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Use for any request involving Datadog APM setup, instrumentation (SSI, ddtrace, agent install), or analysis.\nalwaysApply: true\nmetadata:\n  version: \"1.1.0\"\n  author: datadog-labs\n  repository: https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fagent-skills\n  tags: datadog,apm,tracing,performance,distributed-tracing,dd-apm,install,onboarding,instrumentation,ssi,agent\n  globs: \"**\u002Fddtrace*,**\u002Fdatadog*.yaml,**\u002F*trace*\"\n---\n\n# Datadog APM\n\nDistributed tracing, service maps, and performance analysis.\n\n## Routing — Read This First\n\nMatch the user's request to one of the entries below. Each entry has the same shape: **triggers** → which sub-skill to load → the anti-pattern to avoid. If a request seems to fit more than one entry, see \"Overlap disambiguation\". If nothing matches, see \"None of the above\" at the end.\n\n---\n\n**Kubernetes APM install \u002F instrument \u002F onboard** — trigger when the user mentions Kubernetes, K8s, EKS, GKE, AKS, kind, minikube, K3s, helm, DatadogAgent CR, kubectl, SSI on a cluster, pod injection, or init containers.\n\n**Immediately read** `.claude\u002Fskills\u002Fdd-apm\u002Fk8s-ssi\u002Fagent-install\u002FSKILL.md` now, then `.claude\u002Fskills\u002Fdd-apm\u002Fk8s-ssi\u002Fenable-ssi\u002FSKILL.md`, then `.claude\u002Fskills\u002Fdd-apm\u002Fk8s-ssi\u002Fverify-ssi\u002FSKILL.md` — do not proceed from memory.\n\n> **Common wrong approaches that LOOK like they work but silently fail:**\n> - `helm install datadog datadog\u002Fdatadog` — the standard chart does NOT support SSI via DatadogAgent CR.\n> - Adding `ddtrace` imports or `ddtrace-run` to the app — SSI auto-instruments WITHOUT any code changes.\n> - `admission.datadoghq.com\u002Fenabled` annotations — that's admission controller config injection, not SSI init container injection.\n\n---\n\n**Linux APM install \u002F instrument \u002F onboard** — trigger when the user mentions a single host, VM, EC2 instance, bare-metal, RHEL\u002FUbuntu\u002FDebian, systemd, or no orchestrator.\n\n**Immediately read** `.claude\u002Fskills\u002Fdd-apm\u002Flinux-ssi\u002Fagent-install\u002FSKILL.md` now, then `.claude\u002Fskills\u002Fdd-apm\u002Flinux-ssi\u002Fenable-ssi\u002FSKILL.md`, then `.claude\u002Fskills\u002Fdd-apm\u002Flinux-ssi\u002Fverify-ssi\u002FSKILL.md` — do not proceed from memory.\n\n> **Do NOT** install the agent via plain `apt-get install datadog-agent` (or yum equivalent) and assume SSI follows — host auto-instrumentation requires the install script with the SSI flags, which the sub-skill walks through.\n\n---\n\n**Service rename \u002F service remapping** — trigger when the user mentions renaming a service, collapsing multiple service names, stripping suffixes\u002Fprefixes, or cleaning up inferred services.\n\n**Immediately read** `.claude\u002Fskills\u002Fdd-apm\u002Fservice-remapping\u002FSKILL.md` now — do not proceed from memory.\n\n> **Do NOT** change `tags.datadoghq.com\u002Fservice` labels or `DD_SERVICE` env vars to rename a service in Datadog. That requires a rollout and only affects new data. Use a service remapping rule — it rewrites the name at ingestion time with no deployment change.\n\n---\n\n### Overlap disambiguation\n\nWhen a request could plausibly fit more than one entry above, use these tiebreakers:\n\n| Hint | Route to |\n|---|---|\n| Cluster orchestrator mentioned (EKS\u002FGKE\u002FAKS\u002Fkind\u002FK3s\u002Fminikube) — even if \"just one node\" | k8s-ssi |\n| Single host, VM, or EC2 with no orchestrator | linux-ssi |\n| \"Several services that should be one\" | service-remapping — the sub-skill picks the rule type based on whether the duplicates are real instrumented services or inferred entities (DBs, queues, external APIs) |\n| \"My service shows under the wrong name\" | First check `DD_SERVICE` on the deploy. If correct and the name is still wrong → service-remapping. |\n| \"Reduce APM volume \u002F cost \u002F noise\" | No sub-skill yet. Ask whether the user means sampling (fewer ingested traces) or retention filters (less indexed data) before suggesting commands. |\n\n---\n\n### None of the above\n\nIf the request doesn't match any entry above, continue reading the trace-search, service analysis, and metrics content below. If even that doesn't fit, **ask the user to clarify** — do not invent a workflow.\n\n---\n\n## Requirements\n\nDatadog Labs Pup should be installed. See [Setup Pup](https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fagent-skills\u002Ftree\u002Fmain?tab=readme-ov-file#setup-pup) if not.\n\n## Command Execution Order (Token-Efficient)\n\nFor scoped commands, use this order:\n\n1. Check context first (prior outputs, conversation, saved values).\n2. If a required value is missing, run a discovery command first.\n3. If still ambiguous, ask the user to confirm.\n4. Then run the target command.\n5. Avoid speculative commands likely to fail.\n\n## Quick Start\n\n```bash\npup auth login\n# Confirm env tag with the user first (do not assume production\u002Fprod\u002Fprd).