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Only use if no Datadog Agent is deployed on the cluster yet.\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,agent,operator,install\n  alwaysApply: \"false\"\n  tools: helm,kubectl,curl,pup\n---\n\n# Install the Datadog Agent 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## Phase 0: Load Credentials\n\n```bash\n[ -f environment ] && source environment\necho \"DD_API_KEY set: $([ -n \"${DD_API_KEY:-}\" ] && echo yes || echo no)\"\necho \"DD_SITE: ${DD_SITE:-not set}\"\necho \"helm: $(helm version --short 2>\u002Fdev\u002Fnull || echo NOT FOUND)\"\n```\n\n**If `helm` is not found** — tell the user:\n\n> `helm` is required for this skill. Install it with:\n> ```bash\n> brew install helm        # macOS\n> # or see https:\u002F\u002Fhelm.sh\u002Fdocs\u002Fintro\u002Finstall\u002F for other platforms\n> ```\n> Once installed, let me know and I'll continue.\n\nDo not proceed until `helm` is available.\n\n**If `DD_API_KEY` is already set** — proceed to Prerequisites.\n\n**If `DD_API_KEY` is not set** — tell the user:\n\n> I need two things to continue:\n>\n> **1. Datadog API Key** — used to authenticate the Agent with your Datadog account. You can find or create one at: https:\u002F\u002Fapp.datadoghq.com\u002Forganization-settings\u002Fapi-keys\n>\n> **2. Datadog Site** — the region your Datadog account is on. Most accounts use `datadoghq.com`. Check your Datadog URL to confirm (e.g. `app.datadoghq.eu` → site is `datadoghq.eu`). Other options: `us3.datadoghq.com`, `us5.datadoghq.com`, `ap1.datadoghq.com`.\n>\n> Please run the following in this chat to set your credentials (the `!` prefix executes it in this session):\n> ```\n> ! export DD_API_KEY=your-api-key-here\n> ! export DD_SITE=datadoghq.com\n> ```\n\nWait for the user to run the commands, then re-run the check above before continuing.\n\n---\n\n## Prerequisites\n\n- [ ] Kubernetes v1.20+ — `kubectl version`\n- [ ] helm v3+ — `helm version`\n- [ ] kubectl configured to target cluster — `kubectl config current-context`\n- [ ] pup-cli installed — check with `pup --version`; if missing, install it now:\n  ```bash\n  if [[ \"$(uname)\" == \"Darwin\" ]]; then\n    brew tap datadog-labs\u002Fpack && brew install pup\n  else\n    PUP_VERSION=$(curl -s https:\u002F\u002Fapi.github.com\u002Frepos\u002Fdatadog-labs\u002Fpup\u002Freleases\u002Flatest | grep '\"tag_name\"' | cut -d'\"' -f4)\n    curl -L \"https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fpup\u002Freleases\u002Fdownload\u002F${PUP_VERSION}\u002Fpup_linux_amd64.tar.gz\" | tar xz -C \u002Fusr\u002Flocal\u002Fbin pup\n    chmod +x \u002Fusr\u002Flocal\u002Fbin\u002Fpup\n  fi\n  pup --version\n  ```\n  Do not skip — proceed only once `pup --version` succeeds.\n\n---\n\n## Context to resolve before acting\n\n| Variable | How to resolve |\n|---|---|\n| `CLUSTER_NAME` | Check repo IaC, scripts, or `kubectl config current-context` |\n| `DD_SITE` | Ask the user. Default: `datadoghq.com`. Common options: `datadoghq.eu`, `us3.datadoghq.com`, `us5.datadoghq.com`, `ap1.datadoghq.com`. Full list: https:\u002F\u002Fdocs.datadoghq.com\u002Fgetting_started\u002Fsite\u002F |\n| `AGENT_NAMESPACE` | Use `datadog` unless the repo already uses `datadog-agent` consistently |\n| `CHART_VERSION` | Run `helm search repo datadog\u002Fdatadog-operator --versions \\| head -5` and use the latest stable |\n\n---\n\n## Step 1: Check for an Existing Agent Installation\n\n### Claude runs\n\n```bash\nhelm list -A | grep -i datadog\n```\n\nIf a release shows `deployed` — Agent already installed. Skip to Step 5 to confirm health, then exit.