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Agent Skill · 研究检索

cognitive-load(认知负荷)

cognitive-load 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。

cognitive-load 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 51,060。

总安装 594周安装 25GitHub Stars 235下载 208核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:cognitive-load(认知负荷)
来源仓库:https://github.com/flpbalada/my-opencode-config
仓库路径:skills/cognitive-load
安装命令:
npx skills add https://github.com/flpbalada/my-opencode-config --skill cognitive-load
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 cognitive-load

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

cognitive-load 基于工作记忆限制理论提供信息架构与界面简化策略。适用于在 Codex、Claude、Cursor、Gemini CLI 中优化复杂表单与多步流程。区分内在、外在与关联认知负荷,提出针对性减负措施。提供信息分块、渐进披露与视觉引导设计建议。需配合用户测试验证实际认知负担变化。

来源仓库内路径:skills/cognitive-load

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/flpbalada/my-opencode-config --skill cognitive…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

cognitive-load 怎么安装?

在终端执行 npx skills add https://github.com/flpbalada/my-opencode-config --skill cognitive-load。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

cognitive-load 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

cognitive-load 需要什么权限?

权限级别是只读。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。

cognitive-load 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

cognitive-load 用的人多吗?

公开数据是累计安装 594 次,近一周安装 25 次,下载 208 次,GitHub 235 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

cognitive-load 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
cognitive-load

认知负荷 · 本页

594235只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Cognitive Load - Designing for Human Memory

Cognitive Load Theory (CLT), developed by John Sweller, provides a framework for designing experiences that work with human working memory limitations. Understanding the three types of cognitive load helps create interfaces that feel effortless and intuitive.

When to Use This Skill

  • Designing complex forms or workflows
  • Creating onboarding experiences
  • Simplifying feature-rich interfaces
  • Improving task completion rates
  • Planning information architecture
  • Reviewing designs for usability

Working Memory Limitations

Human Processing Capacity:

┌─────────────────────────────────────────────────────┐
│              WORKING MEMORY                          │
│                                                      │
│    Capacity: 7 ± 2 items simultaneously             │
│    Duration: ~20 seconds without rehearsal          │
│    Processing: Serial, not parallel                 │
│                                                      │
│    When exceeded → Errors, frustration, abandonment │
└─────────────────────────────────────────────────────┘

Design goal: Stay WITHIN capacity limits.

Three Types of Cognitive Load

Total Cognitive Load = Intrinsic + Extraneous + Germane

┌─────────────────────────────────────────────────────────────────┐
│                    WORKING MEMORY CAPACITY                       │
├─────────────────┬─────────────────┬─────────────────────────────┤
│   INTRINSIC     │   EXTRANEOUS    │          GERMANE            │
│                 │                 │                             │
│   Task          │   Bad design    │   Learning &                │
│   complexity    │   noise         │   schema building           │
│                 │                 │                             │
│   Can't reduce  │   ELIMINATE     │   MAXIMIZE                  │
│   without       │   THIS          │   THIS                      │
│   changing task │                 │                             │
└─────────────────┴─────────────────┴─────────────────────────────┘

1. Intrinsic Load

What it is: The inherent difficulty of the task itself.

CharacteristicExample
Determined by task complexityFiling taxes vs. sending email
Varies by user expertiseExpert finds it easy, novice struggles
Cannot be eliminatedOnly managed through design

Design strategies:

  • Break complex tasks into smaller steps
  • Provide progressive disclosure
  • Build on existing mental models
  • Offer different paths for different expertise levels

2. Extraneous Load

What it is: Unnecessary mental effort from poor design.

Common Sources of Extraneous Load:

Visual:
├── Cluttered layouts
├── Poor typography
├── Inconsistent patterns
└── Distracting animations

Interaction:
├── Unclear navigation
├── Unexpected behaviors
├── Too many options
└── Hidden functionality

Content:
├── Jargon and complexity
├── Redundant information
├── Missing context
└── Poor information hierarchy

Design strategies:

  • Eliminate decorative elements without purpose
  • Use consistent design patterns
  • Apply strong visual hierarchy
  • Remove redundant information

3. Germane Load

What it is: Productive mental effort that builds understanding.

BenefitExample
Builds mental modelsUser learns system behavior
Enables efficiencyExperienced user works faster
Creates transferable knowledgeSkills apply to similar tasks

Design strategies:

  • Provide clear feedback on actions
  • Create learnable, consistent patterns
  • Design onboarding that builds foundations
  • Use progressive enhancement for power features

Load Analysis Framework

Step 1: Map the Task

Task Decomposition:

[Main Task]
├── Step 1: [Action] → Load type: [I/E/G]
│   └── Decisions required: [count]
├── Step 2: [Action] → Load type: [I/E/G]
│   └── Decisions required: [count]
├── Step 3: [Action] → Load type: [I/E/G]
│   └── Decisions required: [count]
└── Completion

Step 2: Identify Load Sources

For each step, categorize the cognitive demands:

