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Only use after enable-ssi has run.\nmetadata:\n  version: \"1.0.0\"\n  author: datadog-labs\n  repository: https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fagent-skills\n  tags: datadog,apm,kubernetes,ssi,verification,instrumentation\n  alwaysApply: \"false\"\n---\n\n# Verify APM SSI on Kubernetes\n\n> **Before doing anything else:** Fully resolve all variables in `## Context to resolve before acting`. Do not begin Step 1 until every variable has a concrete value.\n\n## Triggers\n\nInvoke this skill when the user expresses intent to:\n- Confirm SSI is working after enabling APM\n- Check whether pods are being instrumented\n- Verify the tracer is running and reporting telemetry\n- Confirm tracer config is applied correctly\n\nDo NOT invoke this skill if:\n- SSI has not been enabled yet — run `enable-ssi` first\n- Pods are not being instrumented at all — use `troubleshoot-ssi`\n\n---\n\n## Prerequisites\n\n- [ ] `enable-ssi` is complete\n- [ ] Application pods have been restarted since SSI was enabled\n\n### pup-cli: check, install, and authenticate\n\n### Claude runs\n\n```bash\npup --version\n```\n\nIf not found:\n\n### Claude runs\n\n```bash\nbrew tap datadog-labs\u002Fpack\nbrew install pup\n```\n\nCheck auth:\n```bash\npup auth status --site \u003CDD_SITE>\n```\n\nIf not authenticated:\n\n### Claude runs\n\n```bash\npup auth login --site \u003CDD_SITE>\n```\n\n> This opens a browser tab for OAuth. Complete the login there — Claude will continue once the command exits.\n\nIf valid token — proceed.\nERROR: No browser available — use API key fallback: `export DD_APP_KEY=\u003Cyour-app-key>`\n\n---\n\n## Context to resolve before acting\n\n| Variable | How to resolve |\n|---|---|\n| `CLUSTER_NAME` | Check `spec.global.clusterName` in `datadog-agent.yaml`, or `kubectl config current-context` |\n| `ENV` | Check `tags.datadoghq.com\u002Fenv` label on the application Deployment |\n| `SERVICE_NAME` | Check `tags.datadoghq.com\u002Fservice` label on the application Deployment |\n\n---\n\n## Step 1: Confirm Pods are Instrumented\n\n### Claude runs\n\n```bash\nkubectl get pod -l app=\u003CAPP_LABEL> -n \u003CAPP_NAMESPACE> \\\n  -o jsonpath='{.items[0].spec.initContainers[*].name}'\n```\n\nIf the output includes `datadog-lib-\u003Clanguage>-init` and `datadog-init-apm-inject` — SSI init containers are injected.\n\nERROR: Init containers missing — pod was not restarted after SSI was enabled, or namespace targeting is not matching. Restart the pod and recheck.\n\n---\n\n## Step 2: Confirm the Tracer is Reporting Telemetry\n\n### Claude runs\n\n```bash\nDD_SITE=\u003CDD_SITE> pup apm services list --env \u003CENV> --from 1h\n```\n\nIf `\u003CSERVICE_NAME>` appears in the services list with `isTraced: true` — continue to Step 3.\n\nERROR: Service missing — send some traffic to the app first, then retry:\n\n### Claude runs\n\n```bash\n# Port-forward and send test traffic\nkubectl port-forward deployment\u002F\u003CDEPLOYMENT_NAME> 8099:8000 -n \u003CAPP_NAMESPACE> &\nsleep 2 && for i in $(seq 1 10); do curl -s -o \u002Fdev\u002Fnull http:\u002F\u002Flocalhost:8099\u002F; done\nsleep 30 && kill %1 2>\u002Fdev\u002Fnull\nDD_SITE=\u003CDD_SITE> pup apm services list --env \u003CENV> --from 10m\n```\n\nERROR: Still missing after traffic — check the agent's trace receiver: `kubectl exec -n \u003CAGENT_NAMESPACE> \u003CAGENT_POD> -c agent -- agent status | grep -A 10 \"Receiver (previous minute)\"`. 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Only use if no Datadog Agent is deployed on the cluster yet.