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See [Setup Pup](https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fagent-skills\u002Ftree\u002Fmain?tab=readme-ov-file#setup-pup).\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\n## Quick Start\n\n```bash\npup auth login\n```\n\n## Common Operations\n\n### List Monitors\n\n```bash\npup monitors list\npup monitors list --tags \"team:platform\"\n```\n\n### Get Monitor\n\n```bash\npup monitors get \u003Cid>\n```\n\n### Create Monitor\n\n```bash\npup monitors create --file monitor.json\n```\n\n### Silence Alerts (Downtime)\n\n```bash\n# No pup monitors mute\u002Funmute commands.\n# Use downtime payloads to silence monitor notifications.\npup downtime create --file downtime.json\npup downtime cancel \u003Cdowntime_id>\n```\n\n## Monitor Creation Best Practices\n\n### 1. Avoid Alert Fatigue\n\n| Rule | Why |\n|------|-----|\n| **No flapping alerts** | Use `last_Xm` not `last_1m` |\n| **Meaningful thresholds** | Based on SLOs, not guesses |\n| **Actionable alerts** | If no action needed, don't alert |\n| **Include runbook** | `@runbook-url` in message |\n\n```python\n# WRONG - will flap constantly\nquery = \"avg(last_1m):avg:system.cpu.user{*} > 50\"  # ❌ Too sensitive\n\n# CORRECT - stable alerting\nquery = \"avg(last_5m):avg:system.cpu.user{env:prod} by {host} > 80\"  # ✅ Reasonable window\n```\n\n### 2. Use Proper Scoping\n\n```python\n# WRONG - alerts on everything\nquery = \"avg(last_5m):avg:system.cpu.user{*} > 80\"  # ❌ No scope\n\n# CORRECT - scoped to what matters\nquery = \"avg(last_5m):avg:system.cpu.user{env:prod,service:api} by {host} > 80\"  # ✅\n```\n\n### 3. Set Recovery Thresholds\n\n```python\nmonitor = {\n    \"query\": \"avg(last_5m):avg:system.cpu.user{env:prod} > 80\",\n    \"options\": {\n        \"thresholds\": {\n            \"critical\": 80,\n            \"critical_recovery\": 70,  # ✅ Prevents flapping\n            \"warning\": 60,\n            \"warning_recovery\": 50\n        }\n    }\n}\n```\n\n### 4. Include Context in Messages\n\n```python\nmessage = \"\"\"\n## High CPU Alert\n\nHost: {{host.name}}\nCurrent Value: {{value}}\nThreshold: {{threshold}}\n\n### Runbook\n1. Check top processes: `ssh {{host.name}} 'top -bn1 | head -20'`\n2. Check recent deploys\n3. Scale if needed\n\n@slack-ops @pagerduty-oncall\n\"\"\"\n```\n\n## NEVER Delete Monitors Directly\n\nUse safe deletion workflow (same as dashboards):\n\n```python\ndef safe_mark_monitor_for_deletion(monitor_id: str, client) -> bool:\n    \"\"\"Mark monitor instead of deleting.\"\"\"\n    monitor = client.get_monitor(monitor_id)\n    name = monitor.get(\"name\", \"\")\n    \n    if \"[MARKED FOR DELETION]\" in name:\n        print(f\"Already marked: {name}\")\n        return False\n    \n    new_name = f\"[MARKED FOR DELETION] {name}\"\n    client.update_monitor(monitor_id, {\"name\": new_name})\n    print(f\"✓ Marked: {new_name}\")\n    return True\n```\n\n## Monitor Types\n\n| Type | Use Case |\n|------|----------|\n| `metric alert` | CPU, memory, custom metrics |\n| `query alert` | Complex metric queries |\n| `service check` | Agent check status |\n| `event alert` | Event stream patterns |\n| `log alert` | Log pattern matching |\n| `composite` | Combine multiple monitors |\n| `apm` | APM metrics |\n\n## Audit Monitors\n\n```bash\n# Find monitors without owners\npup monitors list | jq '.[] | select(.tags | contains([\"team:\"]) | not) | {id, name}'\n\n# Find noisy monitors (high alert count)\npup monitors list | jq 'sort_by(.overall_state_modified) | .[:10] | .