Rspack Split Chunks logo

Rspack Split Chunks

Organization
rstackjs
rspack-split-chunks

Diagnose or tune Rspack splitChunks for duplicate modules, route over-fetching, cache groups, caching, or oversized chunks.

Overview

Publisherrstackjs
Repositoryagent-skills
Skill namerspack-split-chunks
Stars
93
Forks
4
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by rstackjs on GitHub. Read the source before you install it.

Installation

Install the Rspack Split Chunks 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/rstackjs/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/rspack-split-chunks .claude/skills/rspack-split-chunks
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rspack Split Chunks 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 Rspack Split Chunks 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 Rspack Split Chunks 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.

Rspack SplitChunks optimization

Use this skill when the task is to recommend, review, or debug optimization.splitChunks. If you are using ESM library, it's not the same algorithm of this skill.

Default stance

  • Distinguish repo defaults from recommended production baselines.
  • Rspack's built-in default is chunks: "async", but for most production web apps the best starting point is:
js
optimization: {
  splitChunks: {
    chunks: "all",
  },
}
  • Keep the default cache groups unless there is a concrete reason to replace them.
  • Treat name as a graph-shaping option, not a cosmetic naming option.
  • Do not use splitChunks to reason about JavaScript execution order or tree shaking. For JS, chunk loading/execution order is preserved by the runtime dependency graph, and tree shaking is decided elsewhere.

Read references/repo-behavior.md when you need the source-backed rationale.

What to optimize for

First identify which problem the user actually has:

  • duplicated modules across entry or async boundaries
  • a route fetching a large shared chunk with mostly unused modules
  • too many tiny chunks
  • a vendor/common chunk that changes too often and hurts caching
  • an oversized async or initial chunk that should be subdivided
  • confusion about whether splitChunks affects runtime execution order

Do not optimize all of these at once. Pick the primary goal and keep the rest as constraints.

Workflow

1. Start from the safest production baseline

Unless the user already has a measured problem that requires custom grouping, prefer:

js
optimization: {
  splitChunks: {
    chunks: "all",
  },
}

Why:

  • it lets splitChunks dedupe modules across both initial and async chunks
  • it still only loads chunks reachable from the current entry/runtime
  • it usually avoids loading unnecessary modules better than hand-written global vendor buckets

If the existing config disables default or defaultVendors, assume that is suspicious until proven necessary.

2. Audit the config for high-risk knobs

Check these first:

  • fixed name
  • cacheGroups.*.name
  • enforce: true
  • disabled default / defaultVendors
  • broad test: /node_modules/ rules combined with a single global name
  • usedExports: false
  • very small minSize
  • maxSize combined with manual global names

3. Interpret name correctly

Use this rule:

  • No name: splitChunks can keep different chunk combinations separate.
  • Same name: matching modules are merged into the same named split chunk candidate.

That means a fixed name: "vendors" or name: "common" is often the real reason a page starts fetching modules from unrelated dependency chains.

Prefer these alternatives before adding name:

  • keep name unset
  • use idHint if the goal is filename identity, not grouping identity
  • narrow the test so the cache group is smaller
  • split one broad cache group into several focused cache groups
  • rely on maxSize to subdivide a big chunk instead of forcing a global name

Use a fixed name only when the user explicitly wants one shared asset across multiple entries/routes and accepts the extra coupling.

4. Preserve the built-in cache groups by default

Rspack's built-in production-oriented behavior depends heavily on these two groups:

  • default: extracts modules shared by at least 2 chunks and reuses existing chunks
  • defaultVendors: extracts node_modules modules and reuses existing chunks

These defaults are usually the best balance between dedupe and "only fetch what this page needs".

If you customize cacheGroups, do not casually replace these with one manually named vendor bucket.

5. Use chunks: "all" without fear of breaking execution order

When a module group is split out, Rspack connects the new chunk back to the original chunk groups. That preserves JavaScript loading semantics.

So:

  • splitChunks changes chunk topology
  • the runtime still guarantees dependency loading/execution order
  • if execution order appears broken, look for other causes first
  • this statement is about JavaScript, not CSS order

6. Use maxSize as a refinement tool

Use maxSize, maxAsyncSize, or maxInitialSize when the problem is "this shared chunk is too large", not when the problem is "I need a stable vendor chunk name".

Important behavior:

  • maxSize runs after a chunk already exists
  • the split is deterministic
  • modules are grouped by path-derived keys and split near low-similarity boundaries
  • similar file paths tend to stay together

This is usually safer than forcing one giant named vendor chunk, because it keeps chunk graph semantics while subdividing hot spots.

7. Use usedExports deliberately

If the user has multiple runtimes/entries and wants leaner shared chunks per runtime, prefer keeping usedExports enabled.

If they set usedExports: false, expect broader sharing and potentially larger common chunks.

This is still not tree shaking. It only changes how splitChunks groups modules across runtimes.

8. Treat enforce: true as an escape hatch

enforce: true bypasses several normal guardrails. Use it only when the user intentionally wants a split regardless of minSize, minChunks, and request limits.

If a config looks aggressive and hard to explain, check enforce before changing anything else.