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1434,1435,1438,1439],{"name":17,"slug":18,"type":15},{"name":1436,"slug":1437,"type":15},"Deployment","deployment",{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},"2026-04-15T04:57:27.489805",{"slug":1442,"name":1442,"fn":1443,"description":1444,"org":1445,"tags":1446,"stars":25,"repoUrl":26,"updatedAt":1458},"agent-observability-auto-experiment","run iterative code improvements using Datadog data","Run 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. Use when the user says \"run an auto experiment\", \"hill-climb this code\", \"iteratively improve X and measure the delta\", \"optimize this prompt\u002Ffile against my traces\", \"auto-optimize against LLM-Obs\", or wants the local equivalent of the auto_experiments worker. Works from a local dataset file, an ml_app, a dataset_id, or a list of trace_ids.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1447,1448,1451,1454,1457],{"name":17,"slug":18,"type":15},{"name":1449,"slug":1450,"type":15},"Debugging","debugging",{"name":1452,"slug":1453,"type":15},"Evals","evals",{"name":1455,"slug":1456,"type":15},"LLM","llm",{"name":13,"slug":14,"type":15},"2026-07-31T05:52:13.711906",{"slug":1460,"name":1460,"fn":1461,"description":1462,"org":1463,"tags":1464,"stars":25,"repoUrl":26,"updatedAt":1469},"agent-observability-eval-bootstrap","bootstrap evaluators from production traces","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\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},[1465,1466,1467,1468],{"name":17,"slug":18,"type":15},{"name":1452,"slug":1453,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:19.259734",{"slug":1471,"name":1471,"fn":1472,"description":1473,"org":1474,"tags":1475,"stars":25,"repoUrl":26,"updatedAt":1485},"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. 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},[1476,1479,1482,1483,1484],{"name":1477,"slug":1478,"type":15},"Agents","agents",{"name":1480,"slug":1481,"type":15},"Data Pipeline","data-pipeline",{"name":17,"slug":18,"type":15},{"name":1452,"slug":1453,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:17.231423",{"slug":1487,"name":1487,"fn":1488,"description":1489,"org":1490,"tags":1491,"stars":25,"repoUrl":26,"updatedAt":1499},"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},[1492,1495,1496,1497,1498],{"name":1493,"slug":1494,"type":15},"Analytics","analytics",{"name":17,"slug":18,"type":15},{"name":1452,"slug":1453,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:21.212498",{"slug":1501,"name":1501,"fn":1502,"description":1503,"org":1504,"tags":1505,"stars":25,"repoUrl":26,"updatedAt":1515},"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},[1506,1507,1510,1511,1512],{"name":17,"slug":18,"type":15},{"name":1508,"slug":1509,"type":15},"Jupyter","jupyter",{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},{"name":1513,"slug":1514,"type":15},"Python","python","2026-06-19T09:04:22.640384",{"slug":1517,"name":1517,"fn":1518,"description":1519,"org":1520,"tags":1521,"stars":25,"repoUrl":26,"updatedAt":1529},"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},[1522,1523,1524,1525,1526],{"name":17,"slug":18,"type":15},{"name":1449,"slug":1450,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},{"name":1527,"slug":1528,"type":15},"Tracing","tracing","2026-07-31T05:52:08.594182",{"slug":1531,"name":1531,"fn":1532,"description":1533,"org":1534,"tags":1535,"stars":25,"repoUrl":26,"updatedAt":1541},"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},[1536,1537,1538,1539,1540],{"name":1493,"slug":1494,"type":15},{"name":17,"slug":18,"type":15},{"name":1452,"slug":1453,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:26.341497",{"slug":1543,"name":1543,"fn":1544,"description":1545,"org":1546,"tags":1547,"stars":25,"repoUrl":26,"updatedAt":1553},"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},[1548,1549,1550,1551,1552],{"name":17,"slug":18,"type":15},{"name":1449,"slug":1450,"type":15},{"name":1452,