A skill your agent uses whenever the user must hold a sequence of items in working memory — phone numbers, OTP codes, account IDs, address strings, multi-step instructions, long forms, navigation…

Apache-2.0Auto-check passed

Install Chunking

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill chunking -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins chunking --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/chunking .claude/skills/chunking && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
chunking
GitHub stars
1.2k
Token cost
~3.2k tokens
SKILL.md length
1,507 words
Files
2 (incl. references)
Skills in repo
686
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses whenever the user must hold a sequence of items in working memory — phone numbers, OTP codes, account IDs, address strings, multi-step instructions, long forms, navigation…

  • Works in 4 steps: Count chunks visible. More than ~7?… → Count items per chunk. More than ~5?… → The "what's in this group?" test. Can… → …
  • The user must hold a sequence of items in working memory — phone numbers
  • SKILL.md covers Definition (in our own words), Origins and research lineage, Why chunking matters and When to apply, plus 9 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Chunking is an agent skill from hashgraph-online/awesome-codex-plugins. Use this skill whenever the user must hold a sequence of items in working memory — phone numbers, OTP codes, account IDs, address strings, multi-step instructions, long forms, navigation menus with many items. Trigger when designing OTP / verification UIs, formatting numeric strings, breaking long forms into sections, grouping nav items, or reviewing surfaces that "have too many things at once." Chunking is one of the foundational principles in 'Universal Principles of Design' (Lidwell, Holden, Butler 2003)…

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/working-memory-research.md`).

The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • The user must hold a sequence of items in working memory — phone numbers
  • Address strings
  • Multi-step instructions
  • Navigation menus with many items

Example prompts

  • “have too many things at once.”
  • “Universal Principles of Design”
  • “/chunking”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Count chunks visible. More than ~7? Reorganize or split.
  2. Count items per chunk. More than ~5? Split that chunk.
  3. The "what's in this group?" test. Can you name each chunk in 2–3 words? If yes, the chunking is meaningful. If no, the chunks are…
  4. The scanning vs. memorizing diagnostic. Is the user holding this in mind, or finding it? If finding, chunking may be noise; replace with…

What it can do on your machine

Read from SKILL.md and the folder at commit 78497e5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are html).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Chunking loads about 3.2k tokens when it runs, and up to ~4.4k if it reads all its reference files. Until then it costs about 144 tokens; SKILL.md has 1,507 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~144
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 78497e5, republished under its Apache-2.0 licence (© hashgraph-online). 1,507 words, ~3,179 tokens.

Download SKILL.mdSave it as .claude/skills/chunking/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
chunking
description
Use this skill whenever the user must hold a sequence of items in working memory — phone numbers, OTP codes, account IDs, address strings, multi-step instructions, long forms, navigation menus with many items. Trigger when designing OTP / verification UIs, formatting numeric strings, breaking long forms into sections, grouping nav items, or reviewing surfaces that "have too many things at once." Chunking is one of the foundational principles in 'Universal Principles of Design' (Lidwell, Holden, Butler 2003), grounded in Miller's classic working-memory research.

Chunking

Chunking is the practice of grouping a long string of items into a smaller number of meaningful units, each containing a few items. The classic insight: working memory can hold only a handful of independent items, but each "item" can itself be a chunk containing several pieces. Phone numbers are easier to remember as 555-867-5309 (three chunks) than as 5558675309 (ten digits). The same principle applies broadly across UI design.

Definition (in our own words)

Working memory is a small, short-lived store for the items the user is actively manipulating. Its capacity is roughly four to seven independent units. When information is presented as one long unbroken sequence, the user has to hold all of it as separate units and quickly hits the limit. When information is grouped into a few meaningful chunks, the same total content fits within the working-memory budget. Chunking is the design technique for fitting information to that budget.

