Public repository for Datadog Agent Skills
安装
npx skills add datadog-labs/agent-skillsgh skill install datadog-labs/agent-skillsgit clone --depth 1 https://github.com/datadog-labs/agent-skills合集内的技能
- Agent InstallDatadog LabsInstall 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.
- Agent InstallDatadog LabsInstall the Datadog Agent on Linux hosts via SSH with Single Step Instrumentation (SSI) enabled — SSI automatically instruments applications for APM without code changes. Only use if no agent is installed yet.
- Agent Observability Auto ExperimentDatadog LabsRun an iterative code-improvement hill-climb against real Datadog LLM-Obs data, locally, with Claude Code as the agent. Establishes a baseline eval, makes one focused change, re-scores with the same harness, keeps the change if it improves the score in the goal's direction (labeling within-noise gains tentative), and repeats.
- Agent Observability Build Eval From AnnotationsDatadog LabsFit a Datadog LLM-Obs evaluator to human labels. Takes an annotation queue, works out where in the trace the labelled property actually lives, drafts an LLM-judge that predicts the human label, scores that judge against the already-labelled rows with a metric agreed with the user, then hill-climbs it — inspect the errors, make one focused change, re-score, keep it only if it beats the best — for…
- Agent Observability Eval BootstrapDatadog LabsBootstrap 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.
- Agent Observability Eval PipelineDatadog LabsEnd-to-end Agent Observability pipeline for an instrumented ml_app — 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.
- Agent Observability Experiment AnalyzerDatadog LabsAnalyze 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.
- Agent Observability Experiment BootstrapDatadog LabsBootstrap a reproducible LLM Observability experiment through the Python ddtrace SDK or the Node dd-trace SDK. Use for experiment, dataset, evaluator, benchmark, regression, or LLM-as-a-judge scaffolding. The legacy Python invocation remains supported.
- Agent Observability Online ExperimentDatadog LabsLoad this skill when the user wants to run an online experiment (live-traffic A/B test) on an LLM application instrumented with Agent Observability: compare two versions of a prompt, model, or behavior using a Datadog feature flag, score metrics from evaluations, and cost and token metrics.
- Agent Observability Replay TraceDatadog LabsUse 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].
- Agent Observability Session ClassifyDatadog LabsClassify whether user intent was satisfied in a Datadog Agent Observability trace or session. Three modes: (1) session_id — classify a single CMD+I assistant session with RUM; (2) trace_id — classify a single Agent Observability trace without RUM; (3) ml_app — sample and classify multiple sessions or traces from a given LLM app. Output is compact by default (verdict + one-sentence reason).
- Agent Observability Trace RcaDatadog LabsRoot 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.
- Agent SkillsDatadog LabsDatadog skills for AI agents. Essential monitoring, logging, tracing and observability.
- Datadog AppDatadog LabsGuides developers building Datadog Apps with TypeScript, React, the @datadog/apps scaffolder, and @datadog/vite-plugin. Use when a user wants to scaffold, run, debug, upgrade, build, upload, publish, upload without publishing (draft upload), add an upload-no-publish script, set up CI/CD, use OAuth or API/application key auth, trigger/poll Workflow Automation, choose DDSQL or Action Catalog for…
- Dd Account SetupDatadog LabsEnsure the user has an authenticated Datadog account with a valid DD_API_KEY on the right region before any Datadog setup or instrumentation. Detects existing DD_API_KEY / DD_APP_KEY / DD_SITE, validates them against the Datadog API, and fixes the common wrong-region 403. If no usable key exists, signs the user in (OAuth) or creates a new account, then obtains and validates a key.
- Dd Android SdkDatadog LabsRoute Datadog Android SDK upgrades to the migration instructions for the project's current and target major versions.
- Dd Android Sdk V3 UpgradeDatadog LabsUpgrade an Android application from Datadog's v2 mobile RUM SDK to the latest stable v3 SDK, including Core, RUM, Logs, Trace, OkHttp, and Session Replay breaking changes.
- Dd ApmDatadog LabsAPM - 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.
- Dd AuditDatadog LabsAudit Trail investigations - who changed what, key compromise, cost spike root cause, compliance evidence (SOC 2/PCI), and AI activity auditing.
- Dd Audit Ai ActivityDatadog LabsAudit what the Bits AI assistant (MCP server) has done in your Datadog org — tool calls by user, resources accessed, and anomaly flags for AI governance.
- Dd Audit Compliance ReportDatadog LabsGenerate 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.
- Dd Audit Cost Spike InvestigationDatadog LabsInvestigate a Datadog product usage or cost spike by correlating Usage Metering data (when/what spiked) with Audit Trail config changes (who changed what in the preceding window).
- Dd Audit Key CompromiseDatadog LabsInvestigate a potentially compromised Datadog API key — timeline of actions, geo/IP breakdown, endpoints called, anomaly flags, and remediation steps.
- Dd Audit Security InvestigationDatadog LabsAnswer "who did what" security questions from Audit Trail — deletions, config changes, login activity, permission changes, actions from a specific user or IP.
- Dd AWS IntegrationDatadog LabsSet up the Datadog AWS integration with Terraform - creates the cross-account IAM role Datadog assumes (external ID, no stored credentials), attaches the permission policies Datadog publishes, and registers the account through datadog_integration_aws_account so AWS metrics, the resource catalog, and CSPM findings start flowing.
