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.
npx skills add https://github.com/datadog-labs/agent-skills --skill agent-install
> 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.
[ -f environment ] && source environment
echo "DD_API_KEY set: $([ -n "${DD_API_KEY:-}" ] && echo yes || echo no)"
echo "DD_SITE: ${DD_SITE:-not set}"
echo "helm: $(helm version --short 2>/dev/null || echo NOT FOUND)"
If helm is not found — tell the user:
> helm is required for this skill. Install it with:
> `bash
> brew install helm # macOS
> # or see https://helm.sh/docs/intro/install/ for other platforms
> `
> Once installed, let me know and I'll continue.
Do not proceed until helm is available.
If DD_API_KEY is already set — proceed to Prerequisites.
If DD_API_KEY is not set — tell the user:
> I need two things to continue:
>
> 1. Datadog API Key — used to authenticate the Agent with your Datadog account. You can find or create one at: https://app.datadoghq.com/organization-settings/api-keys
>
> 2. Datadog Site — the region your Datadog account is on. Most accounts use datadoghq.com. Check your Datadog URL to confirm (e.g. app.datadoghq.eu → site is datadoghq.eu). Other options: us3.datadoghq.com, us5.datadoghq.com, ap1.datadoghq.com.
>
> Please run the following in this chat to set your credentials (the ! prefix executes it in this session):
> `
> ! export DD_API_KEY=your-api-key-here
> ! export DD_SITE=datadoghq.com
> `
Wait for the user to run the commands, then re-run the check above before continuing.
kubectl versionhelm versionkubectl config current-contextpup --version; if missing, install it now: if [[ "$(uname)" == "Darwin" ]]; then
brew tap datadog-labs/pack && brew install pup
else
PUP_VERSION=$(curl -s https://api.github.com/repos/datadog-labs/pup/releases/latest | grep '"tag_name"' | cut -d'"' -f4)
curl -L "https://github.com/datadog-labs/pup/releases/download/${PUP_VERSION}/pup_linux_amd64.tar.gz" | tar xz -C /usr/local/bin pup
chmod +x /usr/local/bin/pup
fi
pup --version
Do not skip — proceed only once pup --version succeeds.
| Variable | How to resolve |
|---|---|
| CLUSTER_NAME | Check repo IaC, scripts, or kubectl config current-context |
| DD_SITE | Ask the user. Default: datadoghq.com. Common options: datadoghq.eu, us3.datadoghq.com, us5.datadoghq.com, ap1.datadoghq.com. Full list: https://docs.datadoghq.com/getting_started/site/ |
| AGENT_NAMESPACE | Use datadog unless the repo already uses datadog-agent consistently |
| CHART_VERSION | Run helm search repo datadog/datadog-operator --versions \| head -5 and use the latest stable |
helm list -A | grep -i datadog
If a release shows deployed — Agent already installed. Skip to Step 5 to confirm health, then exit.
If there is no output — no existing install. Continue to Step 2.
helm repo add datadog https://helm.datadoghq.com
helm repo update
helm upgrade --install datadog-operator datadog/datadog-operator \
--namespace <AGENT_NAMESPACE> \
--create-namespace \
--version <CHART_VERSION>
kubectl wait --for=condition=Ready pod \
-l app.kubernetes.io/name=datadog-operator \
-n <AGENT_NAMESPACE> \
--timeout=120s
If the Operator pod is Running — continue to Step 3.
ERROR: Pod not ready after 120s — check image pull: kubectl describe pod -l app.kubernetes.io/name=datadog-operator -n <AGENT_NAMESPACE>.
export DD_API_KEY=<your-api-key>
kubectl create secret generic datadog-secret \
--from-literal api-key=$DD_API_KEY \
--namespace <AGENT_NAMESPACE>
If secret/datadog-secret created — continue to Step 4.
ERROR: AlreadyExists — confirm which key it holds via Step 5 before deciding whether to recreate.
[DECISION: cluster type]
kubelet.tlsVerify: false inside spec.globalkubelet.tlsVerify entirely[DECISION: APM/SSI also being enabled in this session]
DatadogAgent for APM — extend this same manifest with features.apm per enable-ssi. One manifest, not two.Save the following as datadog-agent.yaml:
apiVersion: datadoghq.com/v2alpha1
kind: DatadogAgent
metadata:
name: datadog
namespace: <AGENT_NAMESPACE>
spec:
global:
clusterName: <CLUSTER_NAME>
site: <DD_SITE>
credentials:
apiSecret:
secretName: datadog-secret
keyName: api-key
# Self-hosted clusters only (minikube, kind):
# kubelet:
# tlsVerify: false
features:
orchestratorExplorer:
enabled: true
clusterChecks:
enabled: true
logCollection:
enabled: true
containerCollectAll: false
kubectl apply -f datadog-agent.yaml
kubectl wait --for=condition=Ready pod \
-l app.kubernetes.io/component=agent \
-n <AGENT_NAMESPACE> \
--timeout=120s 2>/dev/null || true
kubectl logs -l app.kubernetes.io/component=agent \
-n <AGENT_NAMESPACE> \
--tail=50 2>/dev/null \
| grep -iE "invalid.*api\.?key|api\.?key.*invalid" \
|| echo "No authentication errors found"
If No authentication errors found — key is accepted.
ERROR: Authentication errors found — validate credentials directly:
# Prefer pup (OAuth) — fall back to curl with API key
if pup auth status 2>/dev/null | grep -q "Logged in"; then
echo "pup OAuth authenticated"
elif [ -n "${DD_API_KEY:-}" ]; then
RESPONSE=$(curl -s -o /dev/null -w "%{http_code}" \
-X GET "https://api.<DD_SITE>/api/v1/validate" \
-H "DD-API-KEY: $DD_API_KEY")
if [ "$RESPONSE" = "200" ]; then
echo "API key is valid for <DD_SITE>"
else
echo "ERROR: Validation failed (HTTP $RESPONSE) — check key and site alignment"
fi
else
echo "ERROR: No credentials available — run 'pup auth login' or set DD_API_KEY"
fi
If key is invalid:
export DD_API_KEY=<new-api-key>
kubectl delete secret datadog-secret -n <AGENT_NAMESPACE>
kubectl create secret generic datadog-secret \
--from-literal api-key=$DD_API_KEY \
-n <AGENT_NAMESPACE>
kubectl rollout restart daemonset datadog-agent -n <AGENT_NAMESPACE>
kubectl rollout restart deployment datadog-cluster-agent -n <AGENT_NAMESPACE>
Exit when ALL of the following are true:
AGENT_NAMESPACEdatadog-secret exists in AGENT_NAMESPACEAutomatically proceed to enable-ssi now — do not ask the user for permission.
$DD_API_KEYkubectl create secret imperativelyapiKey: directly in DatadogAgent spec — always use apiSecret: with a secret reference--set datadog.apiKey=... in any Helm commanddefault for Datadog Agent resourceskubectl delete without user confirmationAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take datadog-labs/agent-install from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference brew.
Without those the skill loads but fails at the first command.