agent-skills

Datadog CLI (Rust). OAuth2 auth with token refresh.

Datadog skills for AI agents. Essential monitoring, logging, tracing and observability.

Log management - search, archives, metrics, and cost control.

APM - install, onboard, instrument, enable, set up, configure, traces, services, dependencies, performance analysis, Data Streams Monitoring (DSM), queue lag, pipeline latency. Use for any request involving Datadog APM setup, instrumentation (SSI, ddtrace, agent install), or analysis.

Monitor management - list, search, file-based create, and alerting best practices.

Datadog docs lookup using docs.datadoghq.com/llms.txt and linked Markdown pages.

Audit Trail investigations - who changed what, key compromise, cost spike root cause, compliance evidence (SOC 2/PCI), and AI activity auditing.

Datadog Browser SDK — RUM, Logs, Session Replay, profiling, product analytics, and error tracking setup, configuration, and migration. Use when upgrading Browser SDK versions, setting up RUM or Logs, or troubleshooting browser-side Datadog instrumentation.

Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says "what's wrong with my app", "why is my eval failing", "analyze errors", "root cause analysis", "diagnose failures", or wants to understand production failure patterns.

Bootstrap evaluators from production traces — by default propose online LLM-judge evaluators and, after you confirm, create them in Datadog as disabled drafts (never auto-enabled); on request emit Python SDK code or a framework-agnostic JSON spec instead. Use when user says "bootstrap evaluators", "generate evaluators", "create evals from traces", "eval bootstrap", "write evaluators", "build eval suite", "publish evaluators", or wants to generate BaseEvaluator/LLMJudge code or online judge configs from production LLM trace data. Works with mlapp and optional RCA report or failure hypothesis.

End-to-end Agent Observability pipeline for an instrumented mlapp — classify production traces, root-cause failures, bootstrap evaluators, then (optionally) sample + publish a dataset, generate + run an experiment, and analyze results. Six narrated phases with a standardized banner and a "continue" checkpoint between each. Pure orchestration over the agent-observability sub-skills (agent-observability-session-classify, agent-observability-trace-rca, agent-observability-eval-bootstrap, agent-observability-experiment-bootstrap, agent-observability-experiment-analyzer). Use when user says "run the eval pipeline", "go from traces to evals", "bootstrap evals end to end", "classify then RCA then bootstrap", "build an eval set from scratch", "onboard me to datasets and experiments", "walk me through experiments", "I have an mlapp, now what", "Agent Observability onboarding", "guided experiment setup", "from traces to experiments", or wants a deterministic, narrated tour from produ

Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says "analyze experiment", "compare experiments", "analyze against baseline", or provides one or two experiment IDs for analysis.

Classify whether user intent was satisfied in a Datadog Agent Observability trace or session. Three modes: (1) sessionid — classify a single CMD+I assistant session with RUM; (2) traceid — classify a single Agent Observability trace without RUM; (3) mlapp — sample and classify multiple sessions or traces from a given LLM app. Output is compact by default (verdict + one-sentence reason). Use when evaluating satisfaction, classifying sessions/traces, labeling data, or generating signal for agent-observability-eval-pipeline or agent-observability-trace-rca.

Create and manage APM service remapping rules — rewrite service names at ingestion time to collapse noisy inferred entities, clean up auto-generated names, handle org renames, or normalize naming conventions. Use for any request involving service renaming, service mapping, inferred service cleanup, peer.service normalization, or collapsing fragmented service names.

Generate auditor-ready compliance evidence from Datadog Audit Trail for SOC 2 and PCI DSS. Maps framework controls to specific query patterns and produces formatted output.

Investigate a potentially compromised Datadog API key — timeline of actions, geo/IP breakdown, endpoints called, anomaly flags, and remediation steps.