- Dd Azure IntegrationDatadog LabsSet up the Datadog Azure integration with Terraform - creates an Entra ID app registration and service principal, assigns Monitoring Reader across the chosen subscriptions and management groups, grants the Microsoft Graph permissions Datadog needs for resource discovery, and registers the tenant so Azure metrics and resource collection start flowing.
- Dd Browser SdkDatadog LabsDatadog 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.
- Dd DocsDatadog LabsDatadog docs lookup using docs.datadoghq.com/llms.txt and linked Markdown pages.
- Dd Gcp IntegrationDatadog LabsSet up the Datadog Google Cloud integration with Terraform - creates a service account in the host project, lets Datadog's delegate principal impersonate it via roles/iam.serviceAccountTokenCreator (no service-account keys), enables the required APIs, grants the monitoring roles across the chosen projects and folders, and registers the account through datadog_integration_gcp_sts.
- Dd Instrument LlmoDatadog LabsInstrument the current project with Datadog LLM Observability for Python or Node.js/Next.js backends that call LLMs or run AI agents. Detects the runtime and LLM framework, provisions credentials, adds SDK init (ddtrace/dd-trace) with the correct kwargs, persists the dependency into the deploy manifest, and audits session-ID plumbing for gaps — fixing them when found.
- Dd Instrument RumDatadog LabsInstrument browser-based web applications with Datadog Browser RUM. Detect the application framework, router, package manager, bundler, entrypoint, credentials, and existing RUM setup; add or safely complete classic Browser RUM instrumentation for React, Next.js App or Pages Router, Angular, Vue, Nuxt, Svelte, vanilla JavaScript, SPAs, and iframe-hosted apps; avoid duplicate initialization; and…
- Dd LogsDatadog LabsLog management - search, archives, metrics, and cost control.
- Dd MonitorsDatadog LabsMonitor management - list, search, file-based create, and alerting best practices.
- Dd Oci IntegrationDatadog LabsSet up the Datadog Oracle Cloud Infrastructure (OCI) integration with Terraform - verifies ~/.oci/config, then applies Datadog's official oracle-cloud-integration module to create the Datadog service user, group, IAM policies, and API key in the tenancy and register it with Datadog, optionally including log collection.
- Dd OrchestratorDatadog LabsEntry point for Datadog onboarding. Takes a developer's plain-language goal, ensures a valid Datadog account with dd-account-setup, asks dd-product-recommender which products fit, detects the project's platform and cloud, then composes an ordered plan across the existing skills (agent install, product enable, verify, and optional cloud integration) and dispatches to each by source URL — honestly…
- Dd Product RecommenderDatadog LabsRecommends the right Datadog products for a codebase and/or a stated goal — grounded in a tech-stack→product map and a use-case→product map built from Datadog product capabilities and common technology patterns. Recommendation only; no setup instructions.
- Dd PupDatadog LabsDatadog CLI (Rust). OAuth2 auth with token refresh.
- Enable DsmDatadog LabsEnable Data Streams Monitoring (DSM) on services already instrumented with APM, for end-to-end latency, throughput, and consumer lag across Kafka, RabbitMQ, SQS, SNS, Kinesis, Pub/Sub, IBM MQ, Azure Service Bus, and BullMQ pipelines.
- Enable SsiDatadog LabsEnable Single Step Instrumentation (SSI) on Kubernetes — automatically instruments applications for APM without code changes. Only use if the Datadog Agent is already running on the cluster — if not, use agent-install first.
- Enable SsiDatadog LabsConfigure Unified Service Tags and verify Single Step Instrumentation (SSI) injection on Linux hosts — SSI automatically instruments applications for APM without code changes. Only use if the Datadog Agent is already installed.
- K9 Ownership Byod SetupDatadog LabsGenerate a BYOD ownership preferences reference table for a customer. Walks through preference types, generates CSV, and provides upload instructions (UI, API, cloud storage, or Terraform). Use when asked about BYOD setup, preferences reference table, k9_ownership_preferences, or ownership customization.
- Onboarding SummaryDatadog LabsGenerate a live Single Step Instrumentation (SSI) onboarding confirmation report — verifies APM instrumentation is working end-to-end with deep links into the Datadog UI. Only use after agent-install and enable-ssi have both completed successfully.
- Onboarding SummaryDatadog LabsGenerate a live Single Step Instrumentation (SSI) onboarding confirmation report for Linux hosts — verifies APM instrumentation is working end-to-end with deep links into the Datadog UI. Only use after agent-install and enable-ssi have both completed.
- Service RemappingDatadog LabsCreate 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.
- Setup Browser Sdk SourcemapsDatadog LabsConfigure build-time JavaScript sourcemap upload to Datadog using the Datadog build plugin (@datadog/esbuild-plugin, @datadog/rollup-plugin, @datadog/rspack-plugin, @datadog/vite-plugin, @datadog/webpack-plugin), so RUM and Error Tracking show un-minified stack traces with git metadata attached.
- Triage Flaky TestDatadog LabsLoad when investigating a specific flaky test. Gets history, failure pattern, and category, then recommends fix, quarantine, or escalate.
- Troubleshoot SsiDatadog LabsDiagnose and fix Single Step Instrumentation (SSI) issues on Kubernetes — SSI automatically instruments applications for APM without code changes. Only use if the agent and SSI are already configured but traces are missing or instrumentation is not working.
- Troubleshoot SsiDatadog LabsDiagnose and fix Single Step Instrumentation (SSI) issues on Linux hosts — SSI automatically instruments applications for APM without code changes. Only use if the agent and SSI are configured but traces are missing or instrumentation is not working.