インフラ・DevOpsのスキル

Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).

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Engineering observability and reliability: SLO/SLI design, distributed tracing, alerting, dashboards, capacity planning, toil automation, reliability review. Use for instrumentation or SLO definition.

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 something breaks in production: triage the severity, gather evidence, identify root cause, deploy a fix or mitigation, and write a post-mortem. Provides a calm, structured process for high-stress moments.

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。

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.

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Route data-related requests to the right Monte Carlo skill or workflow. USE WHEN alerts, incidents, data broken, stale, coverage gaps, data quality, or any ambiguous data observability request.

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).

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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.

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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.

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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.

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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.

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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。