[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-datadog-labs-enable-ssi":3,"mdc-r25gqs-key":36,"related-org-datadog-labs-enable-ssi":2690,"related-repo-datadog-labs-enable-ssi":2863},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":31,"sourceUrl":34,"mdContent":35},"enable-ssi","enable Kubernetes Single Step Instrumentation","Enable Single Step Instrumentation (SSI) on Kubernetes — automatically instruments applications for APM without code changes. 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Only use if the Datadog Agent is already running on the cluster — if not, use agent-install first.\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,instrumentation,single-step\n  alwaysApply: \"false\"\n---\n\n# Enable APM on Kubernetes via Single Step Instrumentation\n\n> **Before doing anything else:** Fully resolve all variables in `## Context to resolve before acting`. Do not begin Step 0 until every variable has a concrete value.\n\n---\n\n> **Silent failure — check this before any other step:**\n>\n> If the application has `ddtrace`, `dd-trace`, or any OpenTelemetry SDK in its **dependency manifest** (`requirements.txt`, `package.json`, `Gemfile`, `go.mod`, `pom.xml`) — even with no import statements in code — SSI will silently disable itself at runtime.\n>\n> The failure is invisible: init containers run and complete, the pod starts healthy, no errors appear in `kubectl` or `pup`, but no traces arrive. The injector detects the user-installed tracer and exits cleanly without logging anything.\n>\n> ### Claude runs\n>\n> ```bash\n> grep -rE \"ddtrace|dd-trace|opentelemetry\" \\\n>   requirements.txt package.json Gemfile go.mod pom.xml 2>\u002Fdev\u002Fnull \\\n>   || echo \"No tracer dependency found\"\n> ```\n>\n> If any match — **stop**. Remove the package entirely (not just the import), rebuild the image, reload it into the cluster, and restart the pod before continuing. A package present in the manifest is enough to trigger this even if it is never imported.\n\n---\n\n## Triggers\n\nInvoke this skill when the user expresses intent to:\n- Enable APM on a Kubernetes cluster\n- Instrument Kubernetes applications with Datadog tracing\n- Set up Single Step Instrumentation (SSI)\n\nDo NOT invoke this skill if:\n- The Datadog Agent is not yet installed — run `agent-install` first\n- The user wants to verify SSI after setup — use `verify-ssi`\n- The user wants to enable Profiler, AppSec, or Data Streams — use `dd-apm-k8s-sdk-features`\n\n---\n\n## Prerequisites\n\n> **These are not a reading exercise — actively verify each one before proceeding.**\n\n**Environment**\n- [ ] Datadog Agent is installed and healthy — `agent-install` complete\n- [ ] Kubernetes v1.20+\n- [ ] Linux node pools only — Windows pods require explicit namespace exclusion\n- [ ] Cluster is not ECS Fargate — unsupported\n- [ ] Not a hardened SELinux environment — unsupported\n- [ ] Not a very small VM instance (e.g. t2.micro) — SSI can hit init timeouts\n- [ ] No PodSecurity baseline or restricted policy enforced\n\n**Language and runtime**\n- [ ] Application language is one of: Java, Python, Ruby, Node.js, .NET, PHP\n- [ ] Runtime version is within SSI's supported range — verify against the [SSI compatibility matrix](https:\u002F\u002Fdocs.datadoghq.com\u002Ftracing\u002Ftrace_collection\u002Fautomatic_instrumentation\u002Fsingle-step-apm\u002Fcompatibility\u002F)\n- [ ] Node.js app is not using ESM — SSI does not support ESM\n- [ ] Java app is not already using a `-javaagent` JVM flag\n\n**Existing instrumentation** — confirmed clean by the check at the top of this skill. If you skipped that check, go back and run it now.\n\n---\n\n## Context to resolve before acting\n\n> **Discover from the cluster — do not ask the user for information you can find yourself.