510 skills · sorted by relevance
Structured debug log analysis for Claude Code sessions — auto-discovers most recent log, runs reducer, extracts error patterns, correlates with full log, produces observability report. Fills 5 identified gaps: hook error body capture, agent identity, file path tracking, stall correlation, success visibility.
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Use when adding telemetry, composing an observability surface, reviewing alerts, shipping a production feature, or diagnosing a production issue. Not for live failures, profiling, or launch runbooks.
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Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
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Post-deploy observability for production services — structured logging, RED metrics + Prometheus, Grafana dashboards, SLO-based alerting. Use when setting up monitoring or instrumenting a live service. Trigger on "add logging", "metrics", "dashboard", "alerting", "observability", "monitor this service".
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LLM observability with Langfuse — OpenTelemetry-based tracing, evaluations, prompt management, datasets, and production best practices
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Configures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection. Don't use to configure local application logging frameworks or external APMs outside GKE.
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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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Spring Boot 4 observability with Actuator, Micrometer, and OpenTelemetry. Use when configuring health indicators, health checks, custom metrics, distributed tracing, production endpoint exposure, Kubernetes probes, Cloud Run probes, logging, or monitoring. Covers Actuator endpoint security, Micrometer Timer/Counter/Gauge patterns, OpenTelemetry span customization, liveness/readiness probe setup, and metric tag best practices.
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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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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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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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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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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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Skill for the Observability area of paddock. 105 symbols across 26 files.
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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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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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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。
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Prometheus, Grafana, logging, alerting, and data pipeline observability
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Ensure code changes keep logs/metrics/traces useful for debugging failures and regressions.
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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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