Token导航 LogoToken导航TokenDH.com
研究检索只读clawhub未标认证来源可访问clear审计通过

game-design-peak-end-audit游戏设计巅峰期结束审核

Agent Skill

用于辅助界面设计、视觉规范、排版、配色、布局和交互体验优化。它适合让 Agent 根据产品场景整理页面结构、生成 UI 方案、检查视觉一致性或改进组件层级。使用时需要结合现有品牌、设计系统和用户任务,不应只堆装饰元素;涉及真实页面改动时,应通过截图或浏览器预览检查文本溢出、对齐和响应式表现。

总安装

1,503

周安装

62

GitHub Stars

公开资料未说明

下载量

491
OpenClaw

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

MIT-0

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:game-design-peak-end-audit(游戏设计巅峰期结束审核)
来源仓库:https://github.com/stanestane/game-design-peak-end-audit
安装命令:
openclaw skills install game-design-peak-end-audit
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install game-design-peak-end-audit

简介

从峰终规则视角评估玩家体验的关键节点与收尾设计。

  • 适用于关卡、会话、奖励循环或回归流程的优化分析。
  • 识别情绪高点与结尾印象,提出增强记忆点的设计建议。
  • 结果为心理学驱动的分析,需结合具体玩法实现调整。
  • game-design-peak-end-audit 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
game-design-peak-end-audit
description
Audit a game, feature, session, level, event, onboarding flow, reward sequence, or return-player experience through the lens of the peak-end rule. Use when evaluating which moments players are most likely to remember, whether the emotional high points are strong enough, whether endings, exits, and completions leave the right aftertaste, or why an experience with decent average quality is still remembered as flat, frustrating, or unexpectedly great.

Game Design Peak-End Audit

Audit a design by asking which moments dominate memory and whether the experience earns the memory it leaves behind.

Use this skill when the important question is not just how the experience feels moment to moment, but how players will remember it afterward. The goal is to identify the emotional peaks, the ending shape, and the aftertaste that determine whether a session, level, feature, event, or sequence is remembered as exciting, exhausting, disappointing, triumphant, or forgettable.

Read references/family-conventions.md when you want the shared style, prioritization, and diagnosis rules for this game-design skill family. Read references/output-patterns.md when you want the preferred recommendation and minimal-fix structure.

Core principle

Players do not remember an experience as an average of every second they lived through.

They disproportionately remember:

  • the strongest emotional peaks
  • the ending
  • the story those moments imply afterward

That means a design with decent average quality can still be remembered badly if:

  • its emotional peaks are weak
  • its worst spike dominates memory
  • its ending sours the whole experience

And a rougher experience can still land well if:

  • its peak is strong and meaningful
  • its ending resolves cleanly
  • the memory closes with satisfaction, momentum, relief, or anticipation

What to produce

Generate:

  1. Peak-end profile - the strongest peaks, the ending shape, and the likely remembered summary
  2. Memory dominance diagnosis - which moments will overshadow the rest of the experience
  3. Ending-quality diagnosis - whether the closing moments strengthen or poison the memory
  4. Experience-shape risks - where the design is setting itself up to be remembered for the wrong thing
  5. Design actions - what to amplify, soften, reorder, frame, or close differently

Process

1. Define the audit target

Clarify:

  • what exact experience slice is being audited
  • where it begins and ends from the player's point of view
  • what player segment matters most

Write:

  • Audit target
  • Experience window
  • Primary player segment

2. Map the emotional shape over time

Break the experience into phases and ask:

  • where does emotional intensity rise?
  • where does it sag?
  • where does frustration spike?
  • where does satisfaction spike?
  • what is the final emotional note?

Do not settle for generic labels like "good pacing." Name the moments.

3. Identify positive and negative peaks

Look for the moments most likely to become the remembered highlight or remembered wound.

Examples:

  • first big win
  • dramatic comeback
  • painful difficulty spike
  • surprising reveal
  • humiliating loss
  • beautiful payoff moment
  • reward opening high
  • last-minute collapse

For each peak, ask:

  • how intense is it?
  • how clear is it?
  • does it support the intended fantasy?
  • is it the thing you actually want remembered?

4. Audit the ending

The ending may be:

  • session close
  • boss resolution
  • reward-claim sequence
  • level completion
  • event wrap-up
  • return-to-hub state
  • defeat screen and immediate aftermath

Ask:

  • what emotion does the player leave with?
  • does the ending frame the previous experience positively or negatively?
  • does it create closure, momentum, relief, excitement, emptiness, or irritation?
  • does it respect the effort that preceded it?

5. Determine the likely remembered story

Translate the shape into the sentence the player is likely to remember later.

Examples:

  • "That run had one incredible clutch ending"
  • "The fight was fine, but the ending reward felt pathetic"
  • "That event was mostly chores and then it just sort of stopped"
  • "The level was hard, but the payoff made it worth it"

If the remembered sentence is bad, average quality elsewhere may not save the experience.

6. Diagnose peak-end failure patterns

Look for:

  • strong negative spike dominating weak positives
  • flat experience with no memorable high
  • rewarding middle but deflating ending
  • emotionally strong ending attached to a fantasy mismatch
  • punishing final note after meaningful progress
  • reward ceremony too weak for the effort invested
  • admin or UI sludge contaminating the exit

7. Check segment differences

Ask whether:

  • new players experience the worst peak where veterans experience the best one
  • one audience sees relief while another sees boredom
  • the ending hits differently depending on skill, investment, or outcome state

8. Convert findings into design changes

For each issue, specify:

  • Peak-end problem
  • Why it will dominate memory
  • Suggested change
  • Expected effect on remembered experience

Examples:

  • strengthen the success payoff -> improves remembered value of the whole sequence
  • remove admin friction at the end -> prevents sour aftertaste
  • soften one brutal late spike -> stops the whole feature being remembered as hostile
  • reorder the reward reveal -> lets the ending carry more emotional weight

Response structure

Use this structure unless the user asks for something else:

Audit Target

  • ...

Peak-End Profile

  • ...

Positive Peaks

  • ...

Negative Peaks

  • ...

Ending Quality

  • ...

Likely Remembered Story

  • ...

Recommendations

  1. ...
  2. ...
  3. ...

Minimal Fix

  • ...

Fast mode

Use this quick pass when speed matters:

  • What is the strongest moment?
  • What is the worst moment?
  • What is the final emotional note?
  • Which of those will dominate memory?
  • What one change would most improve the remembered aftertaste?

Usage notes

This audit is especially useful for:

  • onboarding and first-session retention
  • boss fights and level endings
  • event wrap-ups
  • reward ceremonies and chest openings
  • return-player flows
  • session exits and stop points
  • defeat aftermaths
  • premium or high-effort experiences where memory quality really matters

Common patterns to watch for:

  • many systems are fine in aggregate but remembered badly because the ending is weak
  • a single ugly late frustration spike can poison an otherwise decent feature
  • reward structure often over-focuses on average yield and under-focuses on emotional ending quality
  • if the strongest remembered moment is the wrong one, the design has a memory-shape problem
  • closure and anticipation are both valid endings, but aimlessness is not

Working principle

The experience players remember is often not the one designers think they shipped.

Use this skill to identify which moments actually own the memory and whether that memory helps or hurts the design.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

74.16%
按下载量换算364

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills