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Each populates a row in the final report.\n\n### Claude runs\n\n```bash\n# Agent pod count and status\nkubectl get pods -n \u003CAGENT_NAMESPACE> \\\n  -l app.kubernetes.io\u002Fcomponent=agent \\\n  --no-headers\n\n# SSI instrumentation config live in cluster\nkubectl get datadogagent datadog -n \u003CAGENT_NAMESPACE> \\\n  -o jsonpath='{.spec.features.apm.instrumentation}'\n\n# Init container confirmed in app pod spec\nkubectl get pod -l app=\u003CAPP_LABEL> -n \u003CAPP_NAMESPACE> \\\n  -o jsonpath='{.items[0].spec.initContainers[*].name}'\n\n# Service visible and traced in APM\nDD_SITE=\u003CDD_SITE> pup apm services list --env \u003CENV> --from 1h\n\n# Traces arriving in the last hour\nDD_SITE=\u003CDD_SITE> pup traces search --query \"service:\u003CSERVICE_NAME>\" --from 1h --limit 5\n```\n\n---\n\n## Present the report\n\nFill in every value from live command output. Do not leave any placeholder unfilled. 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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. Stop early with `--stop-after \u003Cphase>` to short-circuit at evaluators or dataset, or resume mid-flow with `--start-at \u003Cphase>`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1369,1372,1375,1376,1377],{"name":1370,"slug":1371,"type":15},"Agents","agents",{"name":1373,"slug":1374,"type":15},"Data Pipeline","data-pipeline",{"name":20,"slug":21,"type":15},{"name":1345,"slug":1346,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:17.231423",{"slug":1380,"name":1380,"fn":1381,"description":1382,"org":1383,"tags":1384,"stars":25,"repoUrl":26,"updatedAt":1392},"agent-observability-experiment-analyzer","analyze LLM experiment results","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1385,1388,1389,1390,1391],{"name":1386,"slug":1387,"type":15},"Analytics","analytics",{"name":20,"slug":21,"type":15},{"name":1345,"slug":1346,"type":15},{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:21.212498",{"slug":1394,"name":1394,"fn":1395,"description":1396,"org":1397,"tags":1398,"stars":25,"repoUrl":26,"updatedAt":1408},"agent-observability-experiment-py-bootstrap","generate Python experiment clients for LLM observability","Generates a self-contained Python experiment client that uses the ddtrace.llmobs SDK. Emits either a runnable .py script or a Jupyter .ipynb notebook matching the canonical DataDog reference notebook style. 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},[1399,1400,1403,1404,1405],{"name":20,"slug":21,"type":15},{"name":1401,"slug":1402,"type":15},"Jupyter","jupyter",{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},{"name":1406,"slug":1407,"type":15},"Python","python","2026-06-19T09:04:22.640384",{"slug":1410,"name":1410,"fn":1411,"description":1412,"org":1413,"tags":1414,"stars":25,"repoUrl":26,"updatedAt":1422},"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. Do NOT use for: scored Experiments or the browser \"Replay\" button (that's agent-observability-replay-experiment), building an experiment from a dataset\u002FCSV, writing evaluators, root-causing failed traces, or RUM\u002FHTTP session replay.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1415,1416,1417,1418,1419],{"name":20,"slug":21,"type":15},{"name":1342,"slug":1343,"type":15},{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},{"name":1420,"slug":1421,"type":15},"Tracing","tracing","2026-07-31T05:52:08.594182",{"slug":1424,"name":1424,"fn":1425,"description":1426,"org":1427,"tags":1428,"stars":25,"repoUrl":26,"updatedAt":1434},"agent-observability-session-classify","evaluate user intent satisfaction in sessions","Classify 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). Use when evaluating satisfaction, classifying sessions\u002Ftraces, labeling data, or generating signal for agent-observability-eval-pipeline or agent-observability-trace-rca.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1429,1430,1431,1432,1433],{"name":1386,"slug":1387,"type":15},{"name":20,"slug":21,"type":15},{"name":1345,"slug":1346,"type":15},{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:26.341497",{"slug":1436,"name":1436,"fn":1437,"description":1438,"org":1439,"tags":1440,"stars":25,"repoUrl":26,"updatedAt":1446},"agent-observability-trace-rca","diagnose LLM application failures from traces","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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1441,1442,1443,1444,1445],{"name":20,"slug":21,"type":15},{"name":1342,"slug":1343,"type":15},{"name":1345,"slug":1346,"type":15},{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:24.316244",{"slug":1448,"name":1448,"fn":1449,"description":1450,"org":1451,"tags":1452,"stars":25,"repoUrl":26,"updatedAt":1458},"agent-skills","use Datadog observability skills","Datadog skills for AI agents. Essential monitoring, logging, tracing and observability.