Monitoring & Observability Agent Skills

Design, review, and operate SLOs, SLIs, and error budgets with multi-window burn-rate alerts and automated validation.

Analyze engineering delivery throughput, hiring funnel health, team structure, and production discipline for startup VPEs and founders.

Design, run, and learn from safe chaos engineering experiments with structured plans, blast radius calculation, and postmortem generation.

Generate operational runbooks from a service name — deployment, incident response, maintenance, and rollback workflows. Templated structure customizable per environment.

Systematically profile Node.js, Python, and Go applications to identify CPU, memory, and I/O bottlenecks, generate flamegraphs, analyze bundle sizes, optimize database queries, and run load tests with k6 and Artillery.

Design production-ready observability strategies combining metrics, logs, and traces, including SLI/SLO design, golden-signals monitoring, and alert optimization.

Evaluates Google Cloud workloads against the Reliability pillar of the Well-Architected Framework, providing actionable recommendations for building, deploying, and managing reliable systems.

Configures dynamic threshold alerting policies for Google Cloud Vertex AI Agent Platform agents, monitoring latency, error rates, and quality metrics using Terraform and PromQL.

Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics, including VPC Flow Logs, NAT, firewall, threat logs, latency, throughput, and Connectivity Tests.

Generates operations-focused guidance for Google Cloud workloads based on the Operational Excellence pillar of the Well-Architected Framework, including assessment questions and validation checklists.

Captures and analyzes Windows debug output (OutputDebugString, DbgPrint/KdPrint) from the command line, with filtering, bounded execution, and remote monitoring.

Debug and troubleshoot Azure production issues using AppLens, Azure Monitor, resource health, and systematic diagnosis flows for services like App Service, Functions, AKS, Container Apps, and Messaging.

Query logs and metrics from Azure Monitor and Log Analytics workspaces using the Python SDK.

Provides guidance and reference material for instrumenting web applications with Azure Application Insights, including telemetry patterns, SDK setup, and configuration references.

Send custom logs to Azure Monitor via Data Collection Rules and Data Collection Endpoints using the Java SDK.

Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API and Python SDK.

Configures Azure Monitor Application Insights with OpenTelemetry auto-instrumentation for Python applications in one line.

Auto-instrument Node.js applications with distributed tracing, metrics, and logs using Azure Monitor and OpenTelemetry.

Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights using the deprecated exporter or the recommended autoconfigure package.

Export OpenTelemetry traces, metrics, and logs to Azure Application Insights using Python.

Monitor AWS resources and applications using CloudWatch logs, metrics, alarms, and dashboards with CLI and boto3 examples.

Run config-driven performance benchmarks against a deployed NVIDIA RAG Blueprint server, including profiling and load testing, with a unified report.

Captures a read-only health snapshot from a Jetson device, reporting identity, memory, GPU, thermal, power, storage, services, and top processes.

Operate the VSS alert pipeline for real-time monitoring, Alert-Bridge subscriptions, Slack notifications, incident queries, and camera onboarding.

Diagnose failed or unhealthy Dynamo deployments by collecting a read-only debug bundle, classifying failures, and providing step-by-step remediation guidance.

Measure Jetson DRAM and NvMap usage, capture before/after baselines, and verify memory reclamation with live audit data.

Capture a full DevTools-protocol trace of any browser automation, bisect the stream into per-page searchable buckets, and attach a trace to an in-progress session for debugging.

Monitors a deployed URL for regressions after releases by checking HTTP status, console errors, network failures, performance metrics, content integrity, API health, static assets, and SSE streams.

Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.

Build monitoring dashboards that answer real operator questions for Grafana, SigNoz, and similar platforms, turning metrics into actionable operational views.

Produces an evidence-backed inventory of live automations (jobs, hooks, connectors, MCP servers, wrappers) and recommends keep/merge/cut/fix-next actions before rewriting anything.

Audits ECC Tools GitHub App for cost issues like runaway PR creation, quota bypass, premium-model leakage, and duplicate jobs, using an evidence-first workflow.

Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.

Provides expertise in Langfuse for LLM observability, including tracing, prompt management, evaluation, and integration with LangChain, LlamaIndex, and OpenAI.

Inspect Sentry issues, events, and production errors using the Sentry CLI for read-only observability queries.

Configure and collect crash dumps for modern .NET applications (CoreCLR and NativeAOT) on Linux, macOS, and Windows, including containers.