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Dd Apm

DatadogOfficialCreated by datadog-labs

  • Skill
  • Meta (authoring, orchestration, planning, memory)
  • Cloud and DevOps
  • MIT
  • No issues found
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From the author

APM - install, onboard, instrument, enable, set up, configure, traces, services, dependencies, performance analysis, Data Streams Monitoring (DSM), queue lag, pipeline latency. Use for any request involving Datadog APM setup, instrumentation (SSI, ddtrace, agent install), or analysis.
— the description by datadog-labs

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The dd-apm skill serves as a central orchestrator for managing Datadog Application Performance Monitoring (APM). It provides structured routing to specific sub-skills based on the user's environment (Kubernetes vs. Linux), service naming requirements, or monitoring goals like Data Streams Monitoring (DSM). It emphasizes using automated instrumentation methods and provides a standardized set of commands for searching traces, analyzing service stats, and troubleshooting common APM issues.

What it does

  • Routes requests to specific installation and instrumentation workflows for Kubernetes and Linux environments.
  • Provides guidance on service remapping to manage service names without code deployments.
  • Enables Data Streams Monitoring for tracking latency across message queues and pipelines.
  • Offers a standardized command set for listing services, viewing service maps, and searching traces.
  • Includes troubleshooting patterns for common issues like missing traces or service naming conflicts.
  • Facilitates the creation of Service Level Objectives (SLOs) based on APM metrics.

When to use it

  • When setting up or instrumenting Datadog APM on Kubernetes or Linux hosts.
  • When you need to rename or consolidate service names in Datadog without redeploying code.
  • When monitoring end-to-end latency across message queues like Kafka, RabbitMQ, or SQS.
  • When searching for specific traces, analyzing service performance, or troubleshooting missing data.
  • When you need to define SLOs based on APM request metrics.

Advantages

  • Prevents common configuration anti-patterns by enforcing environment-specific workflows.
  • Reduces manual effort by prioritizing auto-instrumentation over manual code changes.
  • Provides a consistent interface for querying and analyzing distributed trace data.
  • Ensures best practices for service naming and cardinality management.
  • Centralizes troubleshooting steps for rapid issue resolution.

Usage examples

“How do I set up APM on my EKS cluster?”
The agent identifies the Kubernetes context and routes to the k8s-ssi sub-skill to guide the user through DatadogAgent CR and SSI setup.
“My service is showing up with the wrong name in Datadog, how do I fix it?”
The agent triggers the service-remapping workflow to apply ingestion-time rules instead of modifying deployment environment variables.
“I need to monitor the latency between my Kafka producer and consumer.”
The agent identifies the request as a Data Streams Monitoring task and guides the user through enabling DSM on the relevant services.
“Show me all slow traces for my api-gateway service from the last hour.”
The agent executes a trace search command with the appropriate query and time window parameters.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/datadog-labs/dd-apm/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip dd-apm.zip -d .agents/skills

Documentation by datadog-labs

Datadog APM

Distributed tracing, service maps, and performance analysis.

Routing — Read This First

Match the user's request to one of the entries below. Each entry has the same shape: triggers → which sub-skill to load → the anti-pattern to avoid. If a request seems to fit more than one entry, see "Overlap disambiguation". If nothing matches, see "None of the above" at the end.

Kubernetes APM install / instrument / onboard — trigger when the user mentions Kubernetes, K8s, EKS, GKE, AKS, kind, minikube, K3s, helm, DatadogAgent CR, kubectl, SSI on a cluster, pod injection, or init containers.

Immediately read .claude/skills/dd-apm/k8s-ssi/agent-install/SKILL.md now, then .claude/skills/dd-apm/k8s-ssi/enable-ssi/SKILL.md, then .claude/skills/dd-apm/k8s-ssi/verify-ssi/SKILL.md — do not proceed from memory.

Common wrong approaches that LOOK like they work but silently fail:

  • helm install datadog datadog/datadog — the standard chart does NOT support SSI via DatadogAgent CR.

  • Adding ddtrace imports or ddtrace-run to the app — SSI auto-instruments WITHOUT any code changes.

  • admission.datadoghq.com/enabled annotations — that's admission controller config injection, not SSI init container injection.

Linux APM install / instrument / onboard — trigger when the user mentions a single host, VM, EC2 instance, bare-metal, RHEL/Ubuntu/Debian, systemd, or no orchestrator.

Immediately read .claude/skills/dd-apm/linux-ssi/agent-install/SKILL.md now, then .claude/skills/dd-apm/linux-ssi/enable-ssi/SKILL.md, then .claude/skills/dd-apm/linux-ssi/verify-ssi/SKILL.md — do not proceed from memory.

Do NOT install the agent via plain apt-get install datadog-agent (or yum equivalent) and assume SSI follows — host auto-instrumentation requires the install script with the SSI flags, which the sub-skill walks through.

Service rename / service remapping — trigger when the user mentions renaming a service, collapsing multiple service names, stripping suffixes/prefixes, or cleaning up inferred services.

