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grad-elm等级榆树

Agent Skill

grad-elm 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

367

周安装

15

GitHub Stars

125

下载量

119
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:grad-elm(等级榆树)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/grad-elm
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-elm
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-elm

简介

grad-elm 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。

  • 适用于生态学、环境科学或系统建模研究场景。
  • 通过关键词和来源线索匹配,返回相关模型与研究成果。
  • 安装方式:npx skills add https://github.com/asgard-ai-platform/skills --skill grad-elm
  • 建议确认权限范围、维护状态,以及是否涉及联网或文件操作。

SKILL.md

Elaboration Likelihood Model (ELM)

Overview

The Elaboration Likelihood Model (Petty & Cacioppo, 1986) proposes that persuasion occurs through two distinct routes depending on the audience's motivation and ability to process a message. The central route relies on careful evaluation of argument quality, producing durable attitude change. The peripheral route relies on heuristic cues (source attractiveness, number of arguments), producing temporary and fragile attitude shifts.

When to Use

  • Designing marketing, policy, or internal communication strategies for different audience segments
  • Diagnosing why a well-reasoned message failed (audience lacked motivation/ability to process)
  • Predicting whether attitude change will persist and resist counter-persuasion
  • Choosing between investing in argument quality vs. source credibility or presentation style

When NOT to Use

  • When the goal is behavioral compliance rather than genuine attitude change (use compliance techniques)
  • For purely emotional appeals where cognitive processing models are insufficient (use affect-as-information)
  • When the audience has no prior schema on the topic and needs education before persuasion

Assumptions

IRON LAW: Attitude change via the central route is MORE durable
and resistant to counter-persuasion — but requires motivation
AND ability to process. When either is absent, only the
peripheral route is available, and its effects decay.

Key assumptions:

  1. Elaboration exists on a continuum from low to high, not a strict binary
  2. The same variable (e.g., source expertise) can serve as argument, cue, or affect elaboration direction depending on context
  3. Central-route attitudes predict behavior better than peripheral-route attitudes

Methodology

Step 1 — Assess Audience Elaboration Likelihood

FactorHigh ElaborationLow Elaboration
Personal relevanceHigh (topic matters to them)Low (distant from self)
Need for cognitionHigh (enjoys thinking)Low (avoids effortful thought)
Prior knowledgeSufficient to evaluate argumentsInsufficient to engage deeply
Distraction levelLow (can focus)High (divided attention)
Time pressureLow (can deliberate)High (must decide quickly)

Step 2 — Select Persuasion Route

  • Central route (high elaboration): invest in strong, evidence-based arguments
  • Peripheral route (low elaboration): invest in heuristic cues and presentation
  • Mixed (moderate elaboration): use both strong arguments and peripheral cues

Step 3 — Design the Message

Central route elements:

  • Logical argument structure, data, evidence
  • Two-sided messaging (acknowledge counterarguments)
  • Strong argument quality (scrutiny-resistant claims)

Peripheral route elements:

  • Source credibility, attractiveness, likability
  • Social proof (endorsements, testimonials, popularity)
  • Message length and formatting cues
  • Emotional tone and narrative framing

Step 4 — Predict Attitude Outcomes

RouteDurabilityBehavior PredictionCounter-Persuasion Resistance
CentralHighStrongHigh
PeripheralLowWeakLow

Output Format

## ELM Persuasion Strategy: [Context]

### Audience Elaboration Assessment
| Segment | Motivation | Ability | Elaboration Level |
|---------|-----------|---------|-------------------|
| [segment] | [High/Low] | [High/Low] | [High/Moderate/Low] |

### Route Selection: [Central / Peripheral / Mixed]

### Message Design
- Primary arguments: [if central route]
- Peripheral cues: [if peripheral route]
- Source selection: [credibility/attractiveness rationale]

### Predicted Outcomes
- Attitude durability: [High/Medium/Low]
- Behavioral impact: [Strong/Moderate/Weak]
- Counter-persuasion vulnerability: [High/Medium/Low]

### Risk Mitigation
- [What happens if audience elaboration is misjudged]

Gotchas

  • The same variable can play multiple roles: source expertise can be a peripheral cue (low elaboration) or bias argument processing direction (high elaboration)
  • Strong arguments under central processing can backfire if they are perceived as manipulative — reactance theory applies
  • Peripheral cues are not inherently inferior; in low-stakes decisions, they are efficient and adaptive
  • ELM assumes a relatively cognitive model of persuasion; purely visceral or embodied persuasion is not well-captured
  • Cultural differences affect what counts as a strong argument vs. a peripheral cue (e.g., authority weight varies by culture)
  • Repeated exposure can shift processing from peripheral to central as familiarity grows — one-shot message design is insufficient for campaigns

References

  • Petty, R. E. & Cacioppo, J. T. (1986). *Communication and persuasion: central and peripheral routes to attitude change*. Springer-Verlag.
  • Petty, R. E. & Wegener, D. T. (1999). The elaboration likelihood model: current status and controversies. In S. Chaiken & Y. Trope (Eds.), *Dual-process theories in social psychology* (pp. 37-72). Guilford Press.
  • Petty, R. E., Brinol, P. & Priester, J. R. (2009). Mass media attitude change. In J. Bryant & M. B. Oliver (Eds.), *Media effects: advances in theory and research* (3rd ed., pp. 125-164). Routledge.

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能力 4

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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

35.84%
按下载量换算43

Claude

32.36%
按下载量换算39

Cursor

19.92%
按下载量换算24

Gemini CLI

9.28%
按下载量换算11

安全审计

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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

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