\n\nIf there is no output — no existing install. Continue to Step 2.\n\n---\n\n## Step 2: Install the Datadog Operator\n\n### Claude runs\n\n```bash\nhelm repo add datadog https:\u002F\u002Fhelm.datadoghq.com\nhelm repo update\n\nhelm upgrade --install datadog-operator datadog\u002Fdatadog-operator \\\n  --namespace \u003CAGENT_NAMESPACE> \\\n  --create-namespace \\\n  --version \u003CCHART_VERSION>\n\nkubectl wait --for=condition=Ready pod \\\n  -l app.kubernetes.io\u002Fname=datadog-operator \\\n  -n \u003CAGENT_NAMESPACE> \\\n  --timeout=120s\n```\n\nIf the Operator pod is Running — continue to Step 3.\n\nERROR: Pod not ready after 120s — check image pull: `kubectl describe pod -l app.kubernetes.io\u002Fname=datadog-operator -n \u003CAGENT_NAMESPACE>`.\n\n---\n\n## Step 3: Create the API Key Secret\n\n### What you need to do in a terminal\n\n```bash\nexport DD_API_KEY=\u003Cyour-api-key>\n\nkubectl create secret generic datadog-secret \\\n  --from-literal api-key=$DD_API_KEY \\\n  --namespace \u003CAGENT_NAMESPACE>\n```\n\nIf `secret\u002Fdatadog-secret created` — continue to Step 4.\n\nERROR: `AlreadyExists` — confirm which key it holds via Step 5 before deciding whether to recreate.\n\n---\n\n## Step 4: Deploy the DatadogAgent Resource\n\n[DECISION: cluster type]\n- Self-hosted (minikube, kind): include `kubelet.tlsVerify: false` inside `spec.global`\n- Managed (GKE, EKS, AKS): omit `kubelet.tlsVerify` entirely\n\n[DECISION: APM\u002FSSI also being enabled in this session]\n- If yes: do not create a separate `DatadogAgent` for APM — extend this same manifest with `features.apm` per `enable-ssi`. One manifest, not two.\n- If no: use the manifest below as-is.\n\nSave the following as `datadog-agent.yaml`:\n\n```yaml\napiVersion: datadoghq.com\u002Fv2alpha1\nkind: DatadogAgent\nmetadata:\n  name: datadog\n  namespace: \u003CAGENT_NAMESPACE>\nspec:\n  global:\n    clusterName: \u003CCLUSTER_NAME>\n    site: \u003CDD_SITE>\n    credentials:\n      apiSecret:\n        secretName: datadog-secret\n        keyName: api-key\n    # Self-hosted clusters only (minikube, kind):\n    # kubelet:\n    #   tlsVerify: false\n  features:\n    orchestratorExplorer:\n      enabled: true\n    clusterChecks:\n      enabled: true\n    logCollection:\n      enabled: true\n      containerCollectAll: false\n```\n\n### Claude runs\n\n```bash\nkubectl apply -f datadog-agent.yaml\n\nkubectl wait --for=condition=Ready pod \\\n  -l app.kubernetes.io\u002Fcomponent=agent \\\n  -n \u003CAGENT_NAMESPACE> \\\n  --timeout=120s 2>\u002Fdev\u002Fnull || true\n```\n\n---\n\n## Step 5: Verify the API Key\n\n### Claude runs\n\n```bash\nkubectl logs -l app.kubernetes.io\u002Fcomponent=agent \\\n  -n \u003CAGENT_NAMESPACE> \\\n  --tail=50 2>\u002Fdev\u002Fnull \\\n  | grep -iE \"invalid.*api\\.?key|api\\.?key.*invalid\" \\\n  || echo \"No authentication errors found\"\n```\n\nIf `No authentication errors found` — key is accepted.\n\nERROR: Authentication errors found — validate credentials directly:\n\n### Claude runs\n\n```bash\n# Prefer pup (OAuth) — fall back to curl with API key\nif pup auth status 2>\u002Fdev\u002Fnull | grep -q \"Logged in\"; then\n  echo \"pup OAuth authenticated\"\nelif [ -n \"${DD_API_KEY:-}\" ]; then\n  RESPONSE=$(curl -s -o \u002Fdev\u002Fnull -w \"%{http_code}\" \\\n    -X GET \"https:\u002F\u002Fapi.