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1453,1454,1457],{"name":20,"slug":21,"type":15},{"name":1455,"slug":1456,"type":15},"Monitoring","monitoring",{"name":13,"slug":14,"type":15},"2026-04-06T18:08:33.337476",{"slug":1460,"name":1460,"fn":1461,"description":1462,"org":1463,"tags":1464,"stars":25,"repoUrl":26,"updatedAt":1478},"datadog-app","build and manage Datadog applications","Guides developers building Datadog Apps with TypeScript, React, the @datadog\u002Fapps scaffolder, and @datadog\u002Fvite-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\u002FCD, use OAuth or API\u002Fapplication key auth, trigger\u002Fpoll Workflow Automation, choose DDSQL or Action Catalog for backend data access, or query app datastores with DDSQL, including backend function troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1465,1466,1469,1472,1475],{"name":20,"slug":21,"type":15},{"name":1467,"slug":1468,"type":15},"Frontend","frontend",{"name":1470,"slug":1471,"type":15},"React","react",{"name":1473,"slug":1474,"type":15},"TypeScript","typescript",{"name":1476,"slug":1477,"type":15},"Vite","vite","2026-06-18T08:01:32.562331",{"slug":1480,"name":1480,"fn":1481,"description":1482,"org":1483,"tags":1484,"stars":25,"repoUrl":26,"updatedAt":1493},"dd-apm","query Datadog APM traces","APM - install, onboard, instrument, enable, set up, configure, traces, services, dependencies, performance analysis. Use for any request involving Datadog APM setup, instrumentation (SSI, ddtrace, agent install), or analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1485,1486,1489,1490],{"name":20,"slug":21,"type":15},{"name":1487,"slug":1488,"type":15},"Distributed Tracing","distributed-tracing",{"name":13,"slug":14,"type":15},{"name":1491,"slug":1492,"type":15},"Performance","performance","2026-04-06T18:08:34.575282",35,{"items":1496,"total":1551},[1497,1504,1512,1519,1527,1535,1543],{"slug":1322,"name":1322,"fn":1323,"description":1324,"org":1498,"tags":1499,"stars":25,"repoUrl":26,"updatedAt":1333},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1500,1501,1502,1503],{"name":20,"slug":21,"type":15},{"name":1329,"slug":1330,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},{"slug":1335,"name":1335,"fn":1336,"description":1337,"org":1505,"tags":1506,"stars":25,"repoUrl":26,"updatedAt":1351},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1507,1508,1509,1510,1511],{"name":20,"slug":21,"type":15},{"name":1342,"slug":1343,"type":15},{"name":1345,"slug":1346,"type":15},{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},{"slug":1353,"name":1353,"fn":1354,"description":1355,"org":1513,"tags":1514,"stars":25,"repoUrl":26,"updatedAt":1362},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1515,1516,1517,1518],{"name":20,"slug":21,"type":15},{"name":1345,"slug":1346,"type":15},{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},{"slug":1364,"name":1364,"fn":1365,"description":1366,"org":1520,"tags":1521,"stars":25,"repoUrl":26,"updatedAt":1378},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1522,1523,1524,1525,1526],{"name":1370,"slug":1371,"type":15},{"name":1373,"slug":1374,"type":15},{"name":20,"slug":21,"type":15},{"name":1345,"slug":1346,"type":15},{"name":13,"slug":14,"type":15},{"slug":1380,"name":1380,"fn":1381,"description":1382,"org":1528,"tags":1529,"stars":25,"repoUrl":26,"updatedAt":1392},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1530,1531,1532,1533,1534],{"name":1386,"slug":1387,"type":15},{"name":20,"slug":21,"type":15},{"name":1345,"slug":1346,"type":15},{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},{"slug":1394,"name":1394,"fn":1395,"description":1396,"org":1536,"tags":1537,"stars":25,"repoUrl":26,"updatedAt":1408},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1538,1539,1540,1541,1542],{"name":20,"slug":21,"type":15},{"name":1401,"slug":1402,"type":15},{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},{"name":1406,"slug":1407,"type":15},{"slug":1410,"name":1410,"fn":1411,"description":1412,"org":1544,"tags":1545,"stars":25,"repoUrl":26,"updatedAt":1422},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1546,1547,1548,1549,1550],{"name":20,"slug":21,"type":15},{"name":1342,"slug":1343,"type":15},{"name":1348,"slug":1349,"type":15},{"name":13,"slug":14,"type":15},{"name":1420,"slug":1421,"type":15},34]