Flags

OrganizationPopular

vercel

flags

How to add or modify Next.js experimental feature flags end-to-end. Use when editing config-shared.ts, config-schema.ts, define-env-plugin.ts, next-server.ts, export/worker.ts, or module.compiled.js. Covers type declaration, zod schema, build-time injection, runtime env plumbing, and the decision between runtime env-var branching vs separate bundle variants.

Overview

Publisher	vercel
Repository	`next.js`
Skill name	`flags`
Stars	143.1K
Forks	34.7K
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 vercel on GitHub. Read the source before you install it.

Installation

Install the Flags 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.
https://github.com/vercel/next.js/tree/canary/.agents/skills/flags
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/vercel/next.js.git /tmp/next.js
mkdir -p .claude/skills
cp -r /tmp/next.js/.agents/skills/flags .claude/skills/flags

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

Use it in TypingMind

Enable Flags 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 Flags 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 Flags 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.

Feature Flags

Use this skill when adding or changing framework feature flags in Next.js internals.

Required Wiring

All flags need: config-shared.ts (type) → config-schema.ts (zod). If the flag is consumed in user-bundled code (client components, edge routes, app-page.ts template), also add it to define-env.ts for build-time injection. Runtime-only flags consumed exclusively in pre-compiled bundles can skip define-env.ts.

Where the Flag Is Consumed

Client/bundled code only (e.g. __NEXT_PPR in client components): define-env.ts is sufficient. Webpack/Turbopack replaces process.env.X at the user's build time.

Pre-compiled runtime bundles (e.g. code in app-render.tsx): The flag must also be set as a real process.env var at runtime, because app-render.tsx runs from pre-compiled bundles where define-env.ts doesn't reach. Two approaches:

Runtime env var: Set in next-server.ts + export/worker.ts. Both code paths stay in one bundle. Simple but increases bundle size.
Separate bundle variant: Add DefinePlugin entry in next-runtime.webpack-config.js (scoped to bundleType === 'app'), new taskfile tasks, update module.compiled.js selector, and still set env var in next-server.ts + export/worker.ts for bundle selection. Eliminates dead code but adds build complexity.

For runtime flags, also add the field to the NextConfigRuntime Pick type in config-shared.ts.

Runtime-Bundle Model

Runtime bundles are built by next-runtime.webpack-config.js (rspack) via taskfile.js bundle tasks.
Bundle selection occurs at runtime in src/server/route-modules/app-page/module.compiled.js based on process.env vars.
Variants: {turbo/webpack} × {experimental/stable/nodestreams/experimental-nodestreams} × {dev/prod} = up to 16 bundles per route type.
define-env.ts affects user bundling, not pre-compiled runtime internals.
process.env.X checks in app-render.tsx are either replaced by DefinePlugin at runtime-bundle-build time, or read as actual env vars at server startup. They are NOT affected by the user's defines from define-env.ts.
Gotcha: DefinePlugin entries in next-runtime.webpack-config.js must be scoped to the correct bundleType (e.g. app only, not server) to avoid replacing assignment targets in next-server.ts.

Related Skills

$dce-edge - DCE-safe require patterns and edge constraints
$react-vendoring - entry-base boundaries and vendored React
$runtime-debug - reproduction and verification workflow

Frequently asked questions

What does the Flags AI skill do?

Why use Flags on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel/next.js/tree/canary/.agents/skills/flags. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Flags?

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 Flags?

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

Is the Flags AI skill free?

Yes. It is published on GitHub by vercel 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Feature Flags

Required Wiring

Where the Flag Is Consumed

Runtime-Bundle Model

Related Skills

Frequently asked questions

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