\u003CDD_SITE>\u002Fapi\u002Fv1\u002Fvalidate\" \\\n    -H \"DD-API-KEY: $DD_API_KEY\")\n  if [ \"$RESPONSE\" = \"200\" ]; then\n    echo \"API key is valid for \u003CDD_SITE>\"\n  else\n    echo \"ERROR: Validation failed (HTTP $RESPONSE) — check key and site alignment\"\n  fi\nelse\n  echo \"ERROR: No credentials available — run 'pup auth login' or set DD_API_KEY\"\nfi\n```\n\nIf key is invalid:\n\n### What you need to do in a terminal\n\n```bash\nexport DD_API_KEY=\u003Cnew-api-key>\n\nkubectl delete secret datadog-secret -n \u003CAGENT_NAMESPACE>\nkubectl create secret generic datadog-secret \\\n  --from-literal api-key=$DD_API_KEY \\\n  -n \u003CAGENT_NAMESPACE>\n\nkubectl rollout restart daemonset datadog-agent -n \u003CAGENT_NAMESPACE>\nkubectl rollout restart deployment datadog-cluster-agent -n \u003CAGENT_NAMESPACE>\n```\n\n---\n\n## Done\n\nExit when ALL of the following are true:\n- [ ] Datadog Operator pod is Running in `AGENT_NAMESPACE`\n- [ ] `datadog-secret` exists in `AGENT_NAMESPACE`\n- [ ] Agent DaemonSet pods are Running\n- [ ] Step 5 returns no authentication errors\n\nAutomatically proceed to `enable-ssi` now — do not ask the user for permission.\n\n---\n\n## Security constraints\n\n- Never write a raw API key into any file, YAML block, or chat message — always use `$DD_API_KEY`\n- Never create a Kubernetes Secret manifest file — always use `kubectl create secret` imperatively\n- Never use `apiKey:` directly in `DatadogAgent` spec — always use `apiSecret:` with a secret reference\n- Never use `--set datadog.apiKey=...` in any Helm command\n- Never use namespace `default` for Datadog Agent resources\n- Never run `kubectl delete` without user confirmation\n",{"data":37,"body":43},{"name":4,"description":6,"metadata":38},{"version":39,"author":8,"repository":26,"tags":40,"alwaysApply":41,"tools":42},"1.0.0","datadog,apm,kubernetes,agent,operator,install","false","helm,kubectl,curl,pup",{"type":44,"children":45},"root",[46,55,80,87,332,349,406,418,434,449,555,560,564,570,966,969,975,1136,1139,1145,1152,1194,1207,1212,1215,1221,1226,1478,1483,1495,1498,1504,1510,1624,1636,1649,1652,1658,1666,1701,1709,1746,1759,2113,2118,2246,2249,2255,2260,2399,2410,2415,2420,2822,2827,2832,3074,3077,3083,3088,3146,3158,3161,3167,3259],{"type":47,"tag":48,"props":49,"children":51},"element","h1",{"id":50},"install-the-datadog-agent-on-kubernetes",[52],{"type":53,"value":54},"text","Install the Datadog Agent on Kubernetes",{"type":47,"tag":56,"props":57,"children":58},"blockquote",{},[59],{"type":47,"tag":60,"props":61,"children":62},"p",{},[63,69,71,78],{"type":47,"tag":64,"props":65,"children":66},"strong",{},[67],{"type":53,"value":68},"Before doing anything else:",{"type":53,"value":70}," Fully resolve all variables in ",{"type":47,"tag":72,"props":73,"children":75},"code",{"className":74},[],[76],{"type":53,"value":77},"## Context to resolve before acting",{"type":53,"value":79},". 