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Found 42 Skills
Analyse Datadog observability data including metrics, logs, monitors, incidents, SLOs, APM traces, RUM, security signals, and more. Use when asked to investigate infrastructure health, query metrics, search logs, check monitors, diagnose errors, or analyse any Datadog data.
Instrument a Python application with the Elastic Distribution of OpenTelemetry (EDOT) Python agent for automatic tracing, metrics, and logs. Use when adding observability to a Python service that has no existing APM agent.
This skill should be used when the user asks to "investigate an issue", "debug a problem", "find out why something is slow", "check error rates", "analyze user behavior", "understand a production incident", "query telemetry data", "look at logs", "check traces", "examine spans", "analyze RUM data", "check frontend performance", "investigate backend latency", "find transaction data", "check payment metrics", "analyze user journeys", or wants to answer questions using observability data from logs, metrics, traces, RUM, or APM - this is the gateway skill for deciding where to look first.
Grafana Cloud Database Observability — query-level performance insights for MySQL and PostgreSQL. Covers setup with Grafana Alloy, query samples, visual explain plans, RED metrics, pg_stat_statements and Performance Schema integration, and correlation with application traces. Use when monitoring database performance, diagnosing slow queries, setting up database observability for MySQL or PostgreSQL (self-managed, RDS, Aurora, Azure, Cloud SQL), or correlating DB metrics with APM data.
Grafana Cloud Application Observability (APM), Frontend Observability (RUM/Faro), and AI Observability. Covers RED metrics (Rate/Error/Duration), service maps, span metrics from traces, Faro JavaScript/React SDK for browser instrumentation, session replay, AI/LLM model monitoring, and integration with traces/logs/profiles for full-stack correlation. Use when setting up APM, configuring frontend monitoring, analyzing service performance, or monitoring AI/LLM applications.
Activate when reviewing or modifying dependency resolution, lockfile schema, package downloaders, signature/integrity checks, file integration cleanup, or anything that could expose APM to dependency confusion, typosquatting, malicious packages, or token leakage.
Query and analyze Datadog logs, metrics, APM traces, and monitors using the Datadog API. Use when debugging production issues, monitoring application performance, or investigating alerts.
Review existing Datadog dashboards for operational readiness. Audits alert threshold markers, threshold proximity to normal traffic, customer-facing section completeness, and zero-knowledge readability. Uses pup CLI to fetch dashboard definitions. Use when auditing dashboards before on-call handoff, after dashboard changes, or during operational reviews. Do not use for: (1) designing new dashboards from scratch, (2) monitor/alert rule design, (3) APM instrumentation or tracing setup, (4) log pipeline configuration.
Production server monitoring stack covering Prometheus, Node Exporter, Grafana, Alertmanager, Loki, and Promtail on bare-metal or VM Linux hosts. USE WHEN: - Setting up monitoring for a new production server or VPS - Configuring Prometheus scrape targets for application or system metrics - Creating Grafana dashboards and datasource provisioning - Writing Alertmanager routing rules with email/Slack notifications - Implementing the PLG stack (Promtail + Loki + Grafana) for log aggregation - Performing live system diagnostics with htop, iotop, nethogs, ss, vmstat, iostat - Setting up uptime monitoring with UptimeRobot or healthchecks.io DO NOT USE FOR: - Kubernetes-native observability (use the kubernetes skill instead) - Application-level APM (distributed tracing with Jaeger/Tempo — use observability skill) - Cloud-managed monitoring (CloudWatch, GCP Monitoring, Azure Monitor) - Windows Server monitoring
Diagnose why a SigNoz alert fired by correlating the alert's own signal with neighbor signals (error rate, latency, throughput, CPU/memory), traces, and logs around the fire window — and rank likely causes. Make sure to use this skill whenever the user asks "why did this alert fire", "what caused alert X", "investigate this alert", "RCA for the alert that paged me", "what's wrong with [service]" in the context of a recent fire, or otherwise asks for a root-cause analysis of a firing or recently-fired alert. Read-only — does not modify any alert or notification.
Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.
New Relic integration. Manage Accounts. Use when the user wants to interact with New Relic data.