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Datadog Apm

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automateyournetwork
datadog-apm

Analyze distributed traces and service performance in Datadog APM.

Overview

Publisherautomateyournetwork
Repositorynetclaw
Skill namedatadog-apm
Stars
680
Forks
189
Bundled files
Instructions only
LicenseApache-2.0
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 automateyournetwork on GitHub. Read the source before you install it.

Installation

Install the Datadog Apm 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.
    https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/datadog-apm
  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/automateyournetwork/netclaw.git /tmp/netclaw
mkdir -p .claude/skills
cp -r /tmp/netclaw/workspace/skills/datadog-apm .claude/skills/datadog-apm
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Datadog Apm 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 Datadog Apm 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 Datadog Apm 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.

Datadog APM Skill

Analyze distributed traces and service performance in Datadog APM.

Tools

ToolDescription
search_tracesSearch traces with query syntax and filters
get_trace_detailsGet detailed span information for a trace
list_servicesList all instrumented services
get_service_summaryGet service health and performance summary

Example Queries

Search for slow traces in the network service
→ search_traces(query="service:network-api @duration:>1s", time_range="1h")

Find error traces
→ search_traces(query="status:error", time_range="30m")

Get details of a specific trace
→ get_trace_details(trace_id="abc123def456")

List all services
→ list_services()

Get service performance summary
→ get_service_summary(service_name="network-api")

Workflows

Latency Analysis

  1. Search slow traces: search_traces(query="@duration:>500ms")
  2. Identify common patterns (resource, operation)
  3. Drill into trace waterfall: get_trace_details(trace_id="...")
  4. Find bottleneck spans
  5. Compare with baseline performance

Error Investigation

  1. Search error traces: search_traces(query="status:error service:network")
  2. Group by error type and root cause
  3. Review span-level errors in trace details
  4. Correlate with recent deployments or changes

Service Health Review

  1. List all services: list_services()
  2. Get summary for critical services: get_service_summary(service_name="...")
  3. Check error rate, latency percentiles, throughput
  4. Identify degraded or unhealthy services
  5. Review service dependencies map

Root Cause Analysis

  1. Start from incident or alert
  2. Find related traces: search_traces(query="trace_id:abc123")
  3. Walk span tree to identify failure point
  4. Check upstream/downstream service health
  5. Correlate with infrastructure metrics

Prerequisites

  • DD_API_KEY Datadog API key
  • DD_APP_KEY Datadog application key
  • DD_SITE Datadog site (optional, defaults to datadoghq.com)

Server

This skill uses the datadog-mcp server via remote MCP transport.

Notes

  • APM requires instrumented services sending traces to Datadog
  • Trace retention depends on your Datadog plan
  • Large trace searches may be paginated

Failure Behavior

  • On a tool error (timeout, unreachable host, malformed response), report the failure and its error message directly to the user rather than fabricating or guessing at results.
  • For a confirmed read-only call, check connectivity and retry once if appropriate. For any call that changes state or sends a message, a timeout does not prove the action failed: inspect current state or delivery status before retrying, preserve the required approval/change gates, and do not repeat an action whose outcome is unknown.

Frequently asked questions

What does the Datadog Apm AI skill do?

Analyze distributed traces and service performance in Datadog APM.

Why use Datadog Apm on TypingMind?

Because you install it once and use it with any model. Datadog Apm 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 Datadog Apm in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/datadog-apm. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Datadog Apm?

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 Datadog Apm?

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

Is the Datadog Apm AI skill free?

Yes. It is published on GitHub by automateyournetwork under the Apache-2.0 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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