Recommendations by goal

Better default production chunking

Recommend:

js
optimization: {
  splitChunks: {
    chunks: "all",
  },
}

Avoid:

  • disabling default
  • disabling defaultVendors
  • adding name before measuring a real problem

Avoid fetching non-essential modules

Recommend:

  • remove fixed name
  • keep cache groups narrow
  • keep chunks: "all" if dedupe across initial chunks is still desired
  • inspect which routes now depend on a shared chunk after each change

Avoid:

js
cacheGroups: {
  vendors: {
    test: /[\\/]node_modules[\\/]/,
    chunks: "all",
    name: "vendors",
    enforce: true
  }
}

That pattern often creates one over-shared chunk that many pages must fetch.

Improve caching without over-merging

Recommend:

  • keep name unset
  • use idHint
  • keep chunkIds: "deterministic" or other stable id strategies elsewhere in the config
  • split broad groups into smaller focused groups only when the package boundaries are stable and important

Use a fixed name only if the user explicitly prefers cache reuse over route isolation.

Split a large shared chunk

Recommend:

js
optimization: {
  splitChunks: {
    chunks: "all",
    maxSize: 200000,
  },
}

Then tune:

  • maxAsyncSize when async chunks are the pain point
  • maxInitialSize when first-load pressure matters more
  • hidePathInfo if generated part names should not leak path structure

Keep an intentionally shared chunk

Recommend a named chunk only when the user says something like:

  • "all pages should share one React vendor asset"
  • "I want one framework chunk for cache reuse across routes"

Even then, call out the tradeoff explicitly:

  • better cache hit rate
  • more coupling between routes
  • a page may fetch modules it does not execute immediately

Review checklist

When reviewing a user's config, explicitly answer:

  1. Is the goal dedupe, cache stability, request count, or route isolation?
  2. Is chunks: "all" a better baseline than the current config?
  3. Did name accidentally turn multiple candidates into one forced shared chunk?
  4. Were default or defaultVendors disabled without a strong reason?
  5. Would idHint satisfy the naming goal without changing grouping?
  6. Is maxSize a better fit than a broad manual vendor/common bucket?
  7. Does the result still keep each page fetching only reachable chunks?

Minimal stats setup

When the task includes diagnosis, ask for or generate stats that expose chunk relations:

js
stats: {
  chunks: true,
  chunkRelations: true,
  chunkOrigins: true,
  entrypoints: true,
  modules: false
}

Then compare:

  • which entrypoints reference which shared chunks
  • whether a change added a new dependency edge from an entry to a broad shared chunk
  • whether a large shared chunk exists only because of a fixed name

FAQ

Why do I still see duplicate modules?

Common reasons:

  • the shared candidate is too small, so extracting it would not satisfy minSize
  • the candidate does not satisfy minSizeReduction
  • it does not satisfy minChunks
  • request-budget limits reject the split
  • chunks / test / cacheGroups do not actually select the same chunk combination

If the duplicate module is tiny, do not assume this is a bug. Rspack may intentionally keep it in place because splitting it out would create a worse chunk.

Does splitChunks affect JS execution order?

No.

  • splitChunks only changes chunk boundaries and dependency edges
  • JS loading and execution order are runtime concerns
  • if a JS ordering bug appears, investigate runtime/bootstrap, side effects, or app code first

Does splitChunks affect tree shaking?

No.

  • tree shaking is controlled by module-graph analysis such as sideEffects, usedExports, and dead-code elimination
  • splitChunks runs later and only reorganizes already-selected modules into chunks
  • splitChunks.usedExports is only a grouping hint for runtime-specific chunk combinations; it is not tree shaking itself

Can splitChunks affect CSS order?

Yes, potentially.

  • this caveat applies to CSS order, not JS execution order
  • extracted CSS flows such as mini-css-extract-plugin or experiments.css can observe changed final CSS order after splitChunks rewrites chunk groups
  • if CSS order is critical, be careful when splitting order-sensitive styles into separate chunks

See web-infra-dev discussion #12.

Quick conclusions to reuse

  • "Keep chunks: \"all\", keep the default cache groups, and remove name unless you intentionally want forced sharing."
  • "name is not just a filename hint in Rspack splitChunks; it changes grouping behavior."
  • "splitChunks does not control JS execution order or tree shaking; it only changes chunk topology."
  • "splitChunks can affect CSS order in extracted-CSS scenarios, so treat CSS as a separate caveat."
  • "maxSize is the safer tool when the problem is one chunk being too large."

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 Rspack Split Chunks AI skill do?

Diagnose or tune Rspack splitChunks for duplicate modules, route over-fetching, cache groups, caching, or oversized chunks.

Why use Rspack Split Chunks on TypingMind?

Because you install it once and use it with any model. Rspack Split Chunks 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 Rspack Split Chunks in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rstackjs/agent-skills/tree/main/skills/rspack-split-chunks. 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 Rspack Split Chunks?

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 Rspack Split Chunks?

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

Is the Rspack Split Chunks AI skill free?

Yes. It is published on GitHub by rstackjs 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

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