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Agent Skill · 研究检索

game-design-prototyping-companion(游戏设计原型伴侣)

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-prototyping-companion 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 30,479。

总安装 2,869周安装 122GitHub Stars 公开资料未说明下载 1,005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
1.0.1

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-prototyping-companion(游戏设计原型伴侣)
来源仓库:https://github.com/stanestane/game-design-prototyping-companion
安装命令:
openclaw skills install game-design-prototyping-companion
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-prototyping-companion

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。

跟踪原型创意、分支结果与实验路径的可视化管理。适合多版本迭代或创意发散阶段的记录支持。可选择生成 SVG 可视化辅助理解进展。输出为内部管理工具,不直接生成可玩原型。game-design-prototyping-companion 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-prototyping-companion;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-prototyping-companion 怎么安装?

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

game-design-prototyping-companion 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-prototyping-companion 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-prototyping-companion 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-prototyping-companion 用的人多吗?

公开数据是累计安装 2,869 次,近一周安装 122 次,下载 1,005 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-prototyping-companion 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-prototyping-companion

游戏设计原型伴侣 · 本页

2,869—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-prototyping-companion
description
Track game design prototype ideas, branching outcomes, dead ends, baselines, and next experiments, and optionally generate a simple SVG visualization of prototype evolution. Use when a prototype leads to several possible follow-up paths, when the team needs to backtrack without losing learned branches, when exploring unknowns through multiple iterations, or when you want both a written prototype log and a branch map of how the concept evolved over time.

Game Design Prototyping Companion

Track prototype evolution, not just prototype results.

Use this skill when prototyping produces branching decisions, dead ends, alternative paths, and backtracking. The aim is to preserve learning structure: what was tested, what was learned, what branch it created, which path was followed, and which paths remain available to revisit.

This skill can also generate a simple SVG branch map from a lightweight text or JSON structure.

What to produce

Generate one or more of these outputs:

  1. Prototype log - what was tested and why
  2. Branch record - what paths emerged from the result
  3. Decision state - which branch is current, parked, dead, baseline, or promising
  4. Backtrack notes - what can be revisited later and under what condition
  5. SVG branch map - a visual map of prototype evolution

Core principle

A prototype is not just a yes/no answer. It often creates a tree:

  • one branch becomes the current path
  • another becomes a dead end
  • another becomes a parked idea worth revisiting later
  • another reveals a stronger question than the original one

The skill should preserve that tree.

Workflow

1. Define the prototype node

For each prototype node, record:

  • Node ID
  • Prototype name
  • Question being tested
  • What was built or simulated
  • What was learned
  • Result state

Use result states such as:

  • baseline
  • promising
  • branch trigger
  • dead end
  • parked
  • production candidate

2. Record branch options

When a prototype produces several next moves, capture each branch explicitly.

For each branch, record:

  • Branch ID
  • Parent node
  • New idea or variation
  • Reason it exists
  • Current status

3. Mark the chosen path without deleting the others

Do not treat the selected branch as the only meaningful output. Preserve:

  • abandoned paths
  • deferred paths
  • weird side paths
  • stronger substitute ideas revealed by the test

4. Add backtrack logic

If a branch is not chosen now, record:

  • what would justify revisiting it
  • what blocker currently prevents it
  • what later discovery might make it relevant again

5. Generate a visual map when useful

Use scripts/branch_map_svg.py to render a simple SVG from a branch-map JSON file.

Read:

  • references/branch-map-format.md for the input structure
  • references/example-branch-map.json for an example

Response structure

Use this structure unless the user asks for something else:

Prototype Node

  • Node ID: ...
  • Question: ...
  • Built / simulated: ...
  • Learned: ...
  • State: ...

Branches Created

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

Current Chosen Path

  • ...

Parked / Revisit Later

  • ...

Suggested Next Prototype

  • ...

Visualization workflow

When the user wants a visual branch map:

  1. write the branch data to JSON using the format in references/branch-map-format.md
  2. run scripts/branch_map_svg.py <input.json> <output.svg>
  3. return the SVG path and summarize what the map shows

Style rules

  • Preserve branching history.
  • Prefer explicit node IDs over vague prose.
  • Distinguish clearly between what was learned and what was merely assumed.
  • Do not erase dead ends; label them.
  • Do not confuse the current path with the best possible path forever.

References

  • references/branch-map-format.md for the JSON structure
  • references/example-branch-map.json for a starter example
  • references/state-labels.md for recommended branch/node labels

Working principle

Prototype trees are design memory. If you only remember the path you chose, you lose the intelligence of the paths you rejected.

平台分布

OpenClaw

88.93%
按下载量换算894

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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