AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a systematic verification process to ensure that Single Step Instrumentation (SSI) is correctly configured and operational within a Kubernetes cluster. It validates that init containers are properly injected, the tracer is successfully reporting telemetry to the Datadog platform, and that specific tracer configurations are correctly applied to application pods. It is intended for use immediately following the initial SSI setup to confirm successful deployment.
What it does
- Verifies the presence of Datadog SSI init containers in Kubernetes pods.
- Checks if the application service is correctly reporting traces to Datadog.
- Validates that custom tracer configurations are correctly applied as environment variables.
- Automates traffic generation to verify telemetry reporting for inactive services.
- Provides a clear path to troubleshooting if verification steps fail.
- Automatically transitions to an onboarding summary upon successful verification.
When to use it
- Immediately after running the enable-ssi skill.
- When you need to confirm that pods are being instrumented without code changes.
- To verify that the tracer is active and reporting telemetry for a specific service.
- To audit whether custom tracer configurations have reached the target application pods.
Advantages
- Ensures end-to-end visibility into the SSI instrumentation process.
- Reduces manual effort by automating pod inspection and traffic generation.
- Provides clear diagnostic feedback for configuration mismatches.
- Standardizes the verification workflow across different services and environments.
Usage examples
- “Verify that my SSI setup is working for the order-service in the production namespace.”
- The agent will inspect the pod's init containers, check the Datadog service list for active traces, and confirm that all environment-based configurations are correctly applied.
- “Is my application being instrumented by SSI correctly?”
- The agent will guide you through the verification steps, checking for the necessary init containers and confirming telemetry reporting.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/datadog-labs/verify-ssi-vnma/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip verify-ssi.zip -d .agents/skills
Documentation by datadog-labs
Verify APM SSI on Kubernetes
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.
Triggers
Invoke this skill when the user expresses intent to:
Confirm SSI is working after enabling APM
Check whether pods are being instrumented
Verify the tracer is running and reporting telemetry
Confirm tracer config is applied correctly
Do NOT invoke this skill if:
SSI has not been enabled yet — run
enable-ssifirstPods are not being instrumented at all — use
troubleshoot-ssi
Prerequisites
[ ]
enable-ssiis complete[ ] Application pods have been restarted since SSI was enabled
pup-cli: check, install, and authenticate
Claude runs
pup --versionIf not found:
Claude runs
brew tap datadog-labs/pack
brew install datadog-labs/pack/pupCheck auth:
pup auth status --site <DD_SITE>If not authenticated:
Claude runs
pup auth login --site <DD_SITE>This opens a browser tab for OAuth. Complete the login there — Claude will continue once the command exits.
If valid token — proceed.
ERROR: No browser available — use API key fallback: export DD_APP_KEY=<your-app-key>
Context to resolve before acting
| Variable | How to resolve |
|---|---|
CLUSTER_NAME | Check spec.global.clusterName in datadog-agent.yaml, or kubectl config current-context |
ENV | Check tags.datadoghq.com/env label on the application Deployment |
SERVICE_NAME | Check tags.datadoghq.com/service label on the application Deployment |
Step 1: Confirm Pods are Instrumented
Claude runs
kubectl get pod -l app=<APP_LABEL> -n <APP_NAMESPACE> \
-o jsonpath='{.items[0].spec.initContainers[*].name}'If the output includes datadog-lib-<language>-init and datadog-init-apm-inject — SSI init containers are injected.
ERROR: Init containers missing — pod was not restarted after SSI was enabled, or namespace targeting is not matching. Restart the pod and recheck.
Step 2: Confirm the Tracer is Reporting Telemetry
Claude runs
DD_SITE=<DD_SITE> pup apm services list --env <ENV> --from 1hIf <SERVICE_NAME> appears in the services list with isTraced: true — continue to Step 3.
ERROR: Service missing — send some traffic to the app first, then retry:
Claude runs
# Port-forward and send test traffic
kubectl port-forward deployment/<DEPLOYMENT_NAME> 8099:8000 -n <APP_NAMESPACE> &
sleep 2 && for i in $(seq 1 10); do curl -s -o /dev/null http://localhost:8099/; done
sleep 30 && kill %1 2>/dev/null
DD_SITE=<DD_SITE> pup apm services list --env <ENV> --from 10mERROR: Still missing after traffic — check the agent's trace receiver: kubectl exec -n <AGENT_NAMESPACE> <AGENT_POD> -c agent -- agent status | grep -A 10 "Receiver (previous minute)". If receiver shows 0 traces, go to troubleshoot-ssi.