Origins and research lineage

  • George Miller, "The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information." Psychological Review, 1956, vol. 63, p. 81–97. The foundational paper. Miller observed that across many tasks (recalling lists, distinguishing tones, judging quantities), people maxed out at about 7 ± 2 items. The paper introduced "chunk" as the unit of measurement: an item could itself be made up of smaller items, but it counted as one chunk if treated as a unit.
  • Nelson Cowan, "The Magical Number Four in Short-Term Memory: A Reconsideration of Mental Storage Capacity." Behavioral and Brain Sciences, 2001, vol. 24, p. 87–114. Refined Miller's estimate downward. Cowan's analysis found that when chunks are truly independent (not aided by rehearsal or grouping), capacity is closer to 4 ± 1 than 7 ± 2. The "magical number" is now usually given as 4.
  • Alan Baddeley, Working Memory (1986) and Working Memory, Thought, and Action (2007). The standard reference work on the structure and limits of working memory.
  • Lidwell, Holden & Butler (2003) compactly summarized the design implications and warned about misapplication: chunking is for tasks involving memory, not for tasks involving scanning (like consulting a reference list).

Why chunking matters

When users must hold multiple items in mind — to dial a phone number, type a confirmation code, follow multi-step directions, complete a multi-part form — the amount they can hold determines whether they succeed. Exceeding working memory means they make mistakes, look back, or give up.

Chunking expands the effective capacity by packing more information into each chunk. A user can hold roughly 4 chunks; if each chunk contains 3 digits, that's 12 digits total — meaningful for phone numbers. Without chunking, 12 raw digits is well past the limit.

Chunking also accelerates learning: chunked patterns become familiar units (the "555" prefix becomes one chunk, not three) and free up capacity for new information.

When to apply

  • Numeric strings the user must type or read — phone numbers, OTP codes, account numbers, IDs, dates, currency.
  • Long forms — group fields into named sections of 4–6 fields each.
  • Navigation menus — group items into named sections of 4–7 items each.
  • Multi-step instructions — break a 12-step process into three groups of four steps.
  • Tables with many columns — visually group columns by category if they're related.
  • Dashboards with many widgets — group widgets into themed regions.

When NOT to apply

The book's specific warning: don't chunk reference content the user scans rather than memorizes.

  • Long lists the user filters or searches — a contact list with 200 entries shouldn't be chunked into "Contacts A–E, F–J, K–O..." because the user isn't memorizing it; they're scanning or searching. Forced chunking adds visual noise without cognitive benefit.
  • Reference material the user looks up — dictionary entries, documentation pages, settings pages where the user knows what they want. Search and clear labeling beat chunking.
  • Continuous prose — chunking sentences into "first 4 words, next 4 words, next 4 words" damages reading. Body text follows its own rhythm.

The discriminator: are users holding the items in mind (apply chunking) or finding them (don't)?

Optimal chunk size

Different sources give different numbers; the working consensus:

  • Each chunk: 3–5 items. More than 5 risks overflow within the chunk.
  • Total chunks visible: 4–7. More than 7 risks overflow across chunks.
  • For numeric strings: typically 3–4 digits per chunk. Phone numbers, credit cards, OTP codes all converged on this.

These are heuristics; specific tasks may justify different sizes. The grouping should always be perceptually clear (visual gap, dash, slot separation).

Worked examples

Example 1: phone numbers

Most common chunking case. A 10-digit US phone number formatted three ways:

5558675309        ← unchunked: 10 digits, hard to verify or recall
555-867-5309      ← three chunks: 3-3-4
(555) 867-5309    ← same three chunks, with area code marked
555 867 5309      ← spaces instead of dashes

The chunked versions are easier to read aloud, easier to verify, easier to remember briefly. Most international phone formats follow similar logic.

Example 2: OTP / verification codes

A 6-digit OTP entered into a single field is hard to verify mid-entry. Chunked input fields make each digit's position visible:

html
<input type="text" inputmode="numeric" maxlength="6" />  <!-- unchunked -->

<!-- vs. chunked input -->
<div class="otp-input" role="group" aria-label="Verification code">
  <input type="text" inputmode="numeric" maxlength="1" />
  <input type="text" inputmode="numeric" maxlength="1" />
  <input type="text" inputmode="numeric" maxlength="1" />
  <span class="separator" aria-hidden>—</span>
  <input type="text" inputmode="numeric" maxlength="1" />
  <input type="text" inputmode="numeric" maxlength="1" />
  <input type="text" inputmode="numeric" maxlength="1" />
</div>

The chunked version (3-3) helps users verify they typed the right digits and matches the chunked format that's typically displayed in the originating email or SMS.