**\n\n| Variable | How to resolve |\n|---|---|\n| `AGENT_NAMESPACE` | Same namespace used in `agent-install` (e.g. `datadog`) |\n| `APP_NAMESPACE` | Run `kubectl get namespaces --no-headers \\| awk '{print $1}' \\| grep -vE '^(kube-system\\|kube-public\\|kube-node-lease\\|datadog\\|local-path-storage)$'` — instrument all non-system namespaces, or use the namespace(s) the user mentioned |\n| `TARGET_LANGUAGES` | Run `kubectl get pods -A -o jsonpath='{.items[*].spec.containers[*].image}'` and infer language from image names, or check Dockerfiles\u002Fmanifests in the workspace. If uncertain, enable all languages. |\n| `DEPLOYMENT_NAME` | Run `kubectl get deployments -A --no-headers` — identify application deployments (exclude system components) |\n| `APP_LABEL` | Check `spec.selector.matchLabels` in the Deployment manifest via `kubectl get deployment \u003CDEPLOYMENT_NAME> -n \u003CAPP_NAMESPACE> -o yaml` |\n| `CLUSTER_NAME` | Check `spec.global.clusterName` in `datadog-agent.yaml`, or `kubectl config current-context` — needed for kind clusters in Step 0 |\n| `ENV` | Use `apm-evals` if running in an eval cluster (kind cluster names contain \"evalya\"). Otherwise use `production` unless the user specifies otherwise. |\n| `SERVICE_NAME` | Use the deployment name (e.g. `python-app` → service `python-app`). Do not ask the user. |\n| `VERSION` | Use `1.0.0` as the default. Do not ask the user. |\n\n---\n\n## Step 0 (Only if existing instrumentation detected): Remove Manual Instrumentation\n\nScan all source files for: `import ddtrace`, `from ddtrace`, `require 'ddtrace'`, `require(\"dd-trace\")`, `opentelemetry`, `tracer.trace(`\n\nAlso check dependency manifests for `ddtrace` \u002F `dd-trace` \u002F OTel SDK packages.\n\nIf found — remove the import\u002Fpackage, then rebuild and reload:\n\n### Claude runs\n\n```bash\ndocker build -f \u003CDOCKERFILE_PATH> -t \u003CIMAGE_NAME> \u003CBUILD_CONTEXT>\n```\n\n[DECISION: how does this cluster get local images?]\n\nCheck the repo's setup script (e.g. `create.sh`, `Makefile`, `justfile`) for how images are loaded — do not guess from the cluster name or context. Common patterns:\n\n| What you find in the setup script | Load command |\n|---|---|\n| `minikube image load` or `minikube cache add` | `minikube -p \u003CPROFILE> image load \u003CIMAGE_NAME>` — profile is the `-p` flag value in the script, NOT necessarily the kubectl context name |\n| `kind load docker-image` | `kind load docker-image \u003CIMAGE_NAME> --name \u003CCLUSTER_NAME>` |\n| `docker push` to a registry | Push the new image; the cluster will pull on restart — skip local load |\n| `k3d image import` | `k3d image import \u003CIMAGE_NAME> -c \u003CCLUSTER_NAME>` |\n| No image load step (cloud cluster, always pulls from registry) | Skip — image will be pulled on next deployment |\n\nIf the setup script is ambiguous, run the load command it uses exactly as written.\n\n- Registry-based: skip — image will be pulled on next deployment\n\n> **Confirm with the user before restarting.** Tell the user: \"I need to restart `\u003CDEPLOYMENT_NAME>` in `\u003CAPP_NAMESPACE>` to pick up the rebuilt image. Ready to proceed?\" Wait for confirmation.\n\n### Claude runs\n\n```bash\nkubectl rollout restart deployment\u002F\u003CDEPLOYMENT_NAME> -n \u003CAPP_NAMESPACE>\nkubectl wait --for=condition=Ready pod \\\n  -l app=\u003CAPP_LABEL> \\\n  -n \u003CAPP_NAMESPACE> \\\n  --timeout=120s\n```\n\n---\n\n## Step 1: Extend the DatadogAgent Manifest with APM\n\nSSI is configured on the existing `DatadogAgent` resource — do not create a separate manifest.\n\n**Choose targeting scope based on what the user asked for:**\n- User asked to instrument **all applications** or didn't specify scope → **use Option A (cluster-wide)**\n- User asked for specific namespaces only → use Option B\n- User asked to exclude namespaces from cluster-wide → use Option C\n- User asked for specific pods\u002Fworkloads → use Option D\n\n> **Default is cluster-wide (Option A).