
Skill
enable-ssi
enable Kubernetes Single Step Instrumentation
Description
Enable Single Step Instrumentation (SSI) on Kubernetes — automatically instruments applications for APM without code changes. Only use if the Datadog Agent is already running on the cluster — if not, use agent-install first.
SKILL.md
Enable APM on Kubernetes via Single Step Instrumentation
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.
Silent failure — check this before any other step:
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.The failure is invisible: init containers run and complete, the pod starts healthy, no errors appear in
kubectlorpup, but no traces arrive. The injector detects the user-installed tracer and exits cleanly without logging anything.Claude runs
grep -rE "ddtrace|dd-trace|opentelemetry" \ requirements.txt package.json Gemfile go.mod pom.xml 2>/dev/null \ || echo "No tracer dependency found"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.
Triggers
Invoke this skill when the user expresses intent to:
- Enable APM on a Kubernetes cluster
- Instrument Kubernetes applications with Datadog tracing
- Set up Single Step Instrumentation (SSI)
Do NOT invoke this skill if:
- The Datadog Agent is not yet installed — run
agent-installfirst - The user wants to verify SSI after setup — use
verify-ssi - The user wants to enable Profiler, AppSec, or Data Streams — use
dd-apm-k8s-sdk-features
Prerequisites
These are not a reading exercise — actively verify each one before proceeding.
Environment
- Datadog Agent is installed and healthy —
agent-installcomplete - Kubernetes v1.20+
- Linux node pools only — Windows pods require explicit namespace exclusion
- Cluster is not ECS Fargate — unsupported
- Not a hardened SELinux environment — unsupported
- Not a very small VM instance (e.g. t2.micro) — SSI can hit init timeouts
- No PodSecurity baseline or restricted policy enforced
Language and runtime
- Application language is one of: Java, Python, Ruby, Node.js, .NET, PHP
- Runtime version is within SSI's supported range — verify against the SSI compatibility matrix
- Node.js app is not using ESM — SSI does not support ESM
- Java app is not already using a
-javaagentJVM flag
Existing instrumentation — confirmed clean by the check at the top of this skill. If you skipped that check, go back and run it now.
Context to resolve before acting
Discover from the cluster — do not ask the user for information you can find yourself.
| Variable | How to resolve |
|---|---|
AGENT_NAMESPACE | Same namespace used in agent-install (e.g. datadog) |
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 |
TARGET_LANGUAGES | Run kubectl get pods -A -o jsonpath='{.items[*].spec.containers[*].image}' and infer language from image names, or check Dockerfiles/manifests in the workspace. If uncertain, enable all languages. |
DEPLOYMENT_NAME | Run kubectl get deployments -A --no-headers — identify application deployments (exclude system components) |
APP_LABEL | Check spec.selector.matchLabels in the Deployment manifest via kubectl get deployment <DEPLOYMENT_NAME> -n <APP_NAMESPACE> -o yaml |
CLUSTER_NAME | Check spec.global.clusterName in datadog-agent.yaml, or kubectl config current-context — needed for kind clusters in Step 0 |
ENV | Use apm-evals if running in an eval cluster (kind cluster names contain "evalya"). Otherwise use production unless the user specifies otherwise. |
SERVICE_NAME | Use the deployment name (e.g. python-app → service python-app). Do not ask the user. |
VERSION | Use 1.0.0 as the default. Do not ask the user. |
Step 0 (Only if existing instrumentation detected): Remove Manual Instrumentation
Scan all source files for: import ddtrace, from ddtrace, require 'ddtrace', require("dd-trace"), opentelemetry, tracer.trace(
Also check dependency manifests for ddtrace / dd-trace / OTel SDK packages.
If found — remove the import/package, then rebuild and reload:
Claude runs
docker build -f <DOCKERFILE_PATH> -t <IMAGE_NAME> <BUILD_CONTEXT>
DECISION: how does this cluster get local images?
Check 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:
| What you find in the setup script | Load command |
|---|---|
minikube image load or minikube cache add | minikube -p <PROFILE> image load <IMAGE_NAME> — profile is the -p flag value in the script, NOT necessarily the kubectl context name |
kind load docker-image | kind load docker-image <IMAGE_NAME> --name <CLUSTER_NAME> |
docker push to a registry | Push the new image; the cluster will pull on restart — skip local load |
k3d image import | k3d image import <IMAGE_NAME> -c <CLUSTER_NAME> |
| No image load step (cloud cluster, always pulls from registry) | Skip — image will be pulled on next deployment |
If the setup script is ambiguous, run the load command it uses exactly as written.
