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Found 59 Skills
Use the `datadog` CLI to manage Datadog resources — monitors, metrics, events, logs, services, errors, and pipelines. Invoke this skill whenever the user asks to query, create, update, or delete Datadog monitors, search logs or errors, check metric values, list APM services, or manage log pipelines. Also trigger when the user mentions Datadog observability tasks like "check the error rate", "look at monitors", "search logs for errors", "list services", or "set up a log pipeline".
Monitor infrastructure, APM, logs, and metrics on Datadog
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.
Expert skill for Datadog Observability & Security Platform
Install the Datadog Agent on Kubernetes using the Datadog Operator — required before enabling Single Step Instrumentation (SSI), which automatically instruments applications for APM without code changes. Only use if no Datadog Agent is deployed on the cluster yet.
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.
Use when a developer wants to iterate on ONE specific Agent Observability / LLM Obs trace whose output they didn't like — re-running that trace against their LOCAL code, seeing a concise diff of the old vs new output, and looping (change code → replay → diff) until satisfied. Invoked as /agent-observability-replay-trace <trace-id> [changes to test]. Signals: "replay this trace"; "iterate on a trace"; "this trace's output is wrong, fix it and re-run"; "re-run trace <id> with <change>"; pasting a trace id from the Agent Observability UI with a description of what to fix. It fetches the trace via the datadog-llmo MCP or the pup CLI, edits code, re-runs the app to emit a NEW trace, and diffs the two — no local server, no browser. For agents traced with ddtrace / LLM Obs (Python first-class), with JSON-serializable entry input. Do NOT use for: scored Experiments or the browser "Replay" button (that's agent-observability-replay-experiment), building an experiment from a dataset/CSV, writing evaluators, root-causing failed traces, or RUM/HTTP session replay.
Datadog docs lookup using docs.datadoghq.com/llms.txt and linked Markdown pages.
Research Langfuse production telemetry with reusable Datadog queries. Use for tenant or project activity, API usage, queue behavior, spans, logs, metrics, or ad hoc measurements across production regions; pair with debug-issue-with-datadog for root-cause analysis.
Full-stack observability with Datadog APM, logs, metrics, synthetics, and RUM. Use when implementing monitoring, tracing, alerting, or cost optimization for production systems.
Automate Datadog tasks via Rube MCP (Composio): query metrics, search logs, manage monitors/dashboards, create events and downtimes. Always search tools first for current schemas.
Datadog CLI (Go). OAuth2 auth with token refresh.