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Agent Skill · 开发

game-design-perceived-randomness-audit(游戏设计感知随机性审核)

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-perceived-randomness-audit 是开发类 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,按安装量在开发类的 21,746 个 Skill 里排第 11,312。

总安装 1,836周安装 75GitHub Stars 公开资料未说明下载 594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-perceived-randomness-audit(游戏设计感知随机性审核)
来源仓库:https://github.com/stanestane/game-design-perceived-randomness-audit
安装命令:
openclaw skills install game-design-perceived-randomness-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-perceived-randomness-audit

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

分析玩家感知随机性的设计机制与心理影响。适用于战利品表、战斗系统或机会驱动玩法的评估。基于玩家行为预期判断公平性与惊喜感平衡。输出为设计建议,不保证完全符合个体玩家感受。game-design-perceived-randomness-audit 属于开发类 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-perceived-randomness-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-perceived-randomness-audit 怎么安装?

在终端执行 openclaw skills install game-design-perceived-randomness-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-perceived-randomness-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-perceived-randomness-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-perceived-randomness-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-perceived-randomness-audit 用的人多吗?

公开数据是累计安装 1,836 次,近一周安装 75 次,下载 594 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-perceived-randomness-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。
  • 需要处理开发类任务、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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
game-design-perceived-randomness-audit

游戏设计感知随机性审核 · 本页

1,836—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-perceived-randomness-audit
description
Audit a game feature, combat system, loot table, reward loop, procedural system, chance mechanic, or uncertainty-driven design by how players are likely to perceive its randomness. Use when you need to evaluate whether a system will feel fair, streaky, rigged, sabotaging, manipulable, or skill-undermining; when players may misread independent events as patterned; or when randomness may sit too close to player action and create frustration. Analyze expectation gaps, gambler's-fallacy-style reactions, hidden pattern-seeking, input-versus-output randomness, perceived fairness, exploit risk, and ways to reshape presentation or mechanics.

Game Design Perceived Randomness Audit

Audit not just the math of randomness, but the psychology of how players will read it.

Use this skill when the core question is not only "is this random system balanced?" but also "what story will players tell themselves about this randomness?" The goal is to catch places where independent events will be perceived as rigged patterns, where streaks will feel malicious, where uncertainty undermines competence, or where the game could use presentation or structure to make randomness feel fairer and more legible.

Read references/perception-patterns.md when you need the key psychology behind randomness perception. Read references/audit-checklist.md when you need a compact checklist of design risks and mitigation levers.

Core behavior

  • Keep the analysis practical, not statistical-for-its-own-sake.
  • Audit player perception, not only formal probability.
  • Distinguish actual randomness from perceived randomness.
  • Identify where players will imagine patterns, sabotage, or "the game knows what I need and withholds it" behavior.
  • Check whether randomness happens before the decision or between the decision and the outcome.
  • Treat randomness that interferes directly with player action as more dangerous than randomness that shapes the environment.
  • Look for streak pain, expectation mismatch, control illusions, and competence damage.
  • Recommend concrete fixes in system design, UX, communication, and presentation.

What to ask first

Prioritize these questions:

  1. What is the feature or random system?
  2. What outcomes are random?
  3. What does the player know before acting, and what is hidden until after acting?
  4. What player action is this randomness attached to?
  5. What does the player usually want in the moment when the random outcome occurs?
  6. How often does the randomness happen, and how visible are streaks?
  7. Is the system meant to feel exciting, fair, learnable, punishing, suspenseful, or skill-testing?
  8. Are there safeguards, pity rules, weighted drops, rerolls, previews, or player-choice layers already present?

If information is missing, infer cautiously and state the assumption.

What to diagnose

Quickly identify:

  • whether the player is likely to assume hidden patterns in independent outcomes
  • whether gambler's-fallacy-style expectations will make the system feel unfair after streaks
  • whether players will expect conditional fairness such as "I have not gotten X lately so I should get it soon"
  • whether randomness is input randomness, output randomness, or an unhealthy blur of both
  • whether the system damages the feeling of competence by making deliberate actions resolve unpredictably
  • whether players can meaningfully react to uncertainty using skill
  • whether the presentation helps players understand the randomness or makes it feel rigged
  • whether the mitigation method creates exploit risk or false promises

Response structure

Always organize the answer using this structure.

System Read

  • describe the random system in plain language
  • state what is actually random and what is not

Player Expectation Read

  • explain what players are likely to expect from the system
  • identify likely mistaken assumptions about balance, streak correction, fairness, or hidden intent

Perceived Fairness Risk

  • explain when the system is likely to feel fair, unfair, rigged, streaky, or malicious
  • highlight the most likely frustration story players will tell themselves

Randomness Placement

  • identify whether this is mostly input randomness or output randomness
  • explain whether randomness happens before the player chooses or after the player commits
  • say whether that placement supports or undermines skill expression

Competence and Agency Read

  • explain whether players can adapt to the uncertainty with skill
  • identify where randomness is likely to invalidate intentional play
  • note whether the system feels like a challenge to respond to or a sabotage of action

Risk Diagnosis

  • give the top 2 to 5 perception risks
  • include streak pain, hidden-intent suspicion, exploitability, or expectation mismatch where relevant

Recommendation

  • recommend specific design, UX, or messaging changes
  • separate must-fix from optional polish when useful

Common risk patterns

Raise these when relevant:

  • truly independent drops being perceived as "due"
  • players believing the game withholds exactly what they currently need
  • random combat resolution making skillful decisions feel invalidated
  • pity systems that help fairness but become exploitable if too legible
  • procedural/input randomness producing wildly different challenge levels with too little response time
  • opaque weighting creating conspiracy theories
  • visible streaks without framing, smoothing, or counterweights
  • systems that are mathematically fair over long runs but emotionally awful in short runs

Useful mitigation levers

Only recommend what fits the design intent.

  • move randomness earlier so the player reacts to it rather than being blindsided by it
  • reduce output randomness on high-stakes actions
  • add soft streak smoothing or pity protection
  • batch outcomes into more even distributions when fairness matters more than pure entropy
  • provide player choice among random options
  • expose clearer odds or stronger telegraphing
  • separate excitement randomness from core skill resolution
  • use presentation to give the player a clearer sense of authorship or controlled reveal
  • shift reward pools by context carefully, while watching for exploit loops

Strong use cases

This skill is especially useful for:

  • loot drops and chest rewards
  • gacha, pity, and streak-protection systems
  • crit chance, hit chance, dodge, and proc systems
  • random combat resolution
  • procedural encounter or room generation
  • event reward pools
  • roguelike runs with variable offer quality
  • randomness-heavy retention loops
  • any design where players say the system feels rigged

Weaker use cases

This skill is less useful for:

  • systems with no meaningful uncertainty
  • purely economic balance review without a perception question
  • broad game critique where randomness is not a major issue

Style guidance

  • Be concrete.
  • Do not hide behind statistics if the player-facing feeling is bad.
  • Do not assume mathematically fair means experientially fair.
  • If a system is perception-safe even though players may still complain sometimes, say that too.
  • If the right fix is presentation rather than probability tuning, say so.
  • If the right fix is to remove randomness from a player action entirely, say so directly.

Fast mode

Use this compressed flow when the user wants a quick answer:

  • what is random here
  • what will players assume about that randomness
  • will it feel fair or rigged
  • is the randomness before or after player choice
  • does it support skill or sabotage action
  • what should change first

Working principle

Players do not experience randomness as raw probability tables. They experience it as felt fairness, perceived intent, streak memory, and the degree to which their choices still seem to matter.

平台分布

OpenClaw

96.72%
按下载量换算575

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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