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Recognition Pickers And Palettes

Community
Deibler
recognition-pickers-and-palettes

Use this skill when designing pickers, dropdowns, comboboxes, autocompletes, command palettes, or any UI that lets the user select from a known set. Trigger when picking between a free-text field and a select; when designing a command palette; when reviewing why users keep typing wrong values into autocomplete inputs. Sub-aspect of `recognition-over-recall`; read that first.

Overview

PublisherDeibler
Repositoryuniversal-design-principles
Skill namerecognition-pickers-and-palettes
Stars
9
Forks
3
Bundled files
1
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 Deibler on GitHub. Read the source before you install it.

Installation

Install the Recognition Pickers And Palettes 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.
    https://github.com/Deibler/universal-design-principles/tree/main/plugins/cognition-and-learnability-principles/skills/recognition-pickers-and-palettes
  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/Deibler/universal-design-principles.git /tmp/universal-design-principles
mkdir -p .claude/skills
cp -r /tmp/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-pickers-and-palettes .claude/skills/recognition-pickers-and-palettes
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Recognition Pickers And Palettes 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 Recognition Pickers And Palettes 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 Recognition Pickers And Palettes 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.

Pickers, palettes, and recognition-based selection

Selection UI is the most direct application of recognition-over-recall. Showing the user a list of valid options — visually scannable or filterable — beats requiring them to type from memory.

The picker ladder by set size

Different selection patterns suit different option counts:

Option countPatternNotes
2Toggle / switchVisual either-or
3–5Radio group / segmentedAll options visible
5–8Select dropdownVisible on click
8–20Grouped selectCategorical chunking helps
20+Combobox / typeaheadFilter-as-you-type
100+Search-first pickerSearch-driven; recents augment

Going lighter than the count permits forces unnecessary recall. Going heavier adds complexity without benefit.

Patterns

Combobox / typeahead

html
<combobox label="Country">
  <input placeholder="Search countries..." />
  <listbox>
    <option>United States</option>
    <option>United Kingdom</option>
    <!-- filtered as user types -->
  </listbox>
</combobox>

The user types partial recall; the system filters; the user recognizes the right option. Tolerates typos with fuzzy matching.

Command palette (cmd-K)

A palette that lists actions, navigation destinations, and search:

[ ⌘K opens palette ]

Search:
> Recents
  Create invoice
  Open settings
> Actions
  Search projects
  Invite teammate
> Navigation
  Go to Dashboard
  Go to Projects

Categorical groups + search + recents = recognition for nearly any command.

Recents at the top of pickers

html
<combobox label="Assignee">
  <listbox>
    <group label="Recent">
      <option>Maria Mendoza</option>
      <option>Lin Chen</option>
    </group>
    <group label="All members">
      <!-- alphabetical -->
    </group>
  </listbox>
</combobox>

Repeat-task speed via recents.

Suggestions and predictions

For inputs where the system can predict likely values:

html
<input
  name="email"
  type="email"
  autocomplete="email"
  list="email-suggestions"
/>
<datalist id="email-suggestions">
  <option value="user@example.com" />
  <option value="user@gmail.com" />
</datalist>

The browser surfaces matching options; the user recognizes and selects.

Visual pickers for inherently-visual content

For colors, dates, locations, etc., visual pickers are more recognition-friendly than coded inputs:

  • Color picker (visual swatch grid) beats hex code input.
  • Date picker (calendar) beats text date input.
  • Map picker (visual location) beats lat/long input.

Combobox accessibility

The WAI-ARIA Combobox pattern documents the keyboard interaction:

  • Tab enters the input.
  • Arrow keys navigate the listbox.
  • Enter selects the highlighted option.
  • Esc closes the listbox without selection.

Reach for an accessible combobox library (Radix, React Aria, Headless UI) rather than rolling your own — focus management and ARIA are easy to get wrong.

Anti-patterns

  • Free-text where a picker would do. Asking users to type "United States" exactly when a dropdown could show options.
  • Empty pickers that require typing to see anything (vs. showing options on focus).
  • Picker without filter for sets larger than ~10 items. Scrolling 200 items is slow.
  • No recents. Users repeatedly retype the same destinations.
  • Picker labels that don't match user vocabulary. Showing internal names instead of friendly names.

Heuristics

  1. The "what's available?" test. For each input, ask: how does the user know what valid options are? If they don't, you're requiring recall.
  2. The set-size match. Match the picker pattern to the option count.
  3. The recents-presence audit. For pickers used repeatedly, are recents surfaced? If not, every selection is a fresh recognition task.

Related sub-skills

  • recognition-over-recall (parent).
  • recognition-recents-and-suggestions — accelerating repeat tasks.
  • hicks-law-menus — picker design under decision-time constraints.
  • accessibility-operable — comboboxes are notorious accessibility-test surfaces.

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 Recognition Pickers And Palettes AI skill do?

Use this skill when designing pickers, dropdowns, comboboxes, autocompletes, command palettes, or any UI that lets the user select from a known set. Trigger when picking between a free-text field and a select; when designing a command palette; when reviewing why users keep typing wrong values into autocomplete inputs. Sub-aspect of `recognition-over-recall`; read that first.

Why use Recognition Pickers And Palettes on TypingMind?

Because you install it once and use it with any model. Recognition Pickers And Palettes 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 Recognition Pickers And Palettes in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Deibler/universal-design-principles/tree/main/plugins/cognition-and-learnability-principles/skills/recognition-pickers-and-palettes. 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 Recognition Pickers And Palettes?

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 Recognition Pickers And Palettes?

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

Is the Recognition Pickers And Palettes AI skill free?

It is published on GitHub by Deibler. Check the repository for licensing terms. 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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