Troubleshoot Ssi logo

Troubleshoot Ssi

Organization
datadog-labs
troubleshoot-ssi

Diagnose and fix Single Step Instrumentation (SSI) issues on Linux hosts — SSI automatically instruments applications for APM without code changes. Only use if the agent and SSI are configured but traces are missing or instrumentation is not working.

Overview

Publisherdatadog-labs
Repositoryagent-skills
Skill nametroubleshoot-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 Troubleshoot 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/troubleshoot-ssi .claude/skills/troubleshoot-ssi
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Troubleshoot 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 Troubleshoot 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 Troubleshoot 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.

Troubleshoot APM SSI on Linux

Triggers

Invoke this skill when the user expresses intent to:

  • Debug why a Linux process is not being instrumented
  • Investigate why traces are not appearing in Datadog from a Linux host
  • Diagnose SSI injection failures on Linux
  • Follow up on failed checks from verify-ssi
  • Report that a specific service or host has no traces

Do NOT invoke this skill if:

  • SSI has not been enabled yet — run enable-ssi first

Critical: pup First, SSH Second

You do NOT need SSH access to start troubleshooting. The pup CLI queries Datadog's backend directly. Start with pup commands immediately using information the user already gave you (hostname, service name, env). Only go to SSH if pup doesn't reveal the cause.

pup-cli: check, install, and authenticate

Claude runs

bash
pup --version

If not found, install it (OS-aware):

Claude runs

bash
if [[ "$(uname)" == "Darwin" ]]; then
  brew tap datadog-labs/pack && brew install datadog-labs/pack/pup
else
  PUP_VERSION=$(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

Auth — check in this order:

  1. Check OAuth status:
bash
pup auth status --site <DD_SITE>

If authenticated — proceed directly to Step 1.

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.

  1. If OAuth login is not possible (e.g., no browser access), fall back to API keys:
bash
echo "DD_API_KEY set: $([ -n "${DD_API_KEY:-}" ] && echo yes || echo no)"
echo "DD_APP_KEY set: $([ -n "${DD_APP_KEY:-}" ] && echo yes || echo no)"

If DD_API_KEY and DD_APP_KEY are both set — proceed to Step 1. pup will use them automatically even if pup auth status shows unauthenticated.


Context

Use what the user already provided. Do not ask for missing context upfront — resolve variables lazily, only when a specific step needs them.

VariableHow to resolveWhen needed
DD_HOSTNAMEFrom the user's message, or datadog-agent status via SSHStep 1 — start here
SERVICE_NAMEFrom the user's messageStep 1 — start here
ENVAsk the user only when a command requires itStep 1 (service-library-config get), Step 3
DD_SITEAsk the user, or grep "^site:" /etc/datadog-agent/datadog.yaml via SSHOnly if pup auth check fails
SSH_KEYFrom user or /workspace/.ssh/id_ed25519Step 4 (SSH investigation) only
SSH_USERFrom user or default rootStep 4 (SSH investigation) only
SSH_HOSTFrom user's messageStep 4 (SSH investigation) only

If the user has already provided DD_HOSTNAME and SERVICE_NAME, go directly to Step 1. Do not ask for ENV or SSH details first.


How SSI Works on Linux — Domain Knowledge

Read this before investigating. It gives you the mental model to reason about novel failures.

Injection chain:

  1. Install script (with DD_APM_INSTRUMENTATION_ENABLED=host) installs datadog-apm-inject and language library packages under /opt/datadog-packages/
  2. The inject package writes its launcher path into /etc/ld.so.preload
  3. The Linux dynamic linker pre-loads the launcher into every new process at startup
  4. The launcher detects the process language and loads the appropriate tracer .so from /opt/datadog-packages/datadog-apm-library-<lang>/
  5. The tracer sends spans to the Agent at localhost:8126
  6. The Agent forwards traces to Datadog at intake.<DD_SITE>

Diagnostic layers:

  • pup — sees what Datadog's backend received + injection errors reported by the launcher. Start here.
  • /proc/<pid>/maps — sees the actual shared libraries loaded into a running process. The authoritative check for whether injection succeeded.
  • datadog-agent status — sees whether the local Agent is receiving traces.

Known silent failures:

  • musl libc (Alpine) — launcher is glibc-compiled; musl is ABI-incompatible. Linker loads it but injection silently aborts
  • Existing ddtrace/OTel — launcher detects user-installed tracer and silently disables itself (already_instrumented result class)
  • Unsupported runtime version — silently skipped
  • Process started before SSI was enabled/etc/ld.so.preload only affects new processes
  • Static binary / Go — Go programs link statically and ignore LD_PRELOAD entirely
  • SELinux/AppArmor — can block /etc/ld.so.preload reads for confined processes
  • Package directory empty/corruptdatadog-installer status reflects DB registration, not actual files. A package can show as installed while its directory is empty. Always verify files exist under /opt/datadog-packages/<package>/

Service name identity — important: With SSI, DD_SERVICE is often not set in the process environment. The tracer auto-detects a service name. The telemetry-reported name (what pup fleet tracers list and service-library-config get show) may not match what you expect in the APM UI:

  • JVM: telemetry reports jar artifact name with version (e.g. inventory-service-1.0.0), spans use the base name (inventory-service)
  • Python: telemetry may report fastapi or django rather than the app name
  • Node.js: names typically match

If service-library-config get returns empty, use pup traces search --query "host:<DD_HOSTNAME>" --from 1h --limit 5 to discover what service names have been sending traces, then retry.


Step 1: Triage with pup (no SSH required)

Run these first. The answers determine everything that follows.

Claude runs

bash
# Check for injection errors (failures only — successful injections don't appear here)
pup apm troubleshooting list --hostname <DD_HOSTNAME>

# Check full tracer config — look at apm_enabled, trace_agent_url, site
pup apm service-library-config get --service-name <SERVICE_NAME> --env <ENV>

# Check what services have sent traces (reveals actual service names visible to backend)
pup apm services list --from 1h

# Check if traces exist at all
pup traces search --query "service:<SERVICE_NAME>" --from 15m --limit 5

# Fastest trace confirmation — metrics appear before indexed traces
pup metrics query --query "sum:trace.*.request.hits{host:<DD_HOSTNAME>,service:<SERVICE_NAME>}.as_count()" --from 15m

ENV is required for service-library-config get. If the user didn't provide it, state your assumed value (e.g. prod) and run it anyway — don't stop to ask.

Key values to check in service-library-config get output:

  • apm_enabled — must be true. If false, the tracer won't send traces regardless of injection.
  • trace_agent_url — must point to http://localhost:8126 or the correct agent socket. Wrong value = tracer can't reach the Agent.
  • site — must match your Datadog org's site.

Presenting your findings (required)

Your final response is the deliverable — not your investigation transcript. It must include every diagnostic from this skill that you ran or that applies, each with its purpose and what you found. Avoid these failure modes:

  • Omitting a required diagnostic. Your response must explicitly include, by name:

    • pup apm troubleshooting list --hostname <DD_HOSTNAME> — injection errors
    • pup apm service-library-config get --service-name <SERVICE_NAME> --env <ENV> — runtime SDK config
    • verification that apm_enabled is true and that trace_agent_url points to the correct agent endpoint (e.g. http://localhost:8126)
    • datadog-agent status for the APM receiver, and a /proc/<pid>/maps (or equivalent) check that the tracer .so is loaded into the process

    Run them if you have access; recommend them for the user to run if you don't.

  • Substituting a proxy check. Confirming port 8126 is listening shows the Agent's receiver is up — it is not a substitute for verifying the tracer-side apm_enabled and trace_agent_url config keys. They answer different questions; report both.

  • Concluding before the checks. Outline and run the diagnostics before settling on a root cause, then give specific per-finding remediation and note that the service must be restarted after any config or package fix.


Step 2: State Your Hypotheses

Before investigating, explicitly state your ranked hypotheses based on triage output. Do not skip this step.