** If the user said \"all my applications\", \"my whole cluster\", or didn't restrict scope, use Option A with no `enabledNamespaces` or `targets`.\n\nRecommended `ddTraceVersions`: `java: \"1\"`, `python: \"2\"`, `js: \"5\"`, `dotnet: \"3\"`, `ruby: \"2\"`, `php: \"1\"`\n\n**Option A — Cluster-wide (default):**\n```yaml\nfeatures:\n  apm:\n    instrumentation:\n      enabled: true\n```\n\n**Option B — Specific namespaces only:**\n```yaml\nfeatures:\n  apm:\n    instrumentation:\n      enabled: true\n      enabledNamespaces:\n        - \u003CAPP_NAMESPACE>\n```\n\n**Option C — Cluster-wide with exclusions:**\n```yaml\nfeatures:\n  apm:\n    instrumentation:\n      enabled: true\n      disabledNamespaces:\n        - jenkins\n        - kube-system\n```\n\n**Option D — Target specific workloads:**\n```yaml\nfeatures:\n  apm:\n    instrumentation:\n      enabled: true\n      targets:\n        - name: \u003CTARGET_NAME>\n          namespaceSelector:\n            matchNames:\n              - \u003CAPP_NAMESPACE>\n          ddTraceVersions:\n            \u003CLANGUAGE>: \"\u003CMAJOR_VERSION>\"\n```\n\n> **Note:** `ddTraceVersions` only applies inside a `targets[]` entry (Option D). It is not valid alongside `enabledNamespaces` or at the `instrumentation` level directly.\n\n### Claude runs\n\n```bash\nkubectl apply -f datadog-agent.yaml\n```\n\nIf `datadogagent.datadoghq.com\u002Fdatadog configured` — continue to Step 2.\n\nERROR: Validation error — check YAML. `enabledNamespaces` and `disabledNamespaces` cannot both be set.\n\n---\n\n## Step 2: Inform the User About Unified Service Tags\n\n> **Do NOT modify application Deployments without explicit user confirmation.** Applying labels to existing application workloads is a change to customer-managed resources.\n\nInform the user that adding Unified Service Tags (UST) to their Deployments will enable proper service\u002Fenv\u002Fversion tagging in Datadog. This is optional for SSI to work but recommended for full observability:\n\n```yaml\n# Add to both metadata.labels and spec.template.metadata.labels\ntags.datadoghq.com\u002Fenv: \"\u003CENV>\"\ntags.datadoghq.com\u002Fservice: \"\u003CSERVICE_NAME>\"\ntags.datadoghq.com\u002Fversion: \"\u003CVERSION>\"\n```\n\nIf the user wants you to apply these, get their confirmation first. UST labels are not required for APM traces to flow — SSI works without them.\n\n---\n\n## Step 3: Restart Application Pods\n\n> **Confirm with the user before restarting.** Tell the user: \"I need to restart `\u003CDEPLOYMENT_NAME>` in `\u003CAPP_NAMESPACE>` for SSI to inject into the pods. This will cause a brief outage. Ready to proceed?\" Wait for confirmation.\n\n### Claude runs\n\n```bash\nkubectl rollout restart deployment\u002F\u003CDEPLOYMENT_NAME> -n \u003CAPP_NAMESPACE>\n\nkubectl wait --for=condition=Ready pod \\\n  -l app=\u003CAPP_LABEL> \\\n  -n \u003CAPP_NAMESPACE> \\\n  --timeout=120s\n```\n\nIf pods restart cleanly, init containers named `datadog-lib-\u003Clanguage>-init` will be visible in the pod spec.\n\nERROR: Pods crash-looping — check for existing custom instrumentation. 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intentional",{"type":46,"tag":341,"props":2629,"children":2630},{},[2631,2633,2639],{"type":52,"value":2632},"Never run ",{"type":46,"tag":71,"props":2634,"children":2636},{"className":2635},[],[2637],{"type":52,"value":2638},"kubectl delete",{"type":52,"value":2640}," without user confirmation",{"type":46,"tag":341,"props":2642,"children":2643},{},[2644,2649],{"type":46,"tag":71,"props":2645,"children":2647},{"className":2646},[],[2648],{"type":52,"value":1190},{"type":52,"value":2650}," to a registry always requires user confirmation",{"type":46,"tag":341,"props":2652,"children":2653},{},[2654,2667,2669,2675,2677,2682],{"type":46,"tag":63,"props":2655,"children":2656},{},[2657,2659,2665],{"type":52,"value":2658},"Never use ",{"type":46,"tag":71,"props":2660,"children":2662},{"className":2661},[],[2663],{"type":52,"value":2664},"kubectl