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Agent Skill · 研究检索

cognitive-foundations(认知基础)

cognitive-foundations 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-foundations 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 53,999。

总安装 517周安装 22GitHub Stars 35下载 181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-foundations(认知基础)
来源仓库:https://github.com/petekp/claude-code-setup
仓库路径:skills/cognitive-foundations
安装命令:
npx skills add https://github.com/petekp/claude-code-setup --skill cognitive-foundations
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/petekp/claude-code-setup --skill cognitive-foundations

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-foundations 提供认知科学的底层原理与设计应用合约。适用于在 Codex、Claude、Cursor、Gemini CLI 中 grounding 设计决策于实证研究。输出单原则分析报告,链接 Fitts、Hick-Hyman 等定律到具体场景。支持注意力、感知与记忆失败的模式诊断。

需结合领域知识选择适用原则,避免脱离上下文使用。

来源仓库内路径:skills/cognitive-foundations

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/petekp/claude-code-setup --skill cognitive-fou…;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-foundations 怎么安装?

在终端执行 npx skills add https://github.com/petekp/claude-code-setup --skill cognitive-foundations。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

cognitive-foundations 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-foundations 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-foundations 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-foundations 用的人多吗?

公开数据是累计安装 517 次,近一周安装 22 次,下载 181 次,GitHub 35 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-foundations 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-foundations

认知基础 · 本页

51735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 Foundations

The science of how minds work, and what that means for design.

When to Use This Skill

  • Explaining *why* a design works or fails (grounded in research, not opinion)
  • Evaluating cognitive load or working memory demands
  • Predicting user performance (Fitts, Hick-Hyman)
  • Diagnosing mental model misalignment
  • Justifying design decisions to stakeholders with evidence
  • Understanding attention, perception, or memory failures

Output Contracts

For Single-Principle Analysis

## Cognitive Principle: [Name]

**Principle**: [1-sentence explanation]

**Evidence in Design**: [Where/how this applies]

**Implication**: [Specific, actionable recommendation]

**Confidence**: [High/Medium/Low] — [rationale]

For Cognitive Audit (Comprehensive)

## Cognitive Audit: [Screen/Flow Name]

### Working Memory Load
- Items requiring recall: [count]
- Cross-screen memory demands: [Y/N]
- Verdict: [Acceptable / High / Overloaded]

### Attention Demands
- Preattentive features for critical info: [Y/N]
- Competing attention demands: [list]
- Change blindness risk: [areas where changes may go unnoticed]

### Mental Model Alignment
- Expected user model: [what users likely think]
- System behavior: [what actually happens]
- Gap: [mismatch, if any]

### Predictive Laws
- Fitts's Law concerns: [target size/distance issues]
- Hick's Law concerns: [choice overload areas]

### Gulf Analysis
- Gulf of Execution: [unclear how to act?]
- Gulf of Evaluation: [unclear what happened?]

### Violations of Nielsen's Heuristics
| Heuristic | Violation | Severity |
|-----------|-----------|----------|
| ... | ... | 1-4 |

### Recommendations
1. [Highest priority fix]
2. [Second priority]
3. [Third priority]

For Explaining a Failure

## Failure Analysis: [What Went Wrong]

**Observed Behavior**: [What users did]

**Cognitive Explanation**: [Which principle explains this]

**Root Cause**: [Design element that caused it]

**Fix**: [Specific change]

Quick Reference: Predictive Laws

LawFormulaRule of Thumb
Fitts's LawMT = a + b × log₂(2D/W)Bigger + closer = faster. Screen edges are infinite.
Hick-HymanRT = a + b × log₂(n+1)More choices = slower. Reduce or organize options.
Steering LawT = a + b × (A/W)Narrow paths are slow. Cascading menus are hard.
Power LawT = a × N^(-b)Practice helps. Design for learnability.

Quick Reference: Nielsen's 10 Heuristics

#HeuristicQuick Test
1Visibility of system statusCan user always tell what's happening?
2Match system ↔ real worldLanguage familiar? Metaphors sensible?
3User control and freedomEasy undo? Clear exits?
4Consistency and standardsSame words/actions mean same things?
5Error preventionConstraints prevent errors before they occur?
6Recognition over recallOptions visible? No memory required?
7Flexibility and efficiencyShortcuts for experts?
8Aesthetic and minimalistOnly relevant info? No clutter?
9Error recoveryErrors explained in plain language with fix?
10Help and documentationSearchable, task-focused, concise?

Quick Reference: Working Memory

  • Capacity: ~4 chunks (not 7)
  • Duration: ~20 seconds without rehearsal
  • Test: Count items user must hold in mind across screens/steps

Red flags:

  • "Remember this code and enter it on the next page"
  • Multi-step forms without visible progress/state
  • Complex comparisons requiring mental tracking

Quick Reference: Preattentive Features

Detected in <200ms, no focused attention required:

  • Color (hue, saturation)
  • Size (length, area)
  • Orientation (angle)
  • Motion (flicker, direction)
  • Shape (curvature, enclosure)

Use for: Critical info, errors, changes, status Don't use for: Everything (loses signal value)


Cognitive Load Checklist

Quick assessment for any interface:

FactorLow LoadHigh Load
Choices visible2-4 options10+ options
Memory demandsRecognitionRecall
Steps to goal1-3 clicks5+ clicks
InterruptionsNoneFrequent modals
Novel elementsFamiliar patternsNew conventions
Error recoveryClear undoDestructive actions
Visual complexityClean, groupedDense, undifferentiated

Scoring: Each "High Load" = +1. Score >3 = redesign needed.


Common Violations → Principle

SymptomLikely ViolationFix
Users don't notice changesChange blindnessAnimate, highlight transitions
Users can't find the buttonPoor Fitts's LawIncrease size, reduce distance
Users freeze at optionsHick's Law overloadReduce choices, progressive disclosure
Users forget mid-taskWorking memory exceededShow state, don't require recall
Users misunderstand stateGulf of EvaluationBetter feedback, visibility
Users click wrong thingPoor affordance/signifierClearer visual treatment
Users make same error repeatedlyMode errorVisible mode indicators
Users abandon complex formsCognitive loadChunk, scaffold, save progress

Process

  1. Identify cognitive demands — What is the interface asking the user to perceive, remember, decide, or do?
  2. Match to principles — Which cognitive constraints or laws apply?
  3. Evaluate alignment — Does the design respect or violate these?
  4. Recommend changes — Specific modifications grounded in the principle

Deep Reference Files

For comprehensive principles and research:

  • PSYCHOLOGY.md — Perception, memory, attention, biases, emotion, motivation
  • HCI-THEORY.md — Norman's model, predictive laws, error theory, research methods, heuristics

Primary Sources


Key Researchers

  • Don Norman: Affordances, gulfs, emotional design
  • Daniel Kahneman: Dual process theory, heuristics and biases
  • Stuart Card: GOMS, information foraging, Fitts's Law
  • Anne Treisman: Feature integration, preattentive processing
  • Jakob Nielsen: Usability heuristics, discount usability
  • Ben Shneiderman: Direct manipulation, golden rules

Remember

  • Cognitive science explains *why* design principles work
  • Individual differences exist—design for variability, not averages
  • Lab findings may not generalize (ecological validity matters)
  • Theory informs but doesn't replace observing real users
  • When in doubt, reduce cognitive load—users have less capacity than you think

平台分布

Codex

34.4%
按下载量换算62

Claude

29.88%
按下载量换算54

Cursor

18.1%
按下载量换算33

Gemini CLI

9.29%
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。目前只有一个公开来源,建议同时核对源仓库。

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