Triage signalStrong hypothesis
pup troubleshooting list shows result: error, result_class: incorrect_installationPackage directory empty or corrupt — verify files exist under /opt/datadog-packages/datadog-apm-library-<lang>/, then use remediation flow
pup troubleshooting list shows result: error, import/load errorTracer library couldn't be loaded — check runtime version, libc compatibility
pup troubleshooting list shows result: abort, reason already_instrumentedManual ddtrace/OTel already in the app — launcher silently disabled itself
pup troubleshooting list shows result: abort, reason language not detectedExpected for non-app processes (e.g., bash, cron). Not a failure.
pup troubleshooting list emptyEither no injection attempts yet (process not restarted), or injection succeeded silently
service-library-config get shows apm_enabled: falseTracer is loaded but explicitly disabled — check source field to see who set it
service-library-config get shows trace_agent_url pointing to wrong host/portTracer can't reach the Agent — fix the URL
service-library-config get shows wrong siteTraces going to wrong Datadog org
No traces in pup traces search, no troubleshooting errorsProcess was never injected — check: process not restarted after SSI enabled, /etc/ld.so.preload missing, static binary
Unexpected service name in pup apm services list resultsService name mismatch — use the actual name from trace data for subsequent config lookups
Traces arriving in pupNot a real problem — likely a UI filter or time window. Tell the user and stop.

State your top 1-3 hypotheses explicitly: "Based on triage, I think the most likely cause is X because Y."


Step 3: Investigate with pup (deeper)

Use only the tools relevant to your hypotheses.

Check SDK config in detail:

bash
# Show all config values with their source (env_var, remote_config, code, default)
pup apm service-library-config get --service-name <SERVICE_NAME> --env <ENV>

# Show only configs where instances disagree (config drift)
pup apm service-library-config get --service-name <SERVICE_NAME> --mixed

Key values to check:

  • apm_enabled — if false, tracer won't send traces. Check source to see who disabled it (code > env_var > remote_config > default)
  • trace_agent_url — should be http://localhost:8126 or a Unix socket. Wrong value = tracer can't reach Agent
  • site — must match your Datadog org's site. Mismatch = traces going to wrong org
  • service — with SSI and no DD_SERVICE set, source: default is expected

If service-library-config get returns empty — the service name you're using may not match the actual name in trace data:

bash
pup traces search --query "host:<DD_HOSTNAME>" --from 1h --limit 5

Use the service field from trace results for subsequent config lookups.

Check injection error details:

bash
pup apm troubleshooting list --hostname <DD_HOSTNAME> --timeframe 4h

Step 4: Investigate via SSH (if pup didn't reveal the cause)

Before asking for SSH credentials, briefly explain what you need to check and why, so the user understands the diagnostic plan before handing over access.

Is /etc/ld.so.preload set?

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

If it contains a path ending in launcher.preload.so or libdatadog-apm-inject.so — launcher is armed for new processes. ERROR: Empty or missing — SSI was not fully set up. Re-run the install script with DD_APM_INSTRUMENTATION_ENABLED=host.

Is the tracer actually loaded into the running process?

This is the authoritative injection check — use /proc/<pid>/maps, not environ:

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "pgrep -a -f '<SERVICE_NAME>' | head -3"

Use the PID:

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "sudo cat /proc/<PID>/maps | grep -E 'launcher|apm-library|datadog'"
  • Launcher + language library present — injection succeeded for this process
  • Launcher only, no language library — launcher ran but couldn't inject the tracer (check pup troubleshooting list for the reason)
  • Nothing/etc/ld.so.preload not set, process started before SSI was enabled, or static binary

Was the process started before SSI was enabled?

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "ps -p <PID> -o pid,lstart,cmd; stat /etc/ld.so.preload"

If process started before /etc/ld.so.preload was written, restart the service. Always confirm with the user before restarting production services.

Is the base libc musl?

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "ldd --version 2>&1 | head -1 && cat /etc/os-release | grep PRETTY_NAME"

ERROR: musl — SSI's launcher requires glibc. No workaround; must migrate to Debian/Ubuntu/RHEL/Amazon Linux.

Is it a static binary?

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "file /proc/<PID>/exe; ldd /proc/<PID>/exe 2>&1"

ERROR: statically linked — SSI cannot instrument this binary. Manual instrumentation required.

Are the APM packages actually present on disk?

datadog-installer status reflects only DB registration — a package can show as installed while its directory is empty. Always verify:

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "ls /opt/datadog-packages/ && ls /opt/datadog-packages/datadog-apm-library-<LANG>/ | head -5"

ERROR: Directory empty or missing — package is registered but broken on disk. Use the remediation flow.

Does the app have existing manual instrumentation?

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> "
sudo cat /proc/<PID>/maps | grep -E 'ddtrace|opentelemetry|dd-trace'
"

Also check dependency manifests: requirements.txt, package.json, Gemfile, pom.xml. ERROR: Found — SSI silently disabled itself. Remove manual tracer, restart the service.

