Datadog App

Organization

datadog-labs

datadog-app

Guides developers building Datadog Apps with TypeScript, React, the @datadog/apps scaffolder, and @datadog/vite-plugin. Use when a user wants to scaffold, run, debug, upgrade, build, upload, publish, upload without publishing (draft upload), add an upload-no-publish script, set up CI/CD, use OAuth or API/application key auth, trigger/poll Workflow Automation, choose DDSQL or Action Catalog for backend data access, or query app datastores with DDSQL, including backend function troubleshooting.

Overview

Publisher	datadog-labs
Repository	`agent-skills`
Skill name	`datadog-app`
Stars	172
Forks	28
Bundled files	9
License	MIT
Links	Skill source Repository

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 Datadog App AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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-apps/datadog-app .claude/skills/datadog-app

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Datadog App 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 App 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 App 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 Apps

Use this skill when a developer is building a Datadog Apps project with TypeScript, React, published packages, and the normal production Datadog site. If the user is modifying Datadog platform packages or testing package source changes, use a platform-engineer-oriented workflow instead.

Overview

Datadog Apps are locally developed web apps built with React and TypeScript or JavaScript. Use Apps when a project needs source control, code review, CI/CD, multi-engineer collaboration, AI-assisted local development, custom UI or logic, or backend code that integrates with services beyond low-code App Builder. Apps share App Builder's permissions model and can be embedded in Datadog surfaces such as dashboards and the Internal Developer Portal.

Reference Routing

Read only the reference needed for the user's task:

User task	Read
Create, scaffold, configure prerequisites, or run locally	`references/getting-started.md`
Build, upload, publish, upload without publishing, add upload-no-publish script, configure Datadog site, or understand upload output	`references/build-upload-publish.md`
Add or update GitHub Actions deployment	`references/cicd.md`
Trigger or poll Workflow Automation from a backend function	`references/workflow-http-trigger.md`
Get started querying data from a Datadog App	`references/querying-data/getting-started.md`
Understand or configure Action Catalog connections	`references/querying-data/connections.md`
Query Datadog App datastores with DDSQL	`references/querying-data/ddsql/datastores.md`
Upgrade Datadog Apps dependencies or compare with a freshly scaffolded app	`references/upgrading.md`
Diagnose OAuth, API/application key auth, upload, Node, site, or backend function failures	`references/troubleshooting.md`

Boundaries

After scaffolding or when working inside an existing app, read the app project's AGENTS.md before making changes.
For backend function implementation details, rely on the generated app project's AGENTS.md; this skill only covers local development auth choices and troubleshooting.
Preserve the app project's existing package manager, scripts, Datadog site, and repository conventions.
Do not cover Datadog package/platform development in this skill.
Low-code App Builder to Datadog Apps migration guidance is future work. Do not invent a migration process yet.

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 Datadog App AI skill do?

Why use Datadog App on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/datadog-labs/agent-skills/tree/main/dd-apps/datadog-app. 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 Datadog App?

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 App?

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

Is the Datadog App 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.

View all

Receiving Code Review

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CommunityPopular

Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative…

Requesting Code Review

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CommunityPopular

Use when completing tasks, implementing major features, or before merging to verify work meets requirements

Systematic Debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Test Driven Development

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CommunityPopular

Use when implementing any feature or bugfix, before writing implementation code

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Datadog Apps

Overview

Reference Routing

Boundaries

Bundled files

Frequently asked questions

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