Dd Instrument Rum logo

Dd Instrument Rum

Organization
datadog-labs
dd-instrument-rum

Instrument browser-based web applications with Datadog Browser RUM. Detect the application framework, router, package manager, bundler, entrypoint, credentials, and existing RUM setup; add or safely complete classic Browser RUM instrumentation for React, Next.js App or Pages Router, Angular, Vue, Nuxt, Svelte, vanilla JavaScript, SPAs, and iframe-hosted apps; avoid duplicate initialization; and verify the application still builds. Use when asked to add, set up, instrument, repair, or verify Datadog RUM, Browser Monitoring, Session Replay, or framework-specific Browser RUM plugins.

Overview

Publisherdatadog-labs
Repositoryagent-skills
Skill namedd-instrument-rum
Stars
172
Forks
28
Bundled files
9
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.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by datadog-labs on GitHub. Read the source before you install it.

Installation

Install the Dd Instrument Rum 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-instrument-rum .claude/skills/dd-instrument-rum
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dd Instrument Rum 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 Dd Instrument Rum 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 Dd Instrument Rum 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 Browser RUM instrumentation

Instrument only the browser application in scope. Do not add Datadog APM, tracing, LLM Observability, Logs, source-map upload, user identification, Vercel metadata, Shopify, Salesforce, or unrelated Datadog products.

Use normal file inspection, editing, and command tools for every step except optional RUM application creation. Use only CreateRumApplication for that operation, as described in references/common-credentials.md; never invent Datadog tool names.

Ground rules

  • Inspect before editing. Base every framework, version, entrypoint, command, and configuration decision on project files.
  • Copy package names, import paths, exported symbols, and init option keys exactly from the applicable references. Do not substitute package aliases or recreate SDK APIs from memory.
  • Post a short checklist before making changes and update it as work completes.
  • Initialize RUM exactly once and as early as safely possible in the browser lifecycle. Never put init() in a component render, lifecycle hook, route handler, or repeated callback.
  • Preserve an existing valid init configuration. Add missing compatible framework plugin wiring to that init in place; never create a competing init.
  • Existing-init credential guardrail: when one valid RUM init already exists, treat its applicationId, clientToken, site, service tags, sampling, privacy, tracking, and every other existing option as immutable. Credentials supplied by the task are for a new init only; they are not an override for an existing valid init. In this branch, do not rewrite credential lines while adding plugin wiring.
  • Stop without editing when required credentials cannot be resolved, site is invalid, framework/plugin prerequisites are unmet, permissions prevent the work, or multiple conflicting init calls cannot be safely consolidated.
  • Install only packages required for Browser RUM. Keep @datadog/browser-rum and every @datadog/browser-rum-* integration package on the same exact SDK version.
  • Persist dependencies in the manifest and lockfile used by the real build. Do not rely on packages that happen to exist in node_modules.
  • Preserve application behavior, existing custom error handling, formatting conventions, and unrelated code.
  • Apply edits with the available file-editing tool. If an expected text match fails, re-read the file and adapt to its current contents instead of retrying the same edit.
  • Do not write project paths, framework details, credentials, client tokens, or RUM application IDs to persistent memory.
  • Run a terminating build before reporting success. Never claim telemetry was received unless it was actually observed.

Phase 1: analyze the target

Locate the browser application

Identify the project root that produces browser code. In a monorepo, inspect workspace configuration, scripts, Dockerfiles, and CI/deployment configuration to find the frontend manifest used by the real build rather than editing the repository root by assumption. Record that manifest path and its package manager before editing. If several independent browser applications are plausible and the user did not select one, ask which application to instrument.

Reject non-browser applications such as Ink CLIs and projects with no HTML/browser build target.

Detect the framework and runtime shape

Inspect dependencies and source layout in this order so meta-frameworks win over their underlying UI library:

  1. nuxt -> Nuxt
  2. next -> Next.js; distinguish App Router from Pages Router by source layout
  3. @angular/core -> Angular; distinguish standalone bootstrap from NgModule
  4. vue -> Vue
  5. svelte or @sveltejs/kit -> Svelte or SvelteKit
  6. react -> React
  7. Browser entrypoint without the above -> generic/vanilla browser application

Also detect:

  • Exact framework, React Router, TanStack Router, Vue Router, and Node versions.
  • TypeScript when a tsconfig.json or TypeScript dependency is present; otherwise preserve JavaScript.
  • Package manager from the deployed manifest's packageManager field first, then its adjacent lockfile: bun.lock or bun.lockb, pnpm-lock.yaml, yarn.lock, or package-lock.json. Use npm only when no stronger project signal exists, and never mix managers.
  • Bundler from configuration and dependencies: Vite, Webpack, Rollup, esbuild, Rspack, or Create React App (react-scripts). Treat unmanaged HTML as CDN only when no package/bundler build owns it.
  • Browser entrypoint from build configuration, HTML script targets, framework conventions, and the import graph rather than filename alone.
  • The normal terminating build/typecheck command.
  • Existing public environment-variable conventions.