Is the Agent APM receiver listening and receiving traces?

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "sudo datadog-agent status 2>&1 | grep -A 15 'APM Agent'"
  • feature_auto_instrumentation_enabled: true — SSI is active on the agent
  • Receiver (previous minute) — trace count received by the agent
  • Endpoints — where traces are forwarded
bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "sudo ss -tlnp 2>/dev/null | grep 8126 || sudo netstat -tlnp 2>/dev/null | grep 8126"

ERROR: Port 8126 not listening — APM receiver disabled. Check apm_config.enabled in /etc/datadog-agent/datadog.yaml.

What service name did the tracer register?

With SSI, DD_SERVICE is often not set. Read the tracer's memfd to find the real service name:

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> "
sudo ls -la /proc/<PID>/fd/ | grep 'datadog-tracer-info'
"

Use the fd number:

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "sudo cat /proc/<PID>/fd/<FD_NUM> | python3 -c \"import sys,msgpack; d=msgpack.unpackb(sys.stdin.buffer.read()); print(d)\""

Returns service_name, service_env, tracer_version.

Is SELinux/AppArmor blocking /etc/ld.so.preload?

bash
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> "
getenforce 2>/dev/null
ausearch -m AVC -ts recent 2>/dev/null | grep 'ld.so.preload\|datadog' | tail -10
dmesg | grep -i 'apparmor.*denied.*datadog' | tail -5
"

If SELinux/AppArmor is denying access, work with the user's security team. Do not disable SELinux systemwide.


Step 5: Reflect Before Concluding

Before applying any fix, answer:

  1. What evidence confirms my hypothesis?
  2. What evidence would contradict it — and have I checked?
  3. Is there a simpler explanation I haven't considered?

If the conclusion doesn't hold up, return to Step 2 with new hypotheses.


Step 6: Fix

Remediation: Reinstalling a Broken APM Package

datadog-installer status reflects DB registration, not actual file presence. If pup troubleshooting list shows incorrect_installation but the installer says the package is installed, the registration is stale:

bash
# Remove the stale registration first
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "sudo datadog-installer remove datadog-apm-library-<LANG>"

# Re-run install — now it will actually download and extract
ssh -o StrictHostKeyChecking=no -i <SSH_KEY> <SSH_USER>@<SSH_HOST> \
  "DD_API_KEY=${DD_API_KEY} DD_SITE=${DD_SITE} DD_APM_INSTRUMENTATION_ENABLED=host bash -c \"\$(curl -L https://install.datadoghq.com/scripts/install_script_agent7.sh)\""

If re-running the install script is sufficient (package files are intact), use remove first only if the script reports success but the problem persists.

After any config change — restart the service (confirm with user first for production):

The user must restart the affected service for SSI to re-inject. Identify the service manager and present restart instructions — do not restart automatically unless the user explicitly asks.

Common restart commands:

bash
# systemd
sudo systemctl restart <SERVICE_NAME>
# supervisord
sudo supervisorctl restart <PROGRAM_NAME>
# pm2
pm2 reload <APP_NAME>

Step 7: Verify

Re-run the pup triage commands to confirm the fix worked:

Claude runs

bash
pup apm troubleshooting list --hostname <DD_HOSTNAME> --timeframe 15m
pup traces search --query "service:<SERVICE_NAME>" --from 15m --limit 5
pup metrics query --query "sum:trace.*.request.hits{host:<DD_HOSTNAME>,service:<SERVICE_NAME>}.as_count()" --from 15m

If there are no new injection errors and traces are arriving — resolved. Automatically proceed to onboarding-summary now — do not ask the user for permission.

ERROR: Still failing — return to Step 2 with updated hypotheses.


Security constraints

  • Never write a raw API key into any file or chat message
  • Never disable SELinux systemwide
  • Always confirm before restarting production services
  • datadog-installer remove requires explicit confirmation — confirm with user before running

Frequently asked questions

What does the Troubleshoot Ssi AI skill do?

Diagnose and fix Single Step Instrumentation (SSI) issues on Linux hosts — SSI automatically instruments applications for APM without code changes. Only use if the agent and SSI are configured but traces are missing or instrumentation is not working.

Why use Troubleshoot Ssi on TypingMind?

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

Which AI models can use Troubleshoot 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 Troubleshoot Ssi?

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

Is the Troubleshoot 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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