- Registry-based: skip — image will be pulled on next deployment
Confirm with the user before restarting. Tell the user: "I need to restart
<DEPLOYMENT_NAME>in<APP_NAMESPACE>to pick up the rebuilt image. Ready to proceed?" Wait for confirmation.
Claude runs
kubectl rollout restart deployment/<DEPLOYMENT_NAME> -n <APP_NAMESPACE>
kubectl wait --for=condition=Ready pod \
-l app=<APP_LABEL> \
-n <APP_NAMESPACE> \
--timeout=120s
Step 1: Extend the DatadogAgent Manifest with APM
SSI is configured on the existing DatadogAgent resource — do not create a separate manifest.
Choose targeting scope based on what the user asked for:
- User asked to instrument all applications or didn't specify scope → use Option A (cluster-wide)
- User asked for specific namespaces only → use Option B
- User asked to exclude namespaces from cluster-wide → use Option C
- User asked for specific pods/workloads → use Option D
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
enabledNamespacesortargets.
Recommended ddTraceVersions: java: "1", python: "2", js: "5", dotnet: "3", ruby: "2", php: "1"
Option A — Cluster-wide (default):
features:
apm:
instrumentation:
enabled: true
Option B — Specific namespaces only:
features:
apm:
instrumentation:
enabled: true
enabledNamespaces:
- <APP_NAMESPACE>
Option C — Cluster-wide with exclusions:
features:
apm:
instrumentation:
enabled: true
disabledNamespaces:
- jenkins
- kube-system
Option D — Target specific workloads:
features:
apm:
instrumentation:
enabled: true
targets:
- name: <TARGET_NAME>
namespaceSelector:
matchNames:
- <APP_NAMESPACE>
ddTraceVersions:
<LANGUAGE>: "<MAJOR_VERSION>"
Note:
ddTraceVersionsonly applies inside atargets[]entry (Option D). It is not valid alongsideenabledNamespacesor at theinstrumentationlevel directly.
Claude runs
kubectl apply -f datadog-agent.yaml
If datadogagent.datadoghq.com/datadog configured — continue to Step 2.
ERROR: Validation error — check YAML. enabledNamespaces and disabledNamespaces cannot both be set.
Step 2: Inform the User About Unified Service Tags
Do NOT modify application Deployments without explicit user confirmation. Applying labels to existing application workloads is a change to customer-managed resources.
Inform the user that adding Unified Service Tags (UST) to their Deployments will enable proper service/env/version tagging in Datadog. This is optional for SSI to work but recommended for full observability:
# Add to both metadata.labels and spec.template.metadata.labels
tags.datadoghq.com/env: "<ENV>"
tags.datadoghq.com/service: "<SERVICE_NAME>"
tags.datadoghq.com/version: "<VERSION>"
If 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.
Step 3: Restart Application Pods
Confirm with the user before restarting. Tell the user: "I need to restart
<DEPLOYMENT_NAME>in<APP_NAMESPACE>for SSI to inject into the pods. This will cause a brief outage. Ready to proceed?" Wait for confirmation.
Claude runs
kubectl rollout restart deployment/<DEPLOYMENT_NAME> -n <APP_NAMESPACE>
kubectl wait --for=condition=Ready pod \
-l app=<APP_LABEL> \
-n <APP_NAMESPACE> \
--timeout=120s
If pods restart cleanly, init containers named datadog-lib-<language>-init will be visible in the pod spec.
ERROR: Pods crash-looping — check for existing custom instrumentation. See troubleshoot-ssi.
Done
Exit when ALL of the following are true:
-
features.apm.instrumentationis present in the appliedDatadogAgentmanifest - User has been informed that they need to restart their application pods
- User has been informed about Unified Service Tags (UST) and how to apply them if desired
- Scope confirmed: which workloads are instrumented, which were skipped and why
Automatically proceed to verify-ssi now — do not ask the user for permission.
Security constraints
- Never write a raw API key into any file or chat message
- Never use namespace
defaultfor Datadog resources - Never modify
admissionControllersettings directly — SSI manages this via the Operator - Do not add APM config to application manifests — configure only via
DatadogAgent - Exception: UST labels (
tags.datadoghq.com/*) on application Deployments are required and intentional - Never run
kubectl deletewithout user confirmation docker pushto a registry always requires user confirmation- Never use
kubectl patchto apply UST labels or any Deployment changes. Always edit the Deployment YAML file andkubectl apply -f. Changes made withkubectl patchare transient and will be overwritten on the next rollout.
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