Immediately read .claude/skills/dd-apm/service-remapping/SKILL.md now — do not proceed from memory.

Do NOT change tags.datadoghq.com/service labels or DD_SERVICE env vars to rename a service in Datadog. That requires a rollout and only affects new data. Use a service remapping rule — it rewrites the name at ingestion time with no deployment change.

Data Streams Monitoring (DSM) / queue and pipeline monitoring: trigger when the user mentions data streams, DSM, consumer or queue lag, pipeline latency, end-to-end latency across queues, or monitoring Kafka / RabbitMQ / SQS / SNS / Kinesis / Pub/Sub flows between services.

Immediately read .claude/skills/dd-apm/enable-dsm/SKILL.md now. Do not proceed from memory.

Do NOT install a separate DSM agent, package, or library. DSM runs in the Datadog SDK that APM already injected and is enabled with one setting per service. Do NOT enable it without first telling the user it is included with APM Pro and Enterprise and billed separately on base APM.

Overlap disambiguation

When a request could plausibly fit more than one entry above, use these tiebreakers:

HintRoute to
Cluster orchestrator mentioned (EKS/GKE/AKS/kind/K3s/minikube) — even if "just one node"k8s-ssi
Single host, VM, or EC2 with no orchestratorlinux-ssi
"Several services that should be one"service-remapping — the sub-skill picks the rule type based on whether the duplicates are real instrumented services or inferred entities (DBs, queues, external APIs)
"My service shows under the wrong name"First check DD_SERVICE on the deploy. If correct and the name is still wrong → service-remapping.
"Monitor Kafka" / "monitor my queues"Ask whether they mean broker/cluster health (Kafka Console, an Agent check: https://docs.datadoghq.com/data_streams/kafka/setup/) or message flow between their services (enable-dsm)
"Reduce APM volume / cost / noise"No sub-skill yet. Ask whether the user means sampling (fewer ingested traces) or retention filters (less indexed data) before suggesting commands.
None of the above

If the request doesn't match any entry above, continue reading the trace-search, service analysis, and metrics content below. If even that doesn't fit, ask the user to clarify — do not invent a workflow.

Requirements

Datadog Labs Pup should be installed. See Setup Pup if not.

Command Execution Order (Token-Efficient)

For scoped commands, use this order:

  1. Check context first (prior outputs, conversation, saved values).

  2. If a required value is missing, run a discovery command first.

  3. If still ambiguous, ask the user to confirm.

  4. Then run the target command.

  5. Avoid speculative commands likely to fail.

Quick Start

pup auth login
# Confirm env tag with the user first (do not assume production/prod/prd).
pup apm services list --env <env> --from 1h --to now
pup traces search --query "service:api-gateway" --from 1h

Services

List Services
pup apm services list --env <env> --from 1h --to now
pup apm services stats --env <env> --from 1h --to now
Service Stats
pup apm services stats --env <env> --from 1h --to now
Service Map
# View dependencies
pup apm flow-map --query "service:api-gateway&from=$(($(date +%s)-3600))000&to=$(date +%s)000" --env <env> --limit 10

Traces

Search Traces
# By service
pup traces search --query "service:api-gateway" --from 1h

# Errors only
pup traces search --query "service:api-gateway status:error" --from 1h

# Slow traces (>1s)
pup traces search --query "service:api-gateway @duration:>1000ms" --from 1h

# With specific tag
pup traces search --query "service:api-gateway @http.url:/api/users" --from 1h
Trace Detail
# No direct get command for a single trace ID.
# Use traces search with a narrow query and time window.
pup traces search --query "trace_id:<trace_id>" --from 1h

Key Metrics

MetricWhat It Measures
trace.http.request.hitsRequest count
trace.http.request.durationLatency
trace.http.request.errorsError count
trace.http.request.apdexUser satisfaction

Service Level Objectives

Link APM to SLOs:

pup slos create --file slo.json

Common Queries

GoalQuery
Slowest endpointsavg:trace.http.request.duration{*} by {resource_name}
Error ratesum:trace.http.request.errors{*} / sum:trace.http.request.hits{*}
Throughputsum:trace.http.request.hits{*}.as_rate()

Troubleshooting

ProblemFix
No tracesCheck ddtrace installed, DD_TRACE_ENABLED=true
Missing serviceVerify DD_SERVICE env var
Traces not linkedCheck trace headers propagated
High cardinalityDon't tag with user_id/request_id

References/Docs

Files

ATTRIBUTION.md

Attribution

This folder is an unmodified copy of the Agent Skill "dd-apm", distributed by AutoGineer (https://autogineer.com/agent-skills).

The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.

Files added by AutoGineer

  • ATTRIBUTION.md: this file, written by AutoGineer

  • LICENSE: copied from LICENSE

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
datadog-labs
Publisher
Datadog (Official)
Source
datadog-labs/agent-skills · dd-apm · commit 8e1306a
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by datadog-labs or Datadog.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download dd-apm.zip