patch",{"type":52,"value":2666}," to apply UST labels or any Deployment changes.",{"type":52,"value":2668}," Always edit the Deployment YAML file and ",{"type":46,"tag":71,"props":2670,"children":2672},{"className":2671},[],[2673],{"type":52,"value":2674},"kubectl apply -f",{"type":52,"value":2676},". Changes made with ",{"type":46,"tag":71,"props":2678,"children":2680},{"className":2679},[],[2681],{"type":52,"value":2664},{"type":52,"value":2683}," are transient and will be overwritten on the next rollout.",{"type":46,"tag":2685,"props":2686,"children":2687},"style",{},[2688],{"type":52,"value":2689},"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":2691,"total":2862},[2692,2702,2720,2731,2747,2761,2777,2791,2803,2815,2827,2847],{"slug":374,"name":374,"fn":2693,"description":2694,"org":2695,"tags":2696,"stars":25,"repoUrl":26,"updatedAt":2701},"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},[2697,2698,2699,2700],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},"2026-04-15T04:57:27.489805",{"slug":2703,"name":2703,"fn":2704,"description":2705,"org":2706,"tags":2707,"stars":25,"repoUrl":26,"updatedAt":2719},"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},[2708,2709,2712,2715,2718],{"name":17,"slug":18,"type":15},{"name":2710,"slug":2711,"type":15},"Debugging","debugging",{"name":2713,"slug":2714,"type":15},"Evals","evals",{"name":2716,"slug":2717,"type":15},"LLM","llm",{"name":13,"slug":14,"type":15},"2026-07-31T05:52:13.711906",{"slug":2721,"name":2721,"fn":2722,"description":2723,"org":2724,"tags":2725,"stars":25,"repoUrl":26,"updatedAt":2730},"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},[2726,2727,2728,2729],{"name":17,"slug":18,"type":15},{"name":2713,"slug":2714,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:19.259734",{"slug":2732,"name":2732,"fn":2733,"description":2734,"org":2735,"tags":2736,"stars":25,"repoUrl":26,"updatedAt":2746},"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},[2737,2740,2743,2744,2745],{"name":2738,"slug":2739,"type":15},"Agents","agents",{"name":2741,"slug":2742,"type":15},"Data Pipeline","data-pipeline",{"name":17,"slug":18,"type":15},{"name":2713,"slug":2714,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:17.231423",{"slug":2748,"name":2748,"fn":2749,"description":2750,"org":2751,"tags":2752,"stars":25,"repoUrl":26,"updatedAt":2760},"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},[2753,2756,2757,2758,2759],{"name":2754,"slug":2755,"type":15},"Analytics","analytics",{"name":17,"slug":18,"type":15},{"name":2713,"slug":2714,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:21.212498",{"slug":2762,"name":2762,"fn":2763,"description":2764,"org":2765,"tags":2766,"stars":25,"repoUrl":26,"updatedAt":2776},"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},[2767,2768,2771,2772,2773],{"name":17,"slug":18,"type":15},{"name":2769,"slug":2770,"type":15},"Jupyter","jupyter",{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},{"name":2774,"slug":2775,"type":15},"Python","python","2026-06-19T09:04:22.640384",{"slug":2778,"name":2778,"fn":2779,"description":2780,"org":2781,"tags":2782,"stars":25,"repoUrl":26,"updatedAt":2790},"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},[2783,2784,2785,2786,2787],{"name":17,"slug":18,"type":15},{"name":2710,"slug":2711,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},{"name":2788,"slug":2789,"type":15},"Tracing","tracing","2026-07-31T05:52:08.594182",{"slug":2792,"name":2792,"fn":2793,"description":2794,"org":2795,"tags":2796,"stars":25,"repoUrl":26,"updatedAt":2802},"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},[2797,2798,2799,2800,2801],{"name":2754,"slug":2755,"type":15},{"name":17,"slug":18,"type":15},{"name":2713,"slug":2714,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:26.341497",{"slug":2804,"name":2804,"fn":2805,"description":2806,"org":2807,"tags":2808,"stars":25,"repoUrl":26