[] | {id, name, status: .overall_state}'\n```\n\n## Downtime vs Muting\n\n| Use | When |\n|-----|------|\n| **Downtime** | Any planned silence window |\n| **Monitor edit** | Query\u002Fthreshold behavior changes |\n\n```bash\n# Downtime (preferred)\npup downtime create --file downtime.json\n```\n\n## Failure Handling\n\n| Problem | Fix |\n|---------|-----|\n| Alert not firing | Check query returns data, thresholds |\n| Too many alerts | Increase window, add recovery threshold |\n| No data alerts | Check agent connectivity, metric exists |\n| Auth error | `pup auth refresh` |\n\n## References\n\n- [Monitor Types](https:\u002F\u002Fdocs.datadoghq.com\u002Fmonitors\u002Ftypes\u002F)\n- [Alerting Best Practices](https:\u002F\u002Fdocs.datadoghq.com\u002Fmonitors\u002Fguide\u002F)\n- [SLO Monitors](https:\u002F\u002Fdocs.datadoghq.com\u002Fservice_management\u002Fservice_level_objectives\u002F)\n",{"data":34,"body":40},{"name":4,"description":6,"metadata":35},{"version":36,"author":8,"repository":23,"tags":37,"globs":38,"alwaysApply":39},"1.0.1","datadog,monitors,alerting,alerts,dd-monitors","**\u002Fdatadog*.yaml,**\u002F*monitor*","false",{"type":41,"children":42},"root",[43,52,58,65,81,87,92,122,128,163,169,176,238,244,288,294,327,333,417,423,429,542,593,599,645,651,752,758,877,883,888,998,1004,1145,1151,1257,1263,1317,1355,1361,1439,1445,1478],{"type":44,"tag":45,"props":46,"children":48},"element","h1",{"id":47},"datadog-monitors",[49],{"type":50,"value":51},"text","Datadog Monitors",{"type":44,"tag":53,"props":54,"children":55},"p",{},[56],{"type":50,"value":57},"Create, manage, and maintain monitors for alerting.",{"type":44,"tag":59,"props":60,"children":62},"h2",{"id":61},"prerequisites",[63],{"type":50,"value":64},"Prerequisites",{"type":44,"tag":53,"props":66,"children":67},{},[68,70,79],{"type":50,"value":69},"This requires pup in your path. 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(preferred)\n",{"type":44,"tag":141,"props":1333,"children":1334},{"class":143,"line":202},[1335,1339,1343,1347,1351],{"type":44,"tag":141,"props":1336,"children":1337},{"style":148},[1338],{"type":50,"value":151},{"type":44,"tag":141,"props":1340,"children":1341},{"style":154},[1342],{"type":50,"value":369},{"type":44,"tag":141,"props":1344,"children":1345},{"style":154},[1346],{"type":50,"value":316},{"type":44,"tag":141,"props":1348,"children":1349},{"style":154},[1350],{"type":50,"value":321},{"type":44,"tag":141,"props":1352,"children":1353},{"style":154},[1354],{"type":50,"value":382},{"type":44,"tag":59,"props":1356,"children":1358},{"id":1357},"failure-handling",[1359],{"type":50,"value":1360},"Failure 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Monitors",{"type":44,"tag":1479,"props":1480,"children":1481},"style",{},[1482],{"type":50,"value":1483},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":1485,"total":1658},[1486,1501,1519,1530,1546,1560,1575,1589,1601,1613,1623,1643],{"slug":1487,"name":1487,"fn":1488,"description":1489,"org":1490,"tags":1491,"stars":22,"repoUrl":23,"updatedAt":1500},"agent-install","install Datadog Agent on Kubernetes","Install 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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1492,1493,1496,1499],{"name":20,"slug":21,"type":15},{"name":1494,"slug":1495,"type":15},"Deployment","deployment",{"name":1497,"slug":1498,"type":15},"Kubernetes","kubernetes",{"name":13,"slug":14,"type":15},"2026-04-15T04:57:27.489805",{"slug":1502,"name":1502,"fn":1503,"description":1504,"org":1505,"tags":1506,"stars":22,"repoUrl":23,"updatedAt":1518},"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},[1507,1508,1511,1514,1517],{"name":20,"slug":21,"type":15},{"name":1509,"slug":1510,"type":15},"Debugging","debugging",{"name":1512,"slug":1513,"type":15},"Evals","evals",{"name":1515,"slug":1516,"type":15},"LLM","llm",{"name":13,"slug":14,"type":15},"2026-07-31T05:52:13.711906",{"slug":1520,"name":1520,"fn":1521,"description":1522,"org":1523,"tags":1524,"stars":22,"repoUrl":23,"updatedAt":1529},"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},[1525,1526,1527,1528],{"name":20,"slug":21,"type":15},{"name":1512,"slug":1513,"type":15},{"name":1515,"slug":1516,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:19.259734",{"slug":1531,"name":1531,"fn":1532,"description":1533,"org":1534,"tags":1535,"stars":22,"repoUrl":23,"updatedAt":1545},"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},[1536,1539,1542,1543,1544],{"name":1537,"slug":1538,"type":15},"Agents","agents",{"name":1540,"slug":1541,"type":15},"Data