Verify Ssi logo

Verify Ssi

Organization
datadog-labs
verify-ssi

Verify Single Step Instrumentation (SSI) is working end-to-end on Linux hosts — 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/linux-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 Linux

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 installing the Datadog Agent on Linux
  • Check whether a Linux process is being instrumented
  • Verify the tracer is running and reporting telemetry

Do NOT invoke this skill if:

  • SSI has not been enabled yet — run agent-install first
  • Services have not been restarted since the agent was installed — restart them first, then verify

Prerequisites

  • agent-install is complete
  • Application services have been restarted since the agent was installed

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: export DD_APP_KEY=<your-app-key>


Context to resolve before acting

VariableHow to resolve
DD_HOSTNAMEHostname as Datadog sees it — from sudo datadog-agent status output
SERVICE_NAMEExpected service name in APM — ask the user
ENVEnvironment tag — ask the user
DD_SITEgrep "^site:" /etc/datadog-agent/datadog.yaml via SSH, or ask the user
SSH_KEYPath to SSH private key
SSH_USERSSH username
SSH_HOSTHostname or IP of the target host

Step 1: Confirm the Process is Injected

Use /proc/<pid>/maps — this is the authoritative check. It shows the actual shared libraries loaded into the running process, which is the only way to confirm the launcher and tracer .so files were actually loaded.

Claude runs

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "pgrep -a -f '<SERVICE_NAME>' | head -5"

Use the PID from above:

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "sudo cat /proc/<PID>/maps | grep -E 'launcher|apm-library|datadog'"

If the output includes both the launcher (e.g. launcher.preload.so) and a language library (e.g. apm-library-python) — injection succeeded for this process.

ERROR: Launcher present but no language library — launcher ran but couldn't inject. Check for injection errors:

Claude runs

bash
pup apm troubleshooting list --hostname <DD_HOSTNAME> --timeframe 1h

ERROR: Neither present — process was not injected. Check /etc/ld.so.preload:

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> "cat /etc/ld.so.preload"

If empty — install did not set up the launcher. Re-run the install script with DD_APM_INSTRUMENTATION_ENABLED=host. If non-empty but the process still isn't injected — the process was started before the launcher was installed. Restart the service and recheck.


Step 2: Confirm the Agent is Receiving Traces

Claude runs

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "sudo datadog-agent status 2>&1 | grep -A 15 'APM Agent'"

Healthy output shows:

  • feature_auto_instrumentation_enabled: true
  • Receiver (previous minute) with > 0 traces

ERROR: feature_auto_instrumentation_enabled: false — SSI not active on the agent. Check apm_config in /etc/datadog-agent/datadog.yaml.

ERROR: Receiver (previous minute): 0 — agent running but no traces yet. Generate traffic first (see Step 3), then recheck.


Step 3: Confirm the Service is Visible in Datadog

Claude runs

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

If <SERVICE_NAME> appears with isTraced: true — traces are reaching the Datadog backend.

Flask / ddtrace v3 naming note: With ddtrace >=3.x, Flask spans are emitted as service:flask rather than service:<DD_SERVICE>. The DD_SERVICE value appears as base_service on the spans. If you set DD_SERVICE=my-app, search for service:flask in the APM UI — the service list will show flask, not my-app. Check the base_service tag to confirm it matches your DD_SERVICE.

ERROR: Service missing — generate traffic to trigger trace creation:

Claude runs

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "sudo ss -tlnp 2>/dev/null | grep <PID> || sudo netstat -tlnp 2>/dev/null | grep <PID>"

Use the port from above:

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "for i in \$(seq 1 10); do curl -s -o /dev/null http://localhost:<PORT>/; done"

Wait 30 seconds, then retry:

bash
DD_SITE=<DD_SITE> pup apm services list --env <ENV> --from 10m
DD_SITE=<DD_SITE> pup traces search --query "service:<SERVICE_NAME>" --from 10m --limit 5

ERROR: Still missing — check for injection errors and go to troubleshoot-ssi:

bash
pup apm troubleshooting list --hostname <DD_HOSTNAME> --timeframe 1h

Done

Exit when ALL of the following are true:

  • Step 1: launcher + language library both visible in /proc/<PID>/maps
  • Step 2: agent APM receiver shows > 0 traces/min
  • Step 3: service appears in pup apm services list

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
  • Always confirm before restarting production services

Frequently asked questions

What does the Verify Ssi AI skill do?

Verify Single Step Instrumentation (SSI) is working end-to-end on Linux hosts — 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/linux-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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