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hicks-law希克斯定律

Agent Skill

hicks-law 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

524

周安装

21

GitHub Stars

239

下载量

170
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:hicks-law(希克斯定律)
来源仓库:https://github.com/flpbalada/my-opencode-config
仓库路径:skills/hicks-law
安装命令:
npx skills add https://github.com/flpbalada/my-opencode-config --skill hicks-law
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/flpbalada/my-opencode-config --skill hicks-law

简介

hicks-law 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限和维护状态。
  • 使用前建议核验具体用法,注意是否会触发联网、命令执行或文件读写操作。
  • hicks-law 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Hick's Law - Less Choice, Faster Decisions

Hick's Law (also Hick-Hyman Law) states that the time it takes to make a decision increases logarithmically with the number and complexity of choices. Named after British psychologist William Edmund Hick and American psychologist Ray Hyman (1952).

When to Use This Skill

  • Designing navigation menus and information architecture
  • Simplifying onboarding and setup flows
  • Reducing form field options
  • Prioritizing feature exposure
  • Optimizing conversion funnels
  • Planning dashboard layouts

Core Concepts

The Formula

RT = a + b * log2(n+1)

Where:
RT = Reaction time
a  = Time not involved in decision (physical movement, etc.)
b  = Empirical constant (~0.155s for choice tasks)
n  = Number of equally probable choices

Practical Impact

ChoicesRelative Decision TimeUser Experience
2BaselineQuick, confident
4+1 unitStill manageable
8+2 unitsStarting to slow
16+3 unitsNoticeable hesitation
32+4 unitsOverwhelm begins
64++5+ unitsParalysis likely

The Paradox of Choice

       User Satisfaction
            ^
            |      *
            |   *     *
            |  *        *
            | *           *
            |*              *____
            +-----------------------> Number of Choices
                 Sweet spot
                (4-7 items)

Analysis Framework

Step 1: Audit Decision Points

Map all places users must choose:

Screen/FlowDecision TypeOptions CountComplexity
[Screen 1]Navigation[n][H/M/L]
[Screen 2]Selection[n][H/M/L]
[Screen 3]Configuration[n][H/M/L]

Step 2: Categorize Choices

Essential (keep)     Nice-to-have (maybe)     Remove
       |                    |                    |
       v                    v                    v
   [_______]            [_______]            [_______]
   [_______]            [_______]            [_______]
   [_______]            [_______]            [_______]

Step 3: Apply Reduction Strategies

  1. Chunking: Group related items (3-4 per group)
  2. Progressive disclosure: Hide advanced options
  3. Smart defaults: Pre-select the common choice
  4. Filtering: Let users narrow options
  5. Recommendations: Highlight "Most Popular"

Output Template

## Hick's Law Analysis

**Interface/Flow:** [Name] **Analysis Date:** [Date]

### Decision Point Inventory

| Location  | Current Options | Target | Strategy             |
| --------- | --------------- | ------ | -------------------- |
| [Point 1] | [n]             | [n]    | [Chunk/Hide/Default] |
| [Point 2] | [n]             | [n]    | [Chunk/Hide/Default] |

### Reduction Plan

**Quick wins (no functionality loss):**

1. [Change 1]
2. [Change 2]

**Strategic reductions (requires tradeoffs):**

1. [Change with impact analysis]

### Expected Impact

- Decision time reduction: ~[X]%
- Conversion improvement: ~[X]% (estimated)
- Support ticket reduction: ~[X]% (estimated)

Real-World Examples

Example 1: Netflix vs. Cable

Cable TV: 500+ channels = Decision paralysis

  • Users spend more time browsing than watching
  • Satisfaction decreases despite more options

Netflix approach:

  • Curated rows (chunking)
  • "Top 10" highlights (social proof + reduction)
  • "Because you watched..." (personalized filtering)
  • Auto-play (eliminates decision entirely)

Example 2: In-N-Out Burger

Menu has only 4 items vs. competitors' 50+:

  • Order time: 30 seconds vs. 2+ minutes
  • Customer satisfaction: Higher
  • Operation efficiency: Better

The constraint creates confidence in choice quality.

Example 3: Slack's Onboarding

Original: 15 configuration options upfront

  • Completion rate: 62%
  • Time to complete: 8 minutes

Redesigned: 3 essential questions, rest defaulted

  • Completion rate: 89%
  • Time to complete: 2 minutes

Best Practices

Do

  • Aim for 5-7 options maximum in any grouping
  • Use categorization to chunk larger sets
  • Provide clear visual hierarchy
  • Make the "default" choice obvious
  • Offer search/filter for large option sets

Avoid

  • Showing all features at once
  • Flat menus with 10+ items
  • Requiring decisions without clear benefit
  • Equal visual weight for all options
  • Removing options users actively need

When Hick's Law Doesn't Apply

  • Expert users with learned shortcuts
  • Emergency situations (trained responses)
  • When options are not equally weighted
  • Sequential vs. parallel choices

Reduction Techniques

1. Smart Defaults

Instead of:
[ ] Option A
[ ] Option B
[ ] Option C

Do:
[x] Option B (Recommended)
[ ] Option A
[ ] Option C

2. Progressive Disclosure

Basic Options
[Configure]

v Advanced (click to expand)
  [_] Setting 1
  [_] Setting 2

3. Chunking

Instead of 12 flat options:

Category A        Category B        Category C
- Item 1          - Item 5          - Item 9
- Item 2          - Item 6          - Item 10
- Item 3          - Item 7          - Item 11
- Item 4          - Item 8          - Item 12

Integration with Other Methods

MethodCombined Use
Progressive DisclosureHide complexity, reveal on demand
Cognitive LoadFewer choices = lower cognitive burden
Fogg Behavior ModelSimpler choices increase ability
Jobs-to-be-DoneFocus options on user's actual job

Resources

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.77%
按下载量换算61

Claude

28.69%
按下载量换算49

Cursor

21.77%
按下载量换算37

Gemini CLI

10.2%
按下载量换算17

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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