Read references/rum-core.md, then read exactly the applicable framework reference:

TargetReference
React, React Router, TanStack Routerreferences/rum-react.md
Next.js App or Pages Routerreferences/rum-nextjs.md
Angularreferences/rum-angular.md
Vuereferences/rum-vue.md
Nuxtreferences/rum-nuxt.md
Svelte, vanilla, generic SPA, iframe-hosted appreferences/rum-other-frameworks.md

Validate every prerequisite in the selected reference before provisioning credentials or editing. Do not upgrade a framework and do not silently fall back to core-only RUM when a detected framework plugin is incompatible.

Detect existing RUM instrumentation

Search source, HTML, and configuration files while excluding dependency, build, generated, and coverage directories such as node_modules, dist, build, .next, .nuxt, and coverage.

Check for:

  • Imports or requires from @datadog/browser-rum, including renamed and namespace bindings.
  • Imports from any @datadog/browser-rum-* integration package.
  • Calls bound to the imported datadogRum.init, including aliases and local wrapper modules.
  • DD_RUM.init, window.DD_RUM.init, DD_RUM.onReady, and Datadog CDN loader URLs.
  • Existing plugin registrations, router wrappers/providers/components, and framework error hooks.

Treat package presence alone as insufficient: a dependency may be unused.

  • No init: add one using the selected reference.
  • One valid init: keep its location and all existing values. Do not resolve or apply replacement credentials. Add only missing compatible plugin/router/error wiring to it.
  • One init with missing or invalid required credentials/site: stop and report the invalid configuration; do not layer another init over it.
  • Multiple init calls or mixed npm/CDN setups: stop and report every location unless they can be proven to be one mutually exclusive setup. Do not guess which setup should win.
  • Complete setup: make no changes; still run the applicable verification.

Phase 2: instrument

Resolve credentials through references/common-credentials.md only after analysis succeeds. If analysis found one valid existing init, reuse its existing credentials and skip credential replacement entirely. Supplied task credentials may be used only when creating a new init. Then:

  1. Install the core SDK and compatible framework package(s) with the detected package manager, using the deployed frontend manifest. For an existing valid init, install only a missing integration package; never replace already-valid RUM packages or configuration.
  2. Apply the canonical configuration and install method from references/rum-core.md. For an existing valid init, apply only missing compatible fields to that same object; do not copy credential or canonical-option placeholders over existing values.
  3. Apply the selected framework reference, including router tracking and safe framework error integration when those surfaces exist.
  4. Update the lockfile with the same package manager. Never hand-edit a generated lockfile.
  5. Detect configured formatting from manifest scripts such as lint:fix, fix, or format and from ESLint/Prettier configuration. Run the project's formatter or fix command only for files changed by this work.

Do not add allowedTracingUrls or any backend/CORS/tracing configuration; those belong to an APM onboarding workflow.

Phase 3: verify and report

Follow references/common-verify-report.md. Before reporting success, inspect the final diff against the pre-edit project. When an existing valid init was found, verify that every pre-existing init value—including both credentials—remains byte-for-byte unchanged and that the only setup changes are the required missing integration wiring (plus any strictly necessary dependency/lockfile change). If an existing value changed, restore it before reporting success. Do not report success unless required packages are persisted, the resulting setup contains exactly one init, and the normal build succeeds.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dd Instrument Rum AI skill do?

Instrument browser-based web applications with Datadog Browser RUM. Detect the application framework, router, package manager, bundler, entrypoint, credentials, and existing RUM setup; add or safely complete classic Browser RUM instrumentation for React, Next.js App or Pages Router, Angular, Vue, Nuxt, Svelte, vanilla JavaScript, SPAs, and iframe-hosted apps; avoid duplicate initialization; and verify the application still builds. Use when asked to add, set up, instrument, repair, or verify Datadog RUM, Browser Monitoring, Session Replay, or framework-specific Browser RUM plugins.

Why use Dd Instrument Rum on TypingMind?

Because you install it once and use it with any model. Dd Instrument Rum 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 Dd Instrument Rum in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/datadog-labs/agent-skills/tree/main/dd-instrument-rum. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dd Instrument Rum?

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 Dd Instrument Rum?

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

Is the Dd Instrument Rum 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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