\npup apm services list --env \u003Cenv> --from 1h --to now\npup traces search --query \"service:api-gateway\" --from 1h\n```\n\n## Services\n\n### List Services\n\n```bash\npup apm services list --env \u003Cenv> --from 1h --to now\npup apm services stats --env \u003Cenv> --from 1h --to now\n```\n\n### Service Stats\n\n```bash\npup apm services stats --env \u003Cenv> --from 1h --to now\n```\n\n### Service Map\n\n```bash\n# View dependencies\npup apm flow-map --query \"service:api-gateway&from=$(($(date +%s)-3600))000&to=$(date +%s)000\" --env \u003Cenv> --limit 10\n```\n\n## Traces\n\n### Search Traces\n\n```bash\n# By service\npup traces search --query \"service:api-gateway\" --from 1h\n\n# Errors only\npup traces search --query \"service:api-gateway status:error\" --from 1h\n\n# Slow traces (>1s)\npup traces search --query \"service:api-gateway @duration:>1000ms\" --from 1h\n\n# With specific tag\npup traces search --query \"service:api-gateway @http.url:\u002Fapi\u002Fusers\" --from 1h\n```\n\n### Trace Detail\n\n```bash\n# No direct get command for a single trace ID.\n# Use traces search with a narrow query and time window.\npup traces search --query \"trace_id:\u003Ctrace_id>\" --from 1h\n```\n\n## Key Metrics\n\n| Metric | What It Measures |\n|--------|------------------|\n| `trace.http.request.hits` | Request count |\n| `trace.http.request.duration` | Latency |\n| `trace.http.request.errors` | Error count |\n| `trace.http.request.apdex` | User satisfaction |\n\n## Service Level Objectives\n\nLink APM to SLOs:\n\n```bash\npup slos create --file slo.json\n```\n\n## Common Queries\n\n| Goal | Query |\n|------|-------|\n| Slowest endpoints | `avg:trace.http.request.duration{*} by {resource_name}` |\n| Error rate | `sum:trace.http.request.errors{*} \u002F sum:trace.http.request.hits{*}` |\n| Throughput | `sum:trace.http.request.hits{*}.as_rate()` |\n\n## Troubleshooting\n\n| Problem | Fix |\n|---------|-----|\n| No traces | Check ddtrace installed, DD_TRACE_ENABLED=true |\n| Missing service | Verify DD_SERVICE env var |\n| Traces not linked | Check trace headers propagated |\n| High cardinality | Don't tag with user_id\u002Frequest_id |\n\n## References\u002FDocs\n\n- [APM Setup](https:\u002F\u002Fdocs.datadoghq.com\u002Ftracing\u002F)\n- [Trace Search](https:\u002F\u002Fdocs.datadoghq.com\u002Ftracing\u002Ftrace_explorer\u002F)\n",{"data":37,"body":43},{"name":4,"description":6,"alwaysApply":38,"metadata":39},true,{"version":40,"author":8,"repository":26,"tags":41,"globs":42},"1.1.0","datadog,apm,tracing,performance,distributed-tracing,dd-apm,install,onboarding,instrumentation,ssi,agent","**\u002Fddtrace*,**\u002Fdatadog*.yaml,**\u002F*trace*",{"type":44,"children":45},"root",[46,55,61,68,81,85,95,130,190,193,203,232,253,256,266,282,310,313,320,325,425,428,434,446,449,455,471,477,482,511,517,678,684,690,808,814,876,882,1018,1024,1030,1257,1263,1326,1332,1422,1428,1433,1467,1473,1546,1552,1626,1632,1655],{"type":47,"tag":48,"props":49,"children":51},"element","h1",{"id":50},"datadog-apm",[52],{"type":53,"value":54},"text","Datadog APM",{"type":47,"tag":56,"props":57,"children":58},"p",{},[59],{"type":53,"value":60},"Distributed tracing, service maps, and performance analysis.",{"type":47,"tag":62,"props":63,"children":65},"h2",{"id":64},"routing-read-this-first",[66],{"type":53,"value":67},"Routing — Read This First",{"type":47,"tag":56,"props":69,"children":70},{},[71,73,79],{"type":53,"value":72},"Match the user's request to one of the entries below. 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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\u002FLLMJudge code or online judge configs from production LLM trace data. Works with ml_app and optional RCA report or failure hypothesis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1702,1703,1704,1705],{"name":20,"slug":21,"type":15},{"name":1689,"slug":1690,"type":15},{"name":1692,"slug":1693,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:19.259734",{"slug":1708,"name":1708,"fn":1709,"description":1710,"org":1711,"tags":1712,"stars":25,"repoUrl":26,"updatedAt":1722},"agent-observability-eval-pipeline","run agent observability and evaluation pipelines","End-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. Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-py-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 ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from production data through evaluators, datasets, and experiments. 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Use when the user says \"generate Python experiment\", \"write an SDK experiment\", \"create a ddtrace experiment\", \"Python notebook experiment\", \"use the Agent Observability SDK\", or has `ddtrace` installed and wants idiomatic SDK code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1743,1744,1747,1748,1749],{"name":20,"slug":21,"type":15},{"name":1745,"slug":1746,"type":15},"Jupyter","jupyter",{"name":1692,"slug":1693,"type":15},{"name":13,"slug":14,"type":15},{"name":1750,"slug":1751,"type":15},"Python","python","2026-06-19T09:04:22.640384",{"slug":1754,"name":1754,"fn":1755,"description":1756,"org":1757,"tags":1758,"stars":25,"repoUrl":26,"updatedAt":1766},"agent-observability-replay-trace","iterate on LLM traces with local code","Use when a developer wants to iterate on ONE specific Agent Observability \u002F 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 \u002Fagent-observability-replay-trace \u003Ctrace-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 \u003Cid> with \u003Cchange>\"; 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 \u002F LLM Obs (Python first-class), with JSON-serializable entry input. 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