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Agent Skill · 研究检索

grad-cognitive-load(研究生认知负荷)

grad-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。

grad-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 46,177。

总安装 805周安装 17GitHub Stars 125下载 140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:grad-cognitive-load(研究生认知负荷)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/grad-cognitive-load
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-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/asgard-ai-platform/skills --skill grad-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。

grad-cognitive-load 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于认知科学、教学设计或用户体验研究等场景。通过关键词和来源线索匹配,返回相关理论模型与研究文献。

安装方式:npx skills add https://github.com/asgard-ai-platform/skills --skill grad-cognitive-load 建议确认权限范围、维护状态,以及是否涉及联网或命令执行。

来源仓库内路径:skills/grad-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/asgard-ai-platform/skills --skill grad-cogniti…;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

grad-cognitive-load 怎么安装?

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

grad-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。

grad-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。

grad-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。

grad-cognitive-load 用的人多吗?

公开数据是累计安装 805 次,近一周安装 17 次,下载 140 次,GitHub 125 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。

grad-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
grad-cognitive-load

研究生认知负荷 · 本页

805125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 Theory (CLT)

Overview

Cognitive Load Theory (Sweller, 1988) is grounded in the architecture of human cognition: working memory is severely limited in capacity (7 +/- 2 items) and duration, while long-term memory is essentially unlimited. Effective instructional design must manage three types of cognitive load — intrinsic (task complexity), extraneous (poor design), and germane (schema construction) — so that total load does not exceed working memory capacity.

When to Use

  • Diagnosing why learners fail to comprehend or retain instructional material
  • Redesigning documentation, tutorials, or training programs for reduced cognitive burden
  • Evaluating interface design, dashboards, or information displays for overload
  • Sequencing complex learning material to scaffold schema acquisition

When NOT to Use

  • When the problem is motivational rather than cognitive (learner can process but chooses not to)
  • For expert audiences where schemas already exist and the expertise reversal effect applies
  • When simplification would compromise essential task fidelity (some tasks are irreducibly complex)

Assumptions

IRON LAW: Working memory capacity is FIXED and limited —
instructional design must minimize extraneous load to maximize
germane processing. Total load (intrinsic + extraneous + germane)
must not exceed working memory capacity.

Key assumptions:

  1. Working memory processes novel information; long-term memory stores schemas that bypass WM limits
  2. Intrinsic load is determined by element interactivity — it cannot be reduced without changing the task
  3. Extraneous load is under the designer's control and should always be minimized

Methodology

Step 1 — Analyze Element Interactivity (Intrinsic Load)

Assess how many information elements must be processed simultaneously:

  • Low interactivity: elements can be learned independently (vocabulary lists)
  • High interactivity: elements must be integrated to be understood (grammar rules, circuit design)

Step 2 — Identify Extraneous Load Sources

SourceDescriptionDesign Flaw
Split-attentionIntegrating spatially/temporally separated sourcesText far from diagram
RedundancyProcessing identical information in multiple formatsNarration duplicating on-screen text
Transient informationInformation disappears before processing completesFast animations without pause
Expertise reversalScaffolding that helps novices but hinders expertsForced step-by-step for advanced users
Seductive detailsInteresting but irrelevant informationDecorative images, tangential stories

Step 3 — Optimize Load Distribution

Strategies to manage total cognitive load:

  • Worked examples: reduce intrinsic load for novices by showing solved problems
  • Fading: gradually transition from worked examples to independent problem-solving
  • Modality effect: use dual channels (visual + auditory) to expand effective WM capacity
  • Segmenting: break complex material into learner-paced segments
  • Pre-training: teach component elements before introducing interactions
  • Eliminate redundancy: remove duplicate information across channels

Step 4 — Design for Germane Load

  • Encourage self-explanation and elaboration
  • Use variability in practice problems to promote schema abstraction
  • Provide comparison cases that highlight structural similarities
  • Space practice over time (distributed practice) for schema consolidation

Output Format

## Cognitive Load Analysis: [Context]

### Intrinsic Load Assessment
- Element interactivity: [Low/Medium/High]
- Key interacting elements: [list]
- Learner expertise level: [Novice/Intermediate/Expert]

### Extraneous Load Audit
| Source | Present? | Severity | Fix |
|--------|----------|----------|-----|
| Split-attention | [Yes/No] | [High/Med/Low] | [solution] |
| Redundancy | [Yes/No] | [High/Med/Low] | [solution] |
| Transient info | [Yes/No] | [High/Med/Low] | [solution] |
| Seductive details | [Yes/No] | [High/Med/Low] | [solution] |

### Load Budget
- Estimated total load: [Within/Exceeding capacity]
- Extraneous reduction potential: [High/Medium/Low]

### Redesign Recommendations
1. [Primary extraneous load reduction]
2. [Segmenting or sequencing change]
3. [Germane load enhancement]

Gotchas

  • The expertise reversal effect means that designs optimal for novices actively harm experts — adaptive or layered design is necessary
  • "7 +/- 2" is a rough heuristic; effective WM capacity for novel interacting elements may be as low as 3-4 chunks
  • Germane load is debated in recent literature — some researchers subsume it under intrinsic load management rather than treating it as separate
  • Reducing extraneous load is always beneficial; reducing intrinsic load may oversimplify and prevent deep learning
  • Modality effect applies only when visual and auditory channels carry complementary (not redundant) information
  • Cognitive load is difficult to measure directly — proxy measures (performance, subjective ratings, secondary tasks) each have limitations

References

  • Sweller, J. (1988). Cognitive load during problem solving: effects on learning. *Cognitive Science*, 12(2), 257-285.
  • Sweller, J., Ayres, P. & Kalyuga, S. (2011). *Cognitive load theory*. Springer.
  • Paas, F., Renkl, A. & Sweller, J. (2003). Cognitive load theory and instructional design: recent developments. *Educational Psychologist*, 38(1), 1-4.

平台分布

Codex

36.18%
按下载量换算51

Claude

30.76%
按下载量换算43

Cursor

18.27%
按下载量换算26

Gemini CLI

9.66%
按下载量换算14

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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