StepIntrinsic LoadExtraneous LoadGermane Load
1[Task complexity][Design issues][Learning value]
2[Task complexity][Design issues][Learning value]
3[Task complexity][Design issues][Learning value]

Step 3: Design Interventions

Intervention Strategy:

High Intrinsic Load?
├── YES → Break into smaller steps
├── YES → Add progressive disclosure
├── YES → Provide scaffolding/help
└── YES → Offer simplified path

High Extraneous Load?
├── YES → Remove unnecessary elements
├── YES → Improve visual hierarchy
├── YES → Simplify navigation
└── YES → Fix inconsistent patterns

Low Germane Load?
├── YES → Add meaningful feedback
├── YES → Create learnable patterns
├── YES → Design for skill building
└── YES → Connect to existing knowledge

Output Template

After completing analysis, document as:

## Cognitive Load Analysis

**Feature/Flow:** [Name]

**Date:** [Date]

### Task Map

| Step | Description | Intrinsic    | Extraneous   | Germane      |
| ---- | ----------- | ------------ | ------------ | ------------ |
| 1    | [Step]      | High/Med/Low | High/Med/Low | High/Med/Low |
| 2    | [Step]      | High/Med/Low | High/Med/Low | High/Med/Low |

### Load Issues Identified

#### Intrinsic Load Problems

- [Issue]: [Location]
- [Issue]: [Location]

#### Extraneous Load Problems

- [Issue]: [Location]
- [Issue]: [Location]

### Recommendations

| Priority | Change            | Expected Impact       |
| -------- | ----------------- | --------------------- |
| High     | [Specific change] | [Reduces X load by Y] |
| Medium   | [Specific change] | [Reduces X load by Y] |

### Success Metrics

| Metric               | Current | Target |
| -------------------- | ------- | ------ |
| Task completion rate | X%      | Y%     |
| Time to complete     | X min   | Y min  |
| Error rate           | X%      | Y%     |
| User satisfaction    | X       | Y      |

Real-World Examples

Google Search

Load Optimization:

Intrinsic:  Minimal - just type what you want
Extraneous: Near zero - white space + search box
Germane:    Focus on learning query refinement

Result: 2-3x faster searches than competitors

Slack Progressive Disclosure

New User Experience:
├── Basic: Messaging interface only
├── Intermediate: Threading, channels
├── Advanced: Integrations, workflows
└── Enterprise: Admin controls

Each level builds on previous knowledge (germane load)
without overwhelming new users (extraneous load).

Apple iOS Consistency

System-wide Patterns:

Gestures:
├── Swipe from left = back (everywhere)
├── Pull down = refresh (everywhere)
├── Long press = more options (everywhere)
└── Pinch = zoom (everywhere)

Consistency eliminates extraneous load.
Users learn once, apply everywhere.

Design Strategies by Load Type

Reducing Intrinsic Load

StrategyImplementation
Task chunkingMax 3-5 items per screen
Mental model alignmentUse familiar metaphors
Smart defaultsPre-select common choices
Contextual helpShow info when needed

Eliminating Extraneous Load

StrategyImplementation
Visual hierarchySize, color, position guide attention
Consistent patternsSame action = same interaction
Content prioritizationMost important info first
Distraction removalNo unnecessary animations/elements

Optimizing Germane Load

StrategyImplementation
Clear feedbackShow results of every action
Pattern consistencyCreate learnable behaviors
Progressive enhancementReveal features as expertise grows
Knowledge transferDesign transferable skills

Measurement Approaches

Quantitative

  • Task completion time
  • Error rate and recovery time
  • Number of help requests
  • Tab/window switches during task
  • Abandonment points

Qualitative

  • Think-aloud testing (confusion points)
  • Cognitive walkthroughs (step-by-step effort)
  • Post-task interviews (perceived difficulty)
  • Eye tracking (scanning patterns)

Integration with Other Methods

MethodCombined Use
Cognitive FluencyFluency is one way to reduce extraneous load
Hick's LawFewer choices = lower load
Progressive DisclosureManage intrinsic load over time
Graph ThinkingMap load relationships across system
Five WhysWhy are users struggling/abandoning?

Quick Reference

COGNITIVE LOAD CHECKLIST

Intrinsic Load Management:
□ Tasks broken into 3-5 step chunks
□ Progressive disclosure for complexity
□ Different paths for different expertise
□ Builds on familiar concepts

Extraneous Load Elimination:
□ Clear visual hierarchy
□ Consistent interaction patterns
□ No unnecessary decorative elements
□ Logical information architecture

Germane Load Optimization:
□ Clear feedback on all actions
□ Patterns that transfer across features
□ Onboarding builds mental models
□ Advanced features reward learning

Testing:
□ Task completion measured
□ Error rates tracked
□ User confusion points identified
□ Learning curve improving over time

Resources

平台分布

Codex

34.78%
按下载量换算72

Claude

28.43%
按下载量换算59

Cursor

19.6%
按下载量换算41

Gemini CLI

9.17%
按下载量换算19

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。目前只有一个公开来源,建议同时核对源仓库。

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