Step 3: Confirm Tracer Configuration
Only run this step if ddTraceConfigs was explicitly configured in enable-ssi (e.g. profiling, AppSec, Data Streams). If basic SSI was set up without ddTraceConfigs, skip this step. No ddTraceConfigs means there is nothing to check.
The pod spec is the source of truth for whether ddTraceConfigs reached the workload. SSI adds each entry as an env var on the pod's containers.
Run this for a pod in each target that has ddTraceConfigs. For Data Streams, run it once per Deployment in DSM_SERVICES, in that Deployment's namespace, with <LABEL_KEY> set to DSM_LABEL_KEY and <APP_LABEL> to that Deployment's value (both from enable-dsm). Otherwise <LABEL_KEY> is app. A pod matched only by a target without ddTraceConfigs (such as the catch-all default target) should not carry these variables. That is expected, not a failure.
Claude runs
kubectl get pod -l <LABEL_KEY>=<APP_LABEL> -n <APP_NAMESPACE> --sort-by=.metadata.creationTimestamp \
-o jsonpath='{range .items[-1:].spec.containers[*]}{.name}{":"}{"\n"}{range .env[*]}{" "}{.name}={.value}{.valueFrom}{"\n"}{end}{end}' | grep -E '^[^ ].*:$|^ DD_'To see which target the pod matched:
kubectl get pod -l <LABEL_KEY>=<APP_LABEL> -n <APP_NAMESPACE> --sort-by=.metadata.creationTimestamp \
-o jsonpath='{.items[-1:].metadata.annotations.internal\.apm\.datadoghq\.com/applied-target}'If every ddTraceConfigs variable for that target appears in the application container with the configured value, the config reached the pod. This confirms delivery, not that the SDK acted on it; onboarding-summary checks the product data. Continue.
ERROR: A variable is missing or has a different value.
Different value: the Deployment sets that variable itself, and SSI never overwrites an existing variable. Remove it from the Deployment or align it.
Missing: check it is present in the
DatadogAgentmanifest under the target that matches this pod, and that the pod was restarted after the Cluster Agent finished rolling out.
Then check what the tracer reports at runtime. This output is informational; do not fail Step 3 on it.
Claude runs
pup apm service-library-config get \
--service-name <SERVICE_NAME> \
--env <ENV>This view does not list every setting for every SDK. For example, DD_DATA_STREAMS_ENABLED does not show for Java, Python, Ruby, or Go services even when DSM is on. A setting missing here is not a failure when it is present in the pod env. For Data Streams, onboarding-summary confirms data with data_streams.latency (or enable-dsm Step 3 when it was run standalone).
Done
Exit when ALL of the following are true:
[ ] Step 1: target pods have SSI init containers injected (
datadog-lib-<language>-initanddatadog-init-apm-inject)[ ] Step 2: service appears in
pup apm services listwithisTraced: true[ ] Step 3 (only if
ddTraceConfigswas set): eachddTraceConfigsvariable appears in the env of pods in its target with the configured value. A setting missing frompup apm service-library-config getis not a failure
If any check fails, go to troubleshoot-ssi.
When all steps pass, automatically proceed to onboarding-summary now — do not ask the user for permission.
Security constraints
Never write a raw API key into any file or chat message
Never run
kubectl deletewithout user confirmation
Files
- ATTRIBUTION.mdAdded by AutoGineer959 B
- LICENSELicence file added by AutoGineer1.1 KiB
- SKILL.md6.8 KiB
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs network access, Needs an API key, Needs a command-line tool
Security
What our scanner noticed· Low risk
- Phrasing that asks for actions without confirmation
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- datadog-labs
- Publisher
- Datadog (Official)
- Source
- datadog-labs/agent-skills · dd-apm/k8s-ssi/verify-ssi · commit 8e1306a
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by datadog-labs or Datadog.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download verify-ssi.zip