Onboarding Summary logo

Onboarding Summary

Organization
datadog-labs
onboarding-summary

Generate a live Single Step Instrumentation (SSI) onboarding confirmation report — verifies APM instrumentation is working end-to-end with deep links into the Datadog UI. Only use after agent-install and enable-ssi have both completed successfully.

Overview

Publisherdatadog-labs
Repositoryagent-skills
Skill nameonboarding-summary
Stars
172
Forks
28
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • 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 Onboarding Summary 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/onboarding-summary .claude/skills/onboarding-summary
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Onboarding Summary 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 Onboarding Summary 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 Onboarding Summary 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.

APM Onboarding Summary

Triggers

Invoke this skill when:

  • All steps in verify-ssi have passed
  • All checks in troubleshoot-ssi have been resolved
  • The user asks "is everything working?", "show me the status", or "confirm APM is set up"

Do NOT invoke this skill if any verification or troubleshooting check is still failing — resolve those first.

Context to resolve before acting

VariableHow to resolve
AGENT_NAMESPACENamespace where Datadog Agent is installed
APP_NAMESPACENamespace of the application
APP_LABELCheck spec.selector.matchLabels.app in the Deployment manifest
CLUSTER_NAMEspec.global.clusterName in datadog-agent.yaml
SERVICE_NAMEtags.datadoghq.com/service label on the Deployment
ENVtags.datadoghq.com/env label on the Deployment
DD_SITEspec.global.site in datadog-agent.yaml

Prerequisites

Claude runs

bash
pup auth status --site <DD_SITE>

If valid token — proceed.

ERROR: Not authenticated:

Claude runs

bash
pup auth login --site <DD_SITE>

This opens a browser tab for OAuth. Complete the login there — Claude will continue once the command exits.

Collect live confirmation data

Run all of the following. Each populates a row in the final report.

Claude runs

bash
# Agent pod count and status
kubectl get pods -n <AGENT_NAMESPACE> \
  -l app.kubernetes.io/component=agent \
  --no-headers

# SSI instrumentation config live in cluster
kubectl get datadogagent datadog -n <AGENT_NAMESPACE> \
  -o jsonpath='{.spec.features.apm.instrumentation}'

# Init container confirmed in app pod spec
kubectl get pod -l app=<APP_LABEL> -n <APP_NAMESPACE> \
  -o jsonpath='{.items[0].spec.initContainers[*].name}'

# Service visible and traced in APM
DD_SITE=<DD_SITE> pup apm services list --env <ENV> --from 1h

# Traces arriving in the last hour
DD_SITE=<DD_SITE> pup traces search --query "service:<SERVICE_NAME>" --from 1h --limit 5

Present the report

Fill in every value from live command output. Do not leave any placeholder unfilled. If a value cannot be confirmed, mark that row as failed and link to troubleshoot-ssi.

APM onboarding complete

CheckDetailStatus
Datadog Agent<N> pod(s) Running in <AGENT_NAMESPACE>OK
SSI enabledTargeting namespace <APP_NAMESPACE>, language <LANGUAGE> v<MAJOR_VERSION>OK
Init container injecteddatadog-lib-<language>-init present in pod specOK
Tracer reportingService <SERVICE_NAME> appears in pup apm services list with isTraced: trueOK
APM service visible<SERVICE_NAME> in env <ENV>OK
Traces arriving<N> trace(s) found in the last hourOK

Your service in Datadog — click to open:

Construct each URL by substituting real values. Do not print placeholder URLs.

ViewURL
Service overviewhttps://app.<DD_SITE>/apm/services/<SERVICE_NAME>?env=<ENV>
Traces explorerhttps://app.<DD_SITE>/apm/traces?query=service:<SERVICE_NAME>%20env:<ENV>
Service maphttps://app.<DD_SITE>/apm/map?env=<ENV>&service=<SERVICE_NAME>
Agent fleethttps://app.<DD_SITE>/fleet-automation

Security constraints

  • Never write a raw API key into any file or chat message

Frequently asked questions

What does the Onboarding Summary AI skill do?

Generate a live Single Step Instrumentation (SSI) onboarding confirmation report — verifies APM instrumentation is working end-to-end with deep links into the Datadog UI. Only use after agent-install and enable-ssi have both completed successfully.

Why use Onboarding Summary on TypingMind?

Because you install it once and use it with any model. Onboarding Summary 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 Onboarding Summary 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/onboarding-summary. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Onboarding Summary?

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 Onboarding Summary?

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

Is the Onboarding Summary 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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