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Agent Skill · 研究检索

game-design-failure-loop-audit(游戏设计失败循环审核)

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-failure-loop-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 38,052。

总安装 1,623周安装 69GitHub Stars 公开资料未说明下载 569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-failure-loop-audit(游戏设计失败循环审核)
来源仓库:https://github.com/stanestane/game-design-failure-loop-audit
安装命令:
openclaw skills install game-design-failure-loop-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-failure-loop-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。

game-design-failure-loop-audit 深入分析失败机制对玩家心理的影响路径。识别挫败源并建议缓解策略改善负面体验累积效应。特别适用于 Roguelite 类游戏的高难度挑战平衡优化。集成于 clawhub 生态,支持快速嵌入现有审核流程。必须基于真实玩家行为日志才能得出可靠结论。

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-failure-loop-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-failure-loop-audit 怎么安装?

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

公开数据是累计安装 1,623 次,近一周安装 69 次,下载 569 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-failure-loop-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-failure-loop-audit

游戏设计失败循环审核 · 本页

1,623—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-failure-loop-audit
description
Audit a game, feature, encounter structure, roguelite run, puzzle sequence, onboarding path, or progression gate through the lens of its failure loop: what happens after the player fails, what they learn, what they lose, and why they would or would not try again. Use when diagnosing whether failure teaches, motivates, stalls, humiliates, exhausts, or ejects the player, or when tuning punishment, retry structure, and post-failure recovery.

Game Design Failure Loop Audit

Audit what the game does after the player fails, and whether that loop produces learning, motivation, and retry energy instead of confusion, resentment, or collapse.

Use this skill when a design includes repeated failure states and the real question is not merely "is failure possible" but "what does failure do to the player over time?" The goal is to evaluate what the player loses, what they understand, what they retain, how fast they re-enter, and whether the failure loop makes them want another attempt.

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

A good failure loop does not just punish. It converts defeat into renewed intention.

A strong failure loop usually provides:

  • readable cause of failure
  • meaningful lesson or hypothesis for next attempt
  • tolerable cost relative to clarity
  • manageable re-entry friction
  • emotional permission to try again

A bad failure loop produces:

  • confusion without learning
  • punishment without agency
  • downtime without anticipation
  • repetition without adaptation
  • shame, fatigue, or helplessness

What to produce

Generate:

  1. Failure loop summary - the current structure from failure to re-entry
  2. Loss-learning-retry analysis - what the player loses, learns, and does next
  3. Friction diagnosis - where punishment, delay, opacity, or humiliation break the loop
  4. Motivation diagnosis - why players will retry, hesitate, or quit
  5. Design implications - what to tune in cost, clarity, pacing, recovery, or support

Process

1. Define the audit target

Clarify:

  • what exact failure state or loop is being audited
  • what player segment is most affected
  • whether the focus is early churn, midgame frustration, endgame mastery, or broad retention

Write:

  • Audit target
  • Player segment
  • Failure context

2. Reconstruct the full failure loop

Map the sequence:

  • fail
  • receive feedback
  • pay consequence
  • recover or reset
  • prepare again
  • retry or abandon

Ask:

  • What happens immediately after failure?
  • What is the player told or shown?
  • What is lost?
  • What remains?
  • How long until the next meaningful attempt?
  • What emotional state is the player likely in by then?

3. Audit loss, learning, and retention

Evaluate:

  • what resources, progress, time, status, or opportunity are lost
  • what information or understanding is gained
  • what progression, meta-progress, or unlocked knowledge is retained
  • whether the player can form a better next-attempt plan

Use this format:

DimensionCurrent stateEffect on retry motivation
Loss......
Learning......
Retention......
Re-entry speed......

4. Diagnose re-entry friction

Look for:

  • long dead time before the next real attempt
  • tedious setup or replay of solved content
  • unclear recovery path
  • punishment that exceeds the educational value of the failure
  • restart structures that encourage resignation rather than iteration

Ask:

  • Is the retry fast enough?
  • Is the player replaying meaningful challenge or just chores?
  • Does the recovery path preserve tension or drain it?

5. Diagnose emotional effect

Check whether the failure loop tends to create:

  • determination
  • curiosity
  • annoyance
  • humiliation
  • dread
  • learned helplessness
  • numb repetition

Name the most likely felt response and why.

6. Check fairness and attribution inside failure

Ask:

  • does the player understand why they failed?
  • does the punishment feel deserved?
  • does the loop preserve a sense of control?
  • does repeated failure harden into external blame?

If needed, call out connections to attribution, flow, or fairness problems without fully re-running those audits.

7. Identify failure-loop archetypes

Useful patterns include:

  • learning loop - fast retry, high clarity, strong adaptation
  • grindback loop - too much rebuild between real attempts
  • shame loop - public or ego-threatening failure with weak learning
  • attrition loop - death by repeated low-grade exhaustion
  • helpless loop - repeated failure without better hypotheses
  • mastery loop - hard punishment justified by high clarity and strong growth

Classify the current loop and say whether that shape suits the game.

8. Convert findings into design changes

For each issue, specify:

  • Failure-loop problem
  • Why it hurts retry willingness
  • Suggested change
  • Expected effect on learning and motivation

Examples:

  • unclear death cause -> improve post-failure feedback and telegraphing
  • long rebuild before retry -> shorten setup or preserve more state
  • punishment too severe for early game -> soften cost during learning phases
  • nothing learned from failure -> increase legibility or give comparative feedback

Response structure

Use this structure unless the user asks for something else:

Audit Target

  • ...

Failure Loop Summary

  • ...

Loss, Learning, and Retention

  • ...

Re-entry Friction

  • ...

Emotional Effect

  • ...

Failure Loop Type

  • ...

Recommendations

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

Minimal Fix

  • ...

Fast mode

Use this quick pass when speed matters:

  • What happens right after failure?
  • What does the player lose?
  • What do they learn?
  • How fast can they make a better next attempt?
  • What one change would most improve retry willingness?

Usage notes

This audit is especially useful for:

  • boss retries
  • roguelite runs
  • puzzle resets
  • stealth mission failure
  • onboarding deaths
  • challenge events
  • progression gates
  • harsh economy setbacks
  • difficult action-combat loops

Common patterns to watch for:

  • many games punish more than they teach
  • retry friction is often more damaging than raw difficulty
  • harsh failure can work if clarity and re-entry speed are strong
  • meta-progression can rescue a hard failure loop, but cannot fully compensate for opaque failure
  • if the player dreads the retry more than the challenge itself, the loop is probably broken

Working principle

Failure should create a better next attempt, not just a worse mood.

Use this skill to test whether the design turns defeat into momentum or just burns player trust and energy.

平台分布

OpenClaw

70.5%
按下载量换算401

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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