Example 3: long forms broken into sections

A 24-field signup form is overwhelming. Group into 4 sections of ~6 fields each, with named headings:

html
<form>
  <section>
    <h2>Account</h2>
    <!-- 5 fields: email, password, name, etc. -->
  </section>
  <section>
    <h2>Profile</h2>
    <!-- 4 fields: title, bio, photo, etc. -->
  </section>
  <section>
    <h2>Workspace</h2>
    <!-- 6 fields: workspace name, members, etc. -->
  </section>
  <section>
    <h2>Preferences</h2>
    <!-- 5 fields: notifications, language, etc. -->
  </section>
</form>

The user holds in mind "I'm in the workspace section" rather than "I'm at field 17 of 24." Each section is a manageable chunk.

Example 4: navigation grouped by purpose

A sidebar with 18 nav items is hard to scan; grouped into 3 themed sections of 6 items each:

html
<nav>
  <h3>Workspace</h3>
  <ul>
    <li><a href="/dashboard">Dashboard</a></li>
    <li><a href="/projects">Projects</a></li>
    <li><a href="/team">Team</a></li>
    <li><a href="/calendar">Calendar</a></li>
    <li><a href="/files">Files</a></li>
    <li><a href="/inbox">Inbox</a></li>
  </ul>
  <h3>Reports</h3>
  <ul>...</ul>
  <h3>Settings</h3>
  <ul>...</ul>
</nav>

Users learn the section structure quickly and use it to predict where things live.

Show full SKILL.md (596 more words)Show less
Example 5: address strings

An address string is a chunked structure even when displayed compactly:

1234 Main Street, Apt 5B
San Francisco, CA 94110

Two-line break separates "street" chunk from "city/state/zip" chunk. The internal commas chunk apartment from street and city from state from zip. Users parse and recall this far better than the same characters in one continuous line.

Example 6: multi-step instruction

"To set up your account: Open Settings, click Notifications, choose Email, set frequency, save changes" — five steps in one sentence. Chunked as a numbered list:

1. Open Settings
2. Click Notifications
3. Choose Email
4. Set frequency
5. Save changes

Easier to follow; users can mark progress and look back at any step.

Cross-domain examples

Music

Western music notation chunks notes into measures (typically 3 or 4 beats). Beats group into bars; bars group into phrases; phrases group into sections. Musicians read music as nested chunks, not as individual notes.

Reading

Skilled readers don't process individual letters; they process chunks (common letter combinations, then whole words, then phrases). Speed reading techniques are largely about recognizing larger chunks more efficiently.

Chess

Expert chess players recall positions vastly better than novices — not because their working memory is larger, but because they chunk pieces into meaningful patterns (a defensive formation, an opening structure). De Groot's classic studies (1965) showed that experts and novices recalled random piece arrangements equally; the expert advantage came entirely from recognizing meaningful chunks.

Telephone area codes

Area codes are mnemonic chunks that group geography into recognizable units. "212" is Manhattan; "415" is San Francisco. The numeric chunk doubles as a categorical signal.

Anti-patterns

  • Chunking what should be searched. Forcing a long list into chunks the user must navigate when search would do.
  • Chunks too large. A "section" containing 20 items isn't a chunk; it's a list.
  • Chunks too small. A "section" containing 2 items isn't worth its overhead. Combine.
  • Inconsistent chunk sizes within a sequence. A 10-digit string chunked 5-2-3 is harder than 3-3-4 because the rhythm is irregular.
  • Visual chunking that contradicts logical chunking. Visual separators in places that don't match meaning ("123-4567-89" is structurally awkward even if visually chunked).
  • Decorative grouping with no labels. Grouping must be communicated via headings or visible regions. Just "putting space between things" without naming the groups misses the cognitive benefit.