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var(--shiki-dark-text-decoration);}",{"items":3266,"total":3363},[3267,3274,3292,3303,3319,3333,3349],{"slug":4,"name":4,"fn":5,"description":6,"org":3268,"tags":3269,"stars":25,"repoUrl":26,"updatedAt":27},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3270,3271,3272,3273],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},{"slug":3275,"name":3275,"fn":3276,"description":3277,"org":3278,"tags":3279,"stars":25,"repoUrl":26,"updatedAt":3291},"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},[3280,3281,3284,3287,3290],{"name":17,"slug":18,"type":15},{"name":3282,"slug":3283,"type":15},"Debugging","debugging",{"name":3285,"slug":3286,"type":15},"Evals","evals",{"name":3288,"slug":3289,"type":15},"LLM","llm",{"name":13,"slug":14,"type":15},"2026-07-31T05:52:13.711906",{"slug":3293,"name":3293,"fn":3294,"description":3295,"org":3296,"tags":3297,"stars":25,"repoUrl":26,"updatedAt":3302},"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},[3298,3299,3300,3301],{"name":17,"slug":18,"type":15},{"name":3285,"slug":3286,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:19.259734",{"slug":3304,"name":3304,"fn":3305,"description":3306,"org":3307,"tags":3308,"stars":25,"repoUrl":26,"updatedAt":3318},"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},[3309,3312,3315,3316,3317],{"name":3310,"slug":3311,"type":15},"Agents","agents",{"name":3313,"slug":3314,"type":15},"Data Pipeline","data-pipeline",{"name":17,"slug":18,"type":15},{"name":3285,"slug":3286,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:17.231423",{"slug":3320,"name":3320,"fn":3321,"description":3322,"org":3323,"tags":3324,"stars":25,"repoUrl":26,"updatedAt":3332},"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},[3325,3328,3329,3330,3331],{"name":3326,"slug":3327,"type":15},"Analytics","analytics",{"name":17,"slug":18,"type":15},{"name":3285,"slug":3286,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:21.212498",{"slug":3334,"name":3334,"fn":3335,"description":3336,"org":3337,"tags":3338,"stars":25,"repoUrl":26,"updatedAt":3348},"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},[3339,3340,3343,3344,3345],{"name":17,"slug":18,"type":15},{"name":3341,"slug":3342,"type":15},"Jupyter","jupyter",{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},{"name":3346,"slug":3347,"type":15},"Python","python","2026-06-19T09:04:22.640384",{"slug":3350,"name":3350,"fn":3351,"description":3352,"org":3353,"tags":3354,"stars":25,"repoUrl":26,"updatedAt":3362},"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},[3355,3356,3357,3358,3359],{"name":17,"slug":18,"type":15},{"name":3282,"slug":3283,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},{"name":3360,"slug":3361,"type":15},"Tracing","tracing","2026-07-31T05:52:08.594182",34,{"items":3365,"total":3491},[3366,3373,3381,3388,3396,3404,3412,3420,3432,3444,3456,3476],{"slug":4,"name":4,"fn":5,"description":6,"org":3367,"tags":3368,"stars":25,"repoUrl":26,"updatedAt":27},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3369,3370,3371,3372],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},{"slug":3275,"name":3275,"fn":3276,"description":3277,"org":3374,"tags":3375,"stars":25,"repoUrl":26,"updatedAt":3291},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3376,3377,3378,3379,3380],{"name":17,"slug":18,"type":15},{"name":3282,"slug":3283,"type":15},{"name":3285,"slug":3286,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},{"slug":3293,"name":3293,"fn":3294,"description":3295,"org":3382,"tags":3383,"stars":25,"repoUrl":26,"updatedAt":3302},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3384,3385,3386,3387],{"name":17,"slug":18,"type":15},{"name":3285,"slug":3286,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},