「observability」の検索結果

Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.

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Use when monitoring Agentforce agent sessions, analyzing conversation logs, measuring deflection rates, or diagnosing agent performance issues. Triggers: 'agentforce session analytics', 'how to query agent conversation data', 'monitor agentforce agent effectiveness', 'agent deflection rate', 'utterance analysis agentforce'. NOT for Trust Layer audit logging — use agentforce/einstein-trust-layer. NOT for designing the topic boundaries and guardrails that cause the misrouting you are seeing in the data — use agentforce/agentforce-guardrails.

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Build the executive KPI dashboard for Agentforce: adoption, deflection, latency, cost, quality — KPI definitions, data sources, CRM Analytics lenses, alert thresholds. NOT for the platform's own session tracing, Agent Analytics and health monitoring — use agentforce/agentforce-observability. NOT for scoring agent answer quality offline — use agentforce/agentforce-eval-harness.

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Plan, create, and configure production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers Day-0 checklist, Autopilot vs Standard, networking (private clusters, VPC-native, Gateway API), security (Workload Identity, Secret Manager, RBAC hardening), observability, scaling, cost optimization, and AI/ML inference. WHEN: create GKE cluster, provision GKE environment, design GKE networking, secure GKE, optimize GKE cost, GKE autoscaling, GKE inference, GKE upgrade, GKE observability, GKE multi-tenancy, GKE batch, GKE HPC, GKE compute class.

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Plan and implement infrastructure, containers, CI/CD, deployment safety, observability, and rollback changes. Use for Docker, IaC, cloud runtime, Kubernetes, pipeline, deployment, or operational reliability work. Does not read dotenv files or approve/release deployments.

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Guide setup for and verify a reviewed, read-only Grafana Observability profile and its activated capability packs. Use when a user asks to set up Grafana access, verify profile readiness, discover available infrastructure, APM, log, IoT or business KPI packs, or troubleshoot why a Grafana pack cannot run.

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Message queues, pub/sub, event sourcing with Kafka, RabbitMQ, Redis Streams. Activate on: event-driven, message queue, pub/sub, Kafka, RabbitMQ, event bus, async messaging, dead letter queue. NOT for: CQRS projections (use cqrs-event-sourcing-architect), real-time WebSocket (use websocket-realtime-expert), observability (use observability-apm-expert).

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Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).

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Skill for the Observability area of paddock. 105 symbols across 26 files.

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Plataforma de observabilidade para LLM com rastreamento, avaliação e monitoramento. Use ao depurar aplicações LLM, avaliar saídas de modelos em datasets, monitorar sistemas em produção ou construir pipelines de testes sistemáticos para aplicações de IA.

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Plataforma de observabilidade de IA de código aberto para rastreamento de LLM, avaliação e monitoramento. Use ao debugar aplicações de LLM com rastreamentos detalhados, executar avaliações em datasets ou monitorar sistemas de IA em produção com insights em tempo real.

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Cloudflare Workersのコードを本番運用品質のベストプラクティスに照らして作成・レビューする。新しいWorkerの実装、Workerコードのレビュー、wrangler.jsoncの設定、streaming・floating promises・global state・secrets・bindings・observabilityなどのanti-pattern確認で使用する。事前学習の記憶よりCloudflare公式ドキュメントからの取得を優先する。

Designs and reviews production observability for mobile apps on every stack — Flutter, native Android, native iOS, React Native and Kotlin Multiplatform. Covers crash and error reporting with usable context, breadcrumbs, structured logs, traces and custom metrics, release and cohort segmentation, correlation with backend requests, alerting thresholds, sampling and cost, and what must never be collected. Use when production problems cannot be explained from the available data, when setting up crash reporting or telemetry, or when defining release health alerts.

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Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET).

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Expert en observabilité des agents IA (tracing, logging, suivi des coûts, latence, métriques qualité)

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Prometheus, Grafana, logging, alerting, and data pipeline observability

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When setting up telemetry, debugging distributed systems, or standardizing application output.

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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.

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。

Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET).

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。

Implement eBPF-based runtime security observability and enforcement in Kubernetes clusters using Cilium Tetragon for kernel-level threat Tespit and policy enforcement.

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25 advanced engineering agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Agent design, RAG, MCP servers, CI/CD, database design, observability, security auditing, release management, platform ops.

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Focused Signals scout for PostHog projects using AI observability. Rotates through a set of lenses — cost, latency, errors, volume, eval performance, eval/enrichment config, clusters, and tool usage — watching each for trends and spikes sliced by the dimensions it discovers over time. Leans on the sandbox's bundled `exploring-llm-*` deep-dive skills for the actual queries. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other scouts.

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Focused Signals scout for finding observability gaps in PostHog itself — significant event volumes the team isn't tracking, custom events with no insight or dashboard coverage, insights pointing at events that have stopped firing, dashboards missing related context, critical events with no alerts. Watches the event-stream-vs-saved- inventory delta as the team's product evolves and emits findings recommending new insights, dashboard additions, or alerts when gaps clear the confidence bar. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace or session URL (e.g. /ai-observability/traces/<id> or /ai-observability/sessions/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs. Also use when raw SQL/HogQL against `events.properties.$ai_input` / `$ai_output_choices` returns empty — message content lives only on the dedicated `posthog.ai_events` table.

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