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Agent Skill · 研究检索

game-design-peak-end-audit(游戏设计巅峰期结束审核)

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。

game-design-peak-end-audit 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,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 38,861。

总安装 1,503周安装 62GitHub Stars 公开资料未说明下载 491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
MIT-0
版本
1.0.0

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

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。

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。

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 OpenClaw;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 openclaw skills install game-design-peak-end-audit;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

game-design-peak-end-audit 怎么安装?

在终端执行 openclaw skills install game-design-peak-end-audit。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

game-design-peak-end-audit 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

game-design-peak-end-audit 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。

game-design-peak-end-audit 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

game-design-peak-end-audit 用的人多吗?

公开数据是累计安装 1,503 次,近一周安装 62 次,下载 491 次。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。

game-design-peak-end-audit 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 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

  • 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 OpenClaw,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 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
game-design-peak-end-audit

游戏设计巅峰期结束审核 · 本页

1,503—只读MIT-0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

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.

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。目前只有一个公开来源,建议同时核对源仓库。

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