraries, installs the Monte Carlo OpenTelemetry SDK, and proposes tracing setup and decorator placements as diffs. Asks before editing any file.
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 data incidents and find root causes using Monte Carlo's observability data. Guides the agent through systematic investigation: alert lookup, lineage tracing, ETL checks, query analysis, and data profiling. Activates when a user asks about data issues, incidents, alerts, or why data looks wrong.
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。
[BETA] Offload plan phase to Claude Code's ultraplan cloud — drafts remotely while terminal stays free, review in browser with inline comments, import back via /gsd:import. Claude Code only.
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。
Capture or review how this project connects to external systems (GitHub, AWS, npm, SSH, etc.) so future sessions know the auth recipe. Auto-detection hook logs candidate captures to an inbox; this skill promotes them to permanent recipes in `.planning/AUTH-RECIPES.md` and optionally to user-global memory at `~/.claude/auth-recipes/`.
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。
Post-deploy canary monitoring. (gstack)
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 scaffolds supporting student self-regulation across planning, monitoring, and evaluation phases. Use when students struggle to manage their own learning during extended or independent tasks.
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 metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
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。
Build students' capacity to place historical documents in their temporal and social context. Use when students read sources without considering what was happening at the time, or know the context but don't deploy it.
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。
When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms, or wants to do social listening and engagement triage. Also use when the user mentions 'LinkedIn post,' 'Twitter thread,' 'social media,' 'content calendar,' 'social scheduling,' 'engagement,' 'viral content,' 'what should I post,' 'repurpose this content,' 'tweet ideas,' 'LinkedIn carousel,' 'social media strategy,' 'grow my following,' 'TikTok video,' 'Reels,' 'Shorts,' 'video script,' 'video hook,' 'short-form video,' 'create a reel,' 'social listening,' 'brand mentions,' 'competitor monitoring,' 'top posts to comment on,' or 'find people asking for.' Use this for social media content creation, repurposing, scheduling, short-form video scripting, and social listening. For broader content strategy, see content-strategy. For paid ads, see ad-creative. For earned media, see public-relations.
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。
Prepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
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。
Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.
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。
LLMOps Architect: Run LLM features in production: model routing, evals, observability, caching, cost, safety.
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。
Web3 Infrastructure Architect: Indexers, RPC, wallets, account abstraction, key management and off-chain services.
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。
Deploys React/Vite applications to Alibaba Cloud ESA Pages. Use when the user needs to deploy frontend applications, manage deployments, configure custom domains, or troubleshoot deployment issues on ESA (Edge Security Acceleration) platform.
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 this skill when building, debugging, or answering questions about Liveblocks. Liveblocks gives you the building blocks and infrastructure to enable people and AI to work together inside your app, powering realtime collaboration. Liveblocks features include collaboration, rooms, organizations, workspaces, comments, composer, threads, notifications, multiplayer, conflict resolution, realtime presence, avatar stacks, AI collaborators, AI agents, text editors, Tiptap, BlockNote, Lexical, React Flow, Chat SDK. Common components include AiChat, Thread, InboxNotification, Composer, Toolbar (for Lexical Tiptap), FloatingToolbar, FloatingComposer, FloatingThreads, AnchoredThreads. Common hooks include useThreads, useStorage, useMutation, useOthers, useInboxNotifications, useAiChats. Common issues are related to authentication (ID tokens vs access tokens), permissions, room limits, connection errors, user info.
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。
Deploy application to target platform. Use when user explicitly says 'deploy', 'push to production', 'ship it'. Handles Vercel, Netlify, AWS, GCP, DigitalOcean, and VPS with pre-deploy verification and health checks. Enforces cost allocation tags + Managed-vs-Self-Host crossover decisions so deploy choices map to actual unit economics, not hand-waved 'we'll optimize later'.
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。
Multi-platform Chinese & multilingual TTS text-to-speech via Edge/Doubao/CosyVoice/Qwen3/StepFun/GLM-TTS/Azure/Tencent/Baidu/MiniMax/Xunfei plus ElevenLabs/OpenAI/Google/Atlas Cloud — 15 backends, word-level timestamps, [PAUSE:x] pause markers, pinyin pronunciation overrides
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。
Index and router for plugin-specific Craft CMS 5 guidance — configuration, Twig API, PHP API, migrations, deployment, and pitfalls for the plugins this pack documents. Load whenever a task names a plugin this pack documents, and for passwordless or magic-link auth with no plugin named; read references/<plugin>.md first.
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。