Verify Ssi logo

Verify Ssi

Organization
datadog-labs
verify-ssi

Verify Single Step Instrumentation (SSI) is working end-to-end on Kubernetes — SSI automatically instruments applications for APM without code changes. Only use after enable-ssi has run.

Overview

Publisherdatadog-labs
Repositoryagent-skills
Skill nameverify-ssi
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 Verify Ssi 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/verify-ssi .claude/skills/verify-ssi
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify Ssi 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 Verify Ssi 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 Verify Ssi 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.

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-ssi first
  • Pods are not being instrumented at all — use troubleshoot-ssi

Prerequisites

  • enable-ssi is complete
  • Application pods have been restarted since SSI was enabled

pup-cli: check, install, and authenticate

Claude runs

bash
pup --version

If not found:

Claude runs

bash
brew tap datadog-labs/pack
brew install pup

Check auth:

bash
pup auth status --site <DD_SITE>

If 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.

If valid token — proceed. ERROR: No browser available — use API key fallback: export DD_APP_KEY=<your-app-key>


Context to resolve before acting

VariableHow to resolve
CLUSTER_NAMECheck spec.global.clusterName in datadog-agent.yaml, or kubectl config current-context
ENVCheck tags.datadoghq.com/env label on the application Deployment
SERVICE_NAMECheck tags.datadoghq.com/service label on the application Deployment

Step 1: Confirm Pods are Instrumented

Claude runs

bash
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

bash
DD_SITE=<DD_SITE> pup apm services list --env <ENV> --from 1h

If <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

bash
# 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 10m

ERROR: 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 — an empty response here is expected and not a failure.

Claude runs

bash
pup apm service-library-config get \
  --service-name <SERVICE_NAME> \
  --env <ENV>

If the output shows expected environment variables matching what was configured in ddTraceConfigs — done.

If the output is empty and ddTraceConfigs was not configured — expected, not a failure.

ERROR: Config missing but ddTraceConfigs was configured — check it is present in the DatadogAgent manifest under the correct target, and that pods were restarted after the config change.


Done

Exit when ALL of the following are true:

  • Step 1: target pods have SSI init containers injected (datadog-lib-<language>-init and datadog-init-apm-inject)
  • Step 2: service appears in pup apm services list with isTraced: true
  • Step 3: tracer config matches what was set in DatadogAgent

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 delete without user confirmation

Frequently asked questions

What does the Verify Ssi AI skill do?

Verify Single Step Instrumentation (SSI) is working end-to-end on Kubernetes — SSI automatically instruments applications for APM without code changes. Only use after enable-ssi has run.

Why use Verify Ssi on TypingMind?

Because you install it once and use it with any model. Verify Ssi 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 Verify Ssi 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/verify-ssi. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Verify Ssi?

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 Verify Ssi?

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

Is the Verify Ssi 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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