Pipeline","data-pipeline",{"name":20,"slug":21,"type":15},{"name":1512,"slug":1513,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:17.231423",{"slug":1547,"name":1547,"fn":1548,"description":1549,"org":1550,"tags":1551,"stars":22,"repoUrl":23,"updatedAt":1559},"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},[1552,1555,1556,1557,1558],{"name":1553,"slug":1554,"type":15},"Analytics","analytics",{"name":20,"slug":21,"type":15},{"name":1512,"slug":1513,"type":15},{"name":1515,"slug":1516,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:21.212498",{"slug":1561,"name":1561,"fn":1562,"description":1563,"org":1564,"tags":1565,"stars":22,"repoUrl":23,"updatedAt":1574},"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},[1566,1567,1570,1571,1572],{"name":20,"slug":21,"type":15},{"name":1568,"slug":1569,"type":15},"Jupyter","jupyter",{"name":1515,"slug":1516,"type":15},{"name":13,"slug":14,"type":15},{"name":1573,"slug":546,"type":15},"Python","2026-06-19T09:04:22.640384",{"slug":1576,"name":1576,"fn":1577,"description":1578,"org":1579,"tags":1580,"stars":22,"repoUrl":23,"updatedAt":1588},"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},[1581,1582,1583,1584,1585],{"name":20,"slug":21,"type":15},{"name":1509,"slug":1510,"type":15},{"name":1515,"slug":1516,"type":15},{"name":13,"slug":14,"type":15},{"name":1586,"slug":1587,"type":15},"Tracing","tracing","2026-07-31T05:52:08.594182",{"slug":1590,"name":1590,"fn":1591,"description":1592,"org":1593,"tags":1594,"stars":22,"repoUrl":23,"updatedAt":1600},"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},[1595,1596,1597,1598,1599],{"name":1553,"slug":1554,"type":15},{"name":20,"slug":21,"type":15},{"name":1512,"slug":1513,"type":15},{"name":1515,"slug":1516,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:26.341497",{"slug":1602,"name":1602,"fn":1603,"description":1604,"org":1605,"tags":1606,"stars":22,"repoUrl":23,"updatedAt":1612},"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},[1607,1608,1609,1610,1611],{"name":20,"slug":21,"type":15},{"name":1509,"slug":1510,"type":15},{"name":1512,"slug":1513,"type":15},{"name":1515,"slug":1516,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:24.316244",{"slug":1614,"name":1614,"fn":1615,"description":1616,"org":1617,"tags":1618,"stars":22,"repoUrl":23,"updatedAt":1622},"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},[1619,1620,1621],{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},"2026-04-06T18:08:33.337476",{"slug":1624,"name":1624,"fn":1625,"description":1626,"org":1627,"tags":1628,"stars":22,"repoUrl":23,"updatedAt":1642},"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},[1629,1630,1633,1636,1639],{"name":20,"slug":21,"type":15},{"name":1631,"slug":1632,"type":15},"Frontend","frontend",{"name":1634,"slug":1635,"type":15},"React","react",{"name":1637,"slug":1638,"type":15},"TypeScript","typescript",{"name":1640,"slug":1641,"type":15},"Vite","vite","2026-06-18T08:01:32.562331",{"slug":1644,"name":1644,"fn":1645,"description":1646,"org":1647,"tags":1648,"stars":22,"repoUrl":23,"updatedAt":1657},"dd-apm","query Datadog APM traces","APM - install, onboard, instrument, enable, set up, configure, traces, services, dependencies, performance analysis. 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