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loss-aversion-psychology损失厌恶心理

Agent Skill

loss-aversion-psychology 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

612

周安装

25

GitHub Stars

239

下载量

198
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:loss-aversion-psychology(损失厌恶心理)
来源仓库:https://github.com/flpbalada/my-opencode-config
仓库路径:skills/loss-aversion-psychology
安装命令:
npx skills add https://github.com/flpbalada/my-opencode-config --skill loss-aversion-psychology
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 loss-aversion-psychology

简介

loss-aversion-psychology 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。

  • 适用于研究检索类任务,如信息搜集、资料筛选和线索整理,尤其适合需要自动化处理重复性搜索的场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和实际维护状态后再使用。
  • 使用前应检查是否会触发联网、命令执行或文件读写操作,避免在不安全环境中运行。
  • 建议结合原始 README 和仓库内容进一步核验具体用法和功能边界。

SKILL.md

Loss Aversion Psychology - Losses Loom Larger Than Gains

Loss Aversion is a cognitive bias discovered by Daniel Kahneman and Amos Tversky showing that people feel losses approximately twice as strongly as equivalent gains. This asymmetry profoundly influences decision-making and behavior.

When to Use This Skill

  • Designing retention and anti-churn features
  • Crafting pricing and upgrade messaging
  • Creating urgency in conversion funnels
  • Building streak and progress features
  • Writing copy for landing pages
  • Framing feature benefits

Core Concepts

The 2:1 Ratio

        Psychological Impact
              ^
              |         Gains
              |        /
              |      /
     +--------|----/---------> Value
              |  /
           Loss|/
              |
              |  The loss curve is ~2x steeper
              v

A $100 loss feels as bad as a $200 gain feels good.

Prospect Theory Framework

ConceptDescriptionExample
Reference PointCurrent state as baseline"You currently have X"
Loss FrameEmphasis on what could be lost"Don't lose your progress"
Gain FrameEmphasis on what could be gained"Get 50% more"
Endowment EffectValuing owned things higherFree trial creates ownership

When Loss Framing Works Best

SituationLoss Frame Effective?
High stakes decisionsYes
Preventing bad outcomesYes
Risk-averse audiencesYes
Building habitsYes
Low-involvement decisionsLess effective
Exploratory behaviorLess effective

Analysis Framework

Step 1: Identify Loss Opportunities

Map user journey for potential loss frames:

StageWhat User HasPotential Loss
TrialAccess to featuresLosing access
ActiveProgress/dataLosing progress
At-riskStreak/statusBreaking streak
ChurnedHistory/investmentLosing history

Step 2: Choose Frame Appropriately

Decision: Frame as loss or gain?

Consider:
├── User relationship stage
│   └── New users: Gains more welcoming
│   └── Existing users: Losses more motivating
├── Action reversibility
│   └── Reversible: Lighter touch OK
│   └── Irreversible: Loss frame powerful
└── Ethical considerations
    └── Does this genuinely help the user?

Step 3: Implement Ethically

ApproachEthicalManipulative
"Your streak will reset"Honest reminderManufactured guilt
"Unused credits expire"Clear policyHidden deadline
"Limited time offer"Genuine scarcityFake urgency

Output Template

## Loss Aversion Analysis

**Feature/Message:** [Name] **Date:** [Date]

### Current Framing

**As gain:** [Current copy/design] **User response:** [Current metrics]

### Loss Frame Opportunity

**What user has:** [Established value] **Potential loss:** [What could be lost]
**Loss frame version:** [Proposed copy/design]

### Ethical Check

- [ ] User genuinely benefits from taking action
- [ ] Loss is real, not manufactured
- [ ] Messaging is honest and transparent
- [ ] Would we be comfortable if users knew the psychology?

### Implementation Plan

| Element  | Current   | Proposed | Expected Impact |
| -------- | --------- | -------- | --------------- |
| [Copy 1] | [Text]    | [Text]   | [Estimate]      |
| [Design] | [Current] | [Change] | [Estimate]      |

Real-World Examples

Example 1: Duolingo Streaks

Mechanism: Users build daily learning streaks Loss frame: "Don't lose your 47-day streak!" Psychology:

  • Streak = accumulated investment (endowment)
  • Breaking it = losing days of effort
  • Effect: 2x stronger than "Build a 48-day streak!"

Example 2: LinkedIn Profile Completion

Gain frame: "Complete your profile to get more views" Loss frame: "You're missing out on 40% more profile views"

The loss frame outperforms because it highlights what you're currently losing.

Example 3: Trial Expiration

Weak: "Your trial ends tomorrow" Strong: "Tomorrow you'll lose access to:

  • 47 saved projects
  • 12 team members
  • All your custom settings"

Making the loss concrete and specific amplifies the effect.

Ethical Guidelines

Do

  • Use loss framing for genuinely beneficial actions
  • Be honest about what's at stake
  • Give users real control and options
  • Balance loss frames with positive experiences
  • Test that users feel good after taking action

Avoid

  • Manufacturing fake urgency or scarcity
  • Guilt-tripping for engagement metrics
  • Hiding information to create loss anxiety
  • Using loss aversion on vulnerable users
  • Dark patterns that exploit psychology

The Ethics Test

Ask: "If users knew exactly how this works psychologically, would they:

  1. Thank us for the helpful reminder?
  2. Feel manipulated and resentful?"

If (2), reconsider the approach.

Best Practices

Effective Loss Messaging

ElementExample
Specific"Lose your 23 saved items" not "Lose your data"
Immediate"Expires tonight" not "Expires soon"
Personal"Your progress" not "Progress"
RecoverableShow how to prevent the loss

Timing Matters

TimingEffectiveness
Too earlyFeels irrelevant, ignored
Just rightMotivates action
Too lateCreates resentment
After lossRecovery opportunity

Integration with Other Methods

MethodCombined Use
Hooked ModelInvestment phase creates loss potential
Fogg Behavior ModelLoss increases motivation
Cognitive BiasesCombine with other biases carefully
Progressive DisclosureReveal loss implications gradually

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

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能力 2

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能力 3

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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

33.62%
按下载量换算67

Claude

32.76%
按下载量换算65

Cursor

18.97%
按下载量换算38

Gemini CLI

8.96%
按下载量换算18

安全审计

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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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