Heuristics

  1. Count chunks visible. More than ~7? Reorganize or split.
  2. Count items per chunk. More than ~5? Split that chunk.
  3. The "what's in this group?" test. Can you name each chunk in 2–3 words? If yes, the chunking is meaningful. If no, the chunks are arbitrary; rethink.
  4. The scanning vs. memorizing diagnostic. Is the user holding this in mind, or finding it? If finding, chunking may be noise; replace with search and clear labeling.
  • performance-load — chunking is the canonical reduction of cognitive load.
  • hicks-law — fewer visible options means faster decisions; chunking and Hick's Law often combine in nav design.
  • progressive-disclosure — disclosure works between chunks (show one section, hide others); chunking works within the visible content.
  • mnemonic-device — chunking is itself a mnemonic technique; explicit mnemonics layer on top.
  • signal-to-noise-ratio (perception) — well-chunked content has less perceptual noise.
  • proximity (perception) — proximity is the visual mechanism for showing chunks: closer items group, gaps mark boundaries.
  • hierarchy (perception) — chunked content carries a hierarchy of structure (sections > items).

Sub-aspect skills

  • chunking-form-grouping — applying chunking to long forms.
  • chunking-numeric-and-otp — applying chunking to numeric strings, OTP codes, and identifiers.

Closing

Chunking is the cheapest cognitive lift available in design — it costs only structure, not new content. The discipline is recognizing when content is being held in working memory (chunk it) versus scanned for retrieval (don't add chunks; use search). When applied correctly, chunking turns "too much to hold" into "manageable."

© hashgraph-online, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/chunking of hashgraph-online/awesome-codex-plugins.

  • SKILL.md
  • references/working-memory-research.md

Open the folder on GitHubat commit 78497e5

Compare with similar skills

Chunking next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Chunking compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Chunking this skillhashgraph-online/awesome-codex-plugins1.2k—~3.2kAutomated safety check: PassApache-2.0
Work Item Sequencingforcedotcom/salesforcedx-vscode1k—~1.1kAutomated safety check: PassBSD-3-Clause
Action Items List Managerasgeirtj/system_prompts_leaks69k—~4.9kAutomated safety check: PassCC0-1.0
Legal Holdanthropics/claude-for-legal9.6k1 repos~3.8kAutomated safety check: PassApache-2.0
Document To Action ItemsNousResearch/hermes-agent252k—~976Automated safety check: PassMIT
Meeting Action ItemsNousResearch/hermes-agent252k—~950Automated safety check: PassMIT

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Questions about Chunking

What does Chunking do?

A skill your agent uses whenever the user must hold a sequence of items in working memory — phone numbers, OTP codes, account IDs, address strings, multi-step instructions, long forms, navigation…. Chunking is an agent skill from hashgraph-online/awesome-codex-plugins. Use this skill whenever the user must hold a sequence of items in working memory — phone numbers, OTP codes, account IDs, address strings, multi-step instructions, long forms, navigation menus with many items.

When should I use Chunking?

Chunking fits situations like: the user must hold a sequence of items in working memory — phone numbers; address strings; multi-step instructions; navigation menus with many items.

How do I install Chunking in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill chunking -a claude-code`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/chunking in hashgraph-online/awesome-codex-plugins) into .claude/skills/chunking in your project. Claude Code loads it when a task matches its description.

How do I install Chunking in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill chunking -a codex`. Or copy the skill folder (plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/chunking in hashgraph-online/awesome-codex-plugins) into .agents/skills/chunking in your project. Codex loads it when a task matches its description.

Can I use Chunking in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill chunking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chunking, .gemini/skills/chunking, .github/skills/chunking and .opencode/skills/chunking in your project.

What does Chunking need to run?

SKILL.md names no scripts, command-line tools or credentials: Chunking is instructions for the agent only.

Does Chunking access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Chunking safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Chunking use?

Chunking is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Chunking use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.2k tokens, read only when the agent opens those files.

What are the alternatives to Chunking?

Skills that share tags, products or a category with Chunking: Work Item Sequencing (forcedotcom/salesforcedx-vscode, 1k stars), Action Items List Manager (asgeirtj/system_prompts_leaks, 69k stars), Legal Hold (anthropics/claude-for-legal, 9.6k stars) and Document To Action Items (NousResearch/hermes-agent, 252k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Chunking?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,242 GitHub stars. The repository holds 686 skills in this directory. The repository was last updated on October 8, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.