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Plan, implement, or review observability and instrumentation work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests observability and instrumentation work.
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Expert at making systems observable and debuggable. Covers structured logging, metrics collection, distributed tracing, error tracking, and alerting. Knows how to find the needle in the haystack when production breaks at 3 AM. Use when "observability, logging, metrics, tracing, monitoring, error tracking, Sentry, Datadog, OpenTelemetry, debugging production, observability, logging, metrics, tracing, monitoring, sentry, prometheus, opentelemetry" mentioned.
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Site reliability specialist for Prometheus metrics, distributed tracing, alerting strategies, and SLO designUse when "observability, monitoring, prometheus, grafana, alerting, slo, sli, metrics, tracing, logging, on-call, incident, observability, prometheus, grafana, tracing, jaeger, alerting, slo, sli, metrics, logging, sre, ml-memory" mentioned.
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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, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.
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API Mocking Framework workflow skill. Use this skill when the user needs You are an API mocking expert specializing in realistic mock services for development, testing, and demos. Design mocks that simulate real API behavior and enable parallel development and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Provides monitoring and observability best practices covering the three pillars (logs, metrics, traces), OpenTelemetry instrumentation, Prometheus/Grafana dashboards, SLO-based alerting, and APM strategies. Use when setting up monitoring, observability, prometheus, grafana, opentelemetry, alerting, tracing, logging, metrics, dashboards, SLOs, or APM.
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AgentCore の Observability ナレッジ。OpenTelemetry 必須4点セット（OTEL_EXPORTER設定・StrandsTelemetry・traceable デコレータ・Langfuse連携）、EMF メトリクス、Jaeger/コンソールエクスポーターでのトレース確認、トレースが出ない・繋がらない場合のデバッグ手順等
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Set up observability for AI systems. TRIGGERS - Use when user needs help with ai-observability-setup related tasks.
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Design production-ready observability strategies combining metrics, logs, and traces. Includes SLI/SLO design, golden-signals monitoring, alert optimization. Use when adding observability to a new service, refactoring alerting that is too noisy, or designing an SLO program before scaling production load.
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Ensure designs specify failure modes, timeouts, error contracts, and observability for critical flows.
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Busabase App-in-Skill dashboard visualizing a generated MOCK fleet of LLM agents running behind a shared AI gateway for a generic organization. Use when the user invokes $kelly-agent-observability or /kelly-agent-observability, wants to review agent health, call volume, latency, error rates, cost, or trace-level chain breaks across an agent fleet. This is a demo/reference dashboard over a generated mock fleet snapshot seeded into Busabase by a trusted script — never a live monitoring integration, never a real gateway or real agents.
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Set up observability for AI systems. TRIGGERS - Use when user needs help with ai-observability-setup related tasks.
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Implements comprehensive observability with OpenTelemetry tracing, Prometheus metrics, and structured logging. Includes instrumentation plans, sample dashboards, and alert candidates. Use for "observability", "monitoring", "tracing", or "metrics".
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Complete observability & reliability engineering system. Use when designing monitoring, implementing structured logging, setting up distributed tracing, building alerting systems, creating SLO/SLI frameworks, running incident response, conducting post-mortems, or auditing system reliability. Covers all three pillars (logs/metrics/traces), alert design, dashboard architecture, on-call operations, chaos engineering, and cost optimization.
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Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.
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Set up monitoring for communication between services in Istio or Linkerd environments. Combine metrics, logs, and distributed traces, which follow requests across services, to investigate delays and errors, visualize dependencies, and define service quality targets.
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Set up Langfuse to record LLM application activity, costs, and response times. Manage prompt versions, organize evaluation datasets, and compare results to investigate failures and guide improvements using recorded evidence.
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Audit deployed Vercel projects for cost and performance improvements using production metrics. Examines usage, settings, and relevant code, then produces ranked recommendations supported by verified files and documentation matching the framework version.
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Define service reliability targets, choose measurements that reflect user experience, and calculate error budgets—the failures a target allows. Generate alert thresholds and review existing definitions for unrealistic targets, unclear metrics, and missing response policies.
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Find the right skill among 37 advanced engineering options for architecture, development, automation, and operations. The index maps tasks to individual skills, helping you choose which instructions to load for a specific job.
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Investigate Google Cloud network problems using traffic logs, performance metrics, and connection path tests. Identify blocked traffic, packet loss, and latency, summarize traffic volumes, or estimate VPC Flow Logs costs.
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Configure GKE logging, metrics collection, dashboards, and alerts with Cloud Logging, Cloud Monitoring, and managed Prometheus. Diagnose missing measurements, collection failures, permission errors, and alert rule problems to identify appropriate configuration changes.
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Investigate failing or slow BigQuery jobs and unexpected cost increases. Compare execution details and resource usage with a normal baseline to isolate bottlenecks, identify likely causes, and determine practical remediation steps.
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Set up KubeSphere’s WizTelemetry Data Pipeline to collect, transform, and route logs, audit data, events, and notifications. Configure delivery to OpenSearch, deploy agents across selected clusters, and check their operating status.
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