489 件 ・ 関連度順
概要と使いどころ
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.
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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。
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。
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。
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。
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。
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。
AI Agent Workflow & Skill Architecture Guide — turn expert work into measurable, reusable agent systems. Covers workflow discovery, skill decomposition, harness design, evaluation, human escalation, observability, cost control, and multi-agent orchestration. Includes a measured marketing-delivery case: 15 people × 3–4 weeks reduced to one strategist + AI in 5 days. By Gingiris.
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。
View and manage the runtime changelog for 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。
TDD-based autonomous development loop with checkpoint recovery and observability changelog
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。
Audit existing service mesh configuration — find mTLS gaps, traffic policy issues, and observability holes. Use when asked to "audit our service mesh", "find mTLS gaps", or "review traffic policy issues".
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。
Expert guide for observability, analytics, telemetry, and data pipelines (OpenTelemetry, PostHog, Mixpanel) / Panduan ahli untuk observabilitas, telemetri, dan analitik.
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Reference guide for the Twelve-Factor App methodology — 15 principles (12 original + 3 modern extensions) for building portable, resilient, cloud-native applications. Use when evaluating application architecture, designing cloud-native services, reviewing codebases for methodology compliance, advising on configuration, scaling, observability, security, and deployment patterns. Incorporates the 2025 open-source community evolution and cloud-native reinterpretations of each factor.
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 building, extending, or debugging FastMCP v4 (or v3) Python MCP servers. Activates on FastMCP tool/resource/prompt creation, provider and transform implementation (CodeMode, Tool Search), auth setup (MultiAuth, PropelAuth, KeycloakProvider), client SDK usage, FastMCPApp and Generative UI server building, fastmcp-slim client-only installs, nginx reverse proxy deployment, Prefab Apps, OTEL observability, testing, and v3→v4 migration. Grounded in official FastMCP v4 docs plus locally-verified v3 gotchas — zero speculation.
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。
Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.
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 production-ready AI agents with Microsoft Foundry and Agent Framework. Use when creating AI agents, selecting LLM models, implementing agent orchestration, adding tracing/observability, or evaluating agent quality. Covers agent architecture, model selection, multi-agent workflows, and production 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。
System monitoring, alerting, and observability 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。
Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
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。
实现日志 / 指标 / 追踪三大支柱时使用。覆盖 OpenTelemetry / Prometheus / Grafana / Jaeger / Datadog / Sentry 集成。融合 Google SRE 三支柱 + 结构化日志 + Trace Context 传播。
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Builds CloudWatch Logs Insights queries and metric alarms using AWS SDK v3 (@aws-sdk/client-cloudwatch-logs, @aws-sdk/client-cloudwatch). Generates cross-account observability dashboards with CloudWatch Metrics Insights.
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 Odigos to add OpenTelemetry tracing to Kubernetes or VM workloads without code changes, then route the traces to the observability backend an operator already uses.
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Expert logging guidance based on Boris Tane's loggingsucks.com philosophy. Use when implementing logging, adding observability, debugging production issues, or reviewing code that includes log statements. Covers wide events architecture, structured logging, smart sampling, and high-cardinality field design.
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。
CTO-level architectural advisor for AI-native development. Use this skill whenever you encounter code design decisions, architecture discussions, system resilience questions, or any work touching: "architecture", "design", "scale", "dependencies", "state", "failure", "blast radius", "refactor", "migrate", "optimize", "resilience", "consistency", "observability", "bottleneck", "coupling", "monolith", "microservices", "distributed", "concurrency", "data flow", "system design", or any prompt suggesting code-first thinking when design-first thinking is needed. This skill integrates with Claude Code to review generated code for architectural soundness, define design systems via design.md, and guide teams toward CTO-level thinking. Trigger aggressively on architectural questions—this is where AI adds the most leverage.
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。