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AI/MLhashgraph-online

recognition-recents-and-suggestions

Use this skill when designing surfaces that accelerate repeat tasks through recents, frequently-used items, and contextual suggestions. Trigger when designing pickers used repeatedly, command palettes, navigation that should adapt to user behavior, or any surface where a returning user shouldn''t have to retype their frequent destinations. Sub-aspect of `recognition-over-recall`; read that first.

Stars
336
Source
hashgraph-online/awesome-codex-plugins
Updated
2026-05-27
Slug
hashgraph-online--awesome-codex-plugins--recognition-recents-and-suggestions
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/hashgraph-online/awesome-codex-plugins/HEAD/plugins/HDeibler/universal-design-principles/plugins/cognition-and-learnability-principles/skills/recognition-recents-and-suggestions/SKILL.md -o .claude/skills/recognition-recents-and-suggestions.md

Drops the SKILL.md into .claude/skills/recognition-recents-and-suggestions.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

Recents, frequents, and contextual suggestions

Recognition is fastest when the user doesn't even have to scan a long list — when the system anticipates and surfaces the likely options first. Recents, frequents, and predictive suggestions all leverage this: the user's likely target is at the top, often without typing anything.

Patterns

Recents

A list of items the user has recently interacted with. Surfaced at the top of pickers, navigation, or empty search inputs.

<combobox label="Recipient">
  <input placeholder="Search recipients..." />
  <listbox>
    <group label="Recent">
      <option>Maria Mendoza (last sent: yesterday)</option>
      <option>Marketing Team (last sent: 3 days ago)</option>
    </group>
    <group label="All">...</group>
  </listbox>
</combobox>

Most users compose for a small set of recipients repeatedly; recents collapse the recall task.

Frequents

Items used most often (regardless of recency). Useful when usage is clustered around a small set but not necessarily recent.

Frequently used apps:
  • Email
  • Slack
  • Code editor
  • Browser

A common laptop dock pattern.

Recommended / suggested

System-predicted likely options based on context. Examples:

  • A "for you" feed.
  • "People you may know."
  • "Suggested replies" in messaging.
  • "Suggested tags" when categorizing.

Recommendations work when the prediction is good. Bad recommendations (irrelevant, wrong) are worse than none — they distract and erode trust.

Pinned / favorites

User-curated frequently-accessed items. Less algorithmic than recents/suggestions; user-explicit.

Pinned:
  ★ Q4 Planning Doc
  ★ Team OKRs
  ★ Customer feedback dashboard

Combine with recents and suggestions for a complete fast-access surface.

Smart defaults

Pre-fill fields with predicted values based on context (signed-in user, recent inputs, time of day, location).

<form>
  <label>Country
    <select name="country">
      <option value="US" selected>United States</option>
      <!-- selected because of user's IP location -->
    </select>
  </label>
</form>

The user can change but rarely needs to.

When recents/suggestions hurt

  • When the prediction is bad. Wrong recents distract; the user has to filter past them.
  • When privacy matters. Recents reveal user history; in shared-device contexts this can leak information.
  • When the option set is critical to the task. A "recent" suggestion in a destructive action might bias the user toward the wrong choice.

For high-stakes actions, present the full set without privileging recents.

Privacy and recents

Recents reveal user activity to anyone with screen access. Considerations:

  • Don't surface recents on shared/public devices unless explicitly opted in.
  • Provide a "clear recents" option.
  • Don't leak across tenants (a recent in workspace A shouldn't appear when the user switches to workspace B).
  • Be cautious with sensitive contexts (health apps, finance apps, dating apps).

Anti-patterns

  • Stale recents that include items the user no longer cares about, never expiring.
  • Recents that span privacy boundaries (work email recents in personal context).
  • Suggestions that don't update as the user's behavior changes.
  • Surfacing recents in wrong contexts (showing "recent files" on a different user's account).

Heuristics

  1. The "would the user pick this without typing?" check. For each picker, ask: in the median case, can the user get to their target without typing? If yes, recents are doing their job.
  2. The recents-quality audit. Sample your recents lists. Are they actually relevant to current intent?
  3. The privacy review. What do recents reveal? Should they be hidden by default in some contexts?

Related sub-skills

  • recognition-over-recall (parent).
  • recognition-pickers-and-palettes — picker patterns recents augment.
  • satisficing — recents enable satisficing by surfacing acceptable options first.
  • hicks-law-defaults — defaults and recents both reduce decision cost.