"slug":1453,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:24.316244",{"slug":1555,"name":1555,"fn":1556,"description":1557,"org":1558,"tags":1559,"stars":25,"repoUrl":26,"updatedAt":1565},"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},[1560,1561,1564],{"name":17,"slug":18,"type":15},{"name":1562,"slug":1563,"type":15},"Monitoring","monitoring",{"name":13,"slug":14,"type":15},"2026-04-06T18:08:33.337476",{"slug":1567,"name":1567,"fn":1568,"description":1569,"org":1570,"tags":1571,"stars":25,"repoUrl":26,"updatedAt":1585},"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},[1572,1573,1576,1579,1582],{"name":17,"slug":18,"type":15},{"name":1574,"slug":1575,"type":15},"Frontend","frontend",{"name":1577,"slug":1578,"type":15},"React","react",{"name":1580,"slug":1581,"type":15},"TypeScript","typescript",{"name":1583,"slug":1584,"type":15},"Vite","vite","2026-06-18T08:01:32.562331",{"slug":1587,"name":1587,"fn":1588,"description":1589,"org":1590,"tags":1591,"stars":25,"repoUrl":26,"updatedAt":1600},"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},[1592,1593,1596,1597],{"name":17,"slug":18,"type":15},{"name":1594,"slug":1595,"type":15},"Distributed Tracing","distributed-tracing",{"name":13,"slug":14,"type":15},{"name":1598,"slug":1599,"type":15},"Performance","performance","2026-04-06T18:08:34.575282",35,{"items":1603,"total":1658},[1604,1611,1619,1626,1634,1642,1650],{"slug":1429,"name":1429,"fn":1430,"description":1431,"org":1605,"tags":1606,"stars":25,"repoUrl":26,"updatedAt":1440},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1607,1608,1609,1610],{"name":17,"slug":18,"type":15},{"name":1436,"slug":1437,"type":15},{"name":20,"slug":21,"type":15},{"name":13,"slug":14,"type":15},{"slug":1442,"name":1442,"fn":1443,"description":1444,"org":1612,"tags":1613,"stars":25,"repoUrl":26,"updatedAt":1458},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1614,1615,1616,1617,1618],{"name":17,"slug":18,"type":15},{"name":1449,"slug":1450,"type":15},{"name":1452,"slug":1453,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},{"slug":1460,"name":1460,"fn":1461,"description":1462,"org":1620,"tags":1621,"stars":25,"repoUrl":26,"updatedAt":1469},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1622,1623,1624,1625],{"name":17,"slug":18,"type":15},{"name":1452,"slug":1453,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},{"slug":1471,"name":1471,"fn":1472,"description":1473,"org":1627,"tags":1628,"stars":25,"repoUrl":26,"updatedAt":1485},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1629,1630,1631,1632,1633],{"name":1477,"slug":1478,"type":15},{"name":1480,"slug":1481,"type":15},{"name":17,"slug":18,"type":15},{"name":1452,"slug":1453,"type":15},{"name":13,"slug":14,"type":15},{"slug":1487,"name":1487,"fn":1488,"description":1489,"org":1635,"tags":1636,"stars":25,"repoUrl":26,"updatedAt":1499},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1637,1638,1639,1640,1641],{"name":1493,"slug":1494,"type":15},{"name":17,"slug":18,"type":15},{"name":1452,"slug":1453,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},{"slug":1501,"name":1501,"fn":1502,"description":1503,"org":1643,"tags":1644,"stars":25,"repoUrl":26,"updatedAt":1515},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1645,1646,1647,1648,1649],{"name":17,"slug":18,"type":15},{"name":1508,"slug":1509,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},{"name":1513,"slug":1514,"type":15},{"slug":1517,"name":1517,"fn":1518,"description":1519,"org":1651,"tags":1652,"stars":25,"repoUrl":26,"updatedAt":1529},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1653,1654,1655,1656,1657],{"name":17,"slug":18,"type":15},{"name":1449,"slug":1450,"type":15},{"name":1455,"slug":1456,"type":15},{"name":13,"slug":14,"type":15},{"name":1527,"slug":1528,"type":15},34]