,"updatedAt":2814},"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},[2809,2810,2811,2812,2813],{"name":17,"slug":18,"type":15},{"name":2710,"slug":2711,"type":15},{"name":2713,"slug":2714,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},"2026-06-19T09:04:24.316244",{"slug":2816,"name":2816,"fn":2817,"description":2818,"org":2819,"tags":2820,"stars":25,"repoUrl":26,"updatedAt":2826},"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},[2821,2822,2825],{"name":17,"slug":18,"type":15},{"name":2823,"slug":2824,"type":15},"Monitoring","monitoring",{"name":13,"slug":14,"type":15},"2026-04-06T18:08:33.337476",{"slug":2828,"name":2828,"fn":2829,"description":2830,"org":2831,"tags":2832,"stars":25,"repoUrl":26,"updatedAt":2846},"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},[2833,2834,2837,2840,2843],{"name":17,"slug":18,"type":15},{"name":2835,"slug":2836,"type":15},"Frontend","frontend",{"name":2838,"slug":2839,"type":15},"React","react",{"name":2841,"slug":2842,"type":15},"TypeScript","typescript",{"name":2844,"slug":2845,"type":15},"Vite","vite","2026-06-18T08:01:32.562331",{"slug":2848,"name":2848,"fn":2849,"description":2850,"org":2851,"tags":2852,"stars":25,"repoUrl":26,"updatedAt":2861},"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},[2853,2854,2857,2858],{"name":17,"slug":18,"type":15},{"name":2855,"slug":2856,"type":15},"Distributed Tracing","distributed-tracing",{"name":13,"slug":14,"type":15},{"name":2859,"slug":2860,"type":15},"Performance","performance","2026-04-06T18:08:34.575282",35,{"items":2864,"total":2919},[2865,2872,2880,2887,2895,2903,2911],{"slug":374,"name":374,"fn":2693,"description":2694,"org":2866,"tags":2867,"stars":25,"repoUrl":26,"updatedAt":2701},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2868,2869,2870,2871],{"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":2703,"name":2703,"fn":2704,"description":2705,"org":2873,"tags":2874,"stars":25,"repoUrl":26,"updatedAt":2719},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2875,2876,2877,2878,2879],{"name":17,"slug":18,"type":15},{"name":2710,"slug":2711,"type":15},{"name":2713,"slug":2714,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},{"slug":2721,"name":2721,"fn":2722,"description":2723,"org":2881,"tags":2882,"stars":25,"repoUrl":26,"updatedAt":2730},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2883,2884,2885,2886],{"name":17,"slug":18,"type":15},{"name":2713,"slug":2714,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},{"slug":2732,"name":2732,"fn":2733,"description":2734,"org":2888,"tags":2889,"stars":25,"repoUrl":26,"updatedAt":2746},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2890,2891,2892,2893,2894],{"name":2738,"slug":2739,"type":15},{"name":2741,"slug":2742,"type":15},{"name":17,"slug":18,"type":15},{"name":2713,"slug":2714,"type":15},{"name":13,"slug":14,"type":15},{"slug":2748,"name":2748,"fn":2749,"description":2750,"org":2896,"tags":2897,"stars":25,"repoUrl":26,"updatedAt":2760},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2898,2899,2900,2901,2902],{"name":2754,"slug":2755,"type":15},{"name":17,"slug":18,"type":15},{"name":2713,"slug":2714,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},{"slug":2762,"name":2762,"fn":2763,"description":2764,"org":2904,"tags":2905,"stars":25,"repoUrl":26,"updatedAt":2776},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2906,2907,2908,2909,2910],{"name":17,"slug":18,"type":15},{"name":2769,"slug":2770,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},{"name":2774,"slug":2775,"type":15},{"slug":2778,"name":2778,"fn":2779,"description":2780,"org":2912,"tags":2913,"stars":25,"repoUrl":26,"updatedAt":2790},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2914,2915,2916,2917,2918],{"name":17,"slug":18,"type":15},{"name":2710,"slug":2711,"type":15},{"name":2716,"slug":2717,"type":15},{"name":13,"slug":14,"type":15},{"name":2788,"slug":2789,"type":15},34]