{"slug":3304,"name":3304,"fn":3305,"description":3306,"org":3389,"tags":3390,"stars":25,"repoUrl":26,"updatedAt":3318},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3391,3392,3393,3394,3395],{"name":3310,"slug":3311,"type":15},{"name":3313,"slug":3314,"type":15},{"name":17,"slug":18,"type":15},{"name":3285,"slug":3286,"type":15},{"name":13,"slug":14,"type":15},{"slug":3320,"name":3320,"fn":3321,"description":3322,"org":3397,"tags":3398,"stars":25,"repoUrl":26,"updatedAt":3332},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3399,3400,3401,3402,3403],{"name":3326,"slug":3327,"type":15},{"name":17,"slug":18,"type":15},{"name":3285,"slug":3286,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},{"slug":3334,"name":3334,"fn":3335,"description":3336,"org":3405,"tags":3406,"stars":25,"repoUrl":26,"updatedAt":3348},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3407,3408,3409,3410,3411],{"name":17,"slug":18,"type":15},{"name":3341,"slug":3342,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},{"name":3346,"slug":3347,"type":15},{"slug":3350,"name":3350,"fn":3351,"description":3352,"org":3413,"tags":3414,"stars":25,"repoUrl":26,"updatedAt":3362},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3415,3416,3417,3418,3419],{"name":17,"slug":18,"type":15},{"name":3282,"slug":3283,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},{"name":3360,"slug":3361,"type":15},{"slug":3421,"name":3421,"fn":3422,"description":3423,"org":3424,"tags":3425,"stars":25,"repoUrl":26,"updatedAt":3431},"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},[3426,3427,3428,3429,3430],{"name":3326,"slug":3327,"type":15},{"name":17,"slug":18,"type":15},{"name":3285,"slug":3286,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:26.341497",{"slug":3433,"name":3433,"fn":3434,"description":3435,"org":3436,"tags":3437,"stars":25,"repoUrl":26,"updatedAt":3443},"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},[3438,3439,3440,3441,3442],{"name":17,"slug":18,"type":15},{"name":3282,"slug":3283,"type":15},{"name":3285,"slug":3286,"type":15},{"name":3288,"slug":3289,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:24.316244",{"slug":3445,"name":3445,"fn":3446,"description":3447,"org":3448,"tags":3449,"stars":25,"repoUrl":26,"updatedAt":3455},"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},[3450,3451,3454],{"name":17,"slug":18,"type":15},{"name":3452,"slug":3453,"type":15},"Monitoring","monitoring",{"name":13,"slug":14,"type":15},"2026-04-06T18:08:33.337476",{"slug":3457,"name":3457,"fn":3458,"description":3459,"org":3460,"tags":3461,"stars":25,"repoUrl":26,"updatedAt":3475},"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},[3462,3463,3466,3469,3472],{"name":17,"slug":18,"type":15},{"name":3464,"slug":3465,"type":15},"Frontend","frontend",{"name":3467,"slug":3468,"type":15},"React","react",{"name":3470,"slug":3471,"type":15},"TypeScript","typescript",{"name":3473,"slug":3474,"type":15},"Vite","vite","2026-06-18T08:01:32.562331",{"slug":3477,"name":3477,"fn":3478,"description":3479,"org":3480,"tags":3481,"stars":25,"repoUrl":26,"updatedAt":3490},"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},[3482,3483,3486,3487],{"name":17,"slug":18,"type":15},{"name":3484,"slug":3485,"type":15},"Distributed Tracing","distributed-tracing",{"name":13,"slug":14,"type":15},{"name":3488,"slug":3489,"type":15},"Performance","performance","2026-04-06T18:08:34.575282",35]