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Agent Install

Organization
datadog-labs
agent-install

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.

Overview

Publisherdatadog-labs
Repositoryagent-skills
Skill nameagent-install
Stars
172
Forks
28
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by datadog-labs on GitHub. Read the source before you install it.

Installation

Install the Agent Install AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/datadog-labs/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/dd-apm/k8s-ssi/agent-install .claude/skills/agent-install
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Install in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Install on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Install is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Install the Datadog Agent 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.

Phase 0: Load Credentials

bash
[ -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.


Prerequisites

  • Kubernetes v1.20+ — kubectl version
  • helm v3+ — helm version
  • kubectl configured to target cluster — kubectl config current-context
  • pup-cli installed — check with pup --version; if missing, install it now:
    bash
    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.

Context to resolve before acting

VariableHow to resolve
CLUSTER_NAMECheck repo IaC, scripts, or kubectl config current-context
DD_SITEAsk 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_NAMESPACEUse datadog unless the repo already uses datadog-agent consistently
CHART_VERSIONRun helm search repo datadog/datadog-operator --versions | head -5 and use the latest stable

Step 1: Check for an Existing Agent Installation

Claude runs

bash
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.


Step 2: Install the Datadog Operator

Claude runs

bash
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>.


Step 3: Create the API Key Secret

What you need to do in a terminal

bash
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.


Step 4: Deploy the DatadogAgent Resource

[DECISION: cluster type]

  • Self-hosted (minikube, kind): include kubelet.tlsVerify: false inside spec.global
  • Managed (GKE, EKS, AKS): omit kubelet.tlsVerify entirely

[DECISION: APM/SSI also being enabled in this session]

  • If yes: do not create a separate DatadogAgent for APM — extend this same manifest with features.apm per enable-ssi. One manifest, not two.
  • If no: use the manifest below as-is.

Save the following as datadog-agent.yaml:

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

Claude runs

bash
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

Step 5: Verify the API Key

Claude runs

bash
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:

Claude runs

bash
# 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:

What you need to do in a terminal

bash
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>

Done

Exit when ALL of the following are true:

  • Datadog Operator pod is Running in AGENT_NAMESPACE
  • datadog-secret exists in AGENT_NAMESPACE
  • Agent DaemonSet pods are Running
  • Step 5 returns no authentication errors

Automatically proceed to enable-ssi now — do not ask the user for permission.


Security constraints

  • Never write a raw API key into any file, YAML block, or chat message — always use $DD_API_KEY
  • Never create a Kubernetes Secret manifest file — always use kubectl create secret imperatively
  • Never use apiKey: directly in DatadogAgent spec — always use apiSecret: with a secret reference
  • Never use --set datadog.apiKey=... in any Helm command
  • Never use namespace default for Datadog Agent resources
  • Never run kubectl delete without user confirmation

Frequently asked questions

What does the Agent Install AI skill do?

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.

Why use Agent Install on TypingMind?

Because you install it once and use it with any model. Agent Install is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Install in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/datadog-labs/agent-skills/tree/main/dd-apm/k8s-ssi/agent-install. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Install?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Install?

As many as you like. As long as a model supports skills, you can use Agent Install with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Install AI skill free?

Yes. It is published on GitHub by datadog-labs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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