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Found 478 Skills
Log queries, filtering, pattern analysis, and log correlation. Search and analyze application and infrastructure logs.
Kubernetes clusters, pods, nodes, workloads, storage, networking, and resource relationships. Query K8s inventory, diagnose degraded deployments and pod failures, investigate rollouts, audit ingress and network policies.
Full Sentry SDK setup for Node.js, Bun, and Deno. Use when asked to "add Sentry to Node.js", "add Sentry to Bun", "add Sentry to Deno", "install @sentry/node", "@sentry/bun", or "@sentry/deno", or configure error monitoring, tracing, logging, profiling, metrics, crons, or AI monitoring for server-side JavaScript/TypeScript runtimes.
DevOps and Infrastructure expert with comprehensive knowledge of CI/CD pipelines, containerization, orchestration, infrastructure as code, monitoring, security, and performance optimization. Use PROACTIVELY for any DevOps, deployment, infrastructure, or operational issues. If a specialized expert is a better fit, I will recommend switching and stop.
List Langfuse sessions. Use when checking user sessions, analyzing conversation flows, or monitoring session activity.
LLM cost tracking with Langfuse for cached responses. Use when monitoring cache effectiveness, tracking cost savings, or attributing costs to agents in multi-agent systems.
View Langfuse trace details. Use when checking specific trace input/output, debugging LLM calls, or analyzing costs.
Automatically discover eBPF and kernel skills when working with eBPF, kernel tracing, XDP, kprobes, BPF, Linux kernel, or network filtering. Activates for eBPF development tasks.
Code-first Netra best-practices playbook covering setup, instrumentation, context tracking, custom spans/metrics, integration patterns, evaluation, simulation, and troubleshooting.
Problem entities, root cause analysis (RCA), impact assessment, and problem correlation. Query and analyze Dynatrace-detected problems and incidents.
Build monitoring dashboards that answer real operator questions for Grafana, SigNoz, and similar platforms. Use when turning metrics into a working dashboard instead of a vanity board.
Read production traces, identify what's failing, and build failure taxonomies using open coding and axial coding methodology. Use when debugging agent or pipeline quality, investigating "why are my outputs bad?", or before building any evaluator — error analysis must come first. Do NOT use when you already have identified failure modes and need evaluators (use build-evaluator) or datasets (use generate-synthetic-dataset).