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Game Design Systematizing Empathizing Audit

作者 Stanislav Stankovic · GitHub ↗ · v1.0.0 · MIT-0
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在 OpenClaw 中安装
/install game-design-systematizing-empathizing-audit
功能描述
Evaluate a game, feature, system, UX flow, progression loop, live-ops mechanic, monetization surface, or design concept on two axes: systematizing and empath...
使用说明 (SKILL.md)

Game Design Systematizing Empathizing Audit

Position the design by how strongly it rewards system-thinking and how strongly it reflects emotional understanding of the player.

Use this skill to understand what kind of design a concept is becoming, what kind of people it is likely to appeal to, and what tradeoffs come with that positioning. The goal is not to force every concept toward an imagined ideal balance. The goal is to diagnose where the design sits, who that tends to work for, who may reject it, and whether that positioning feels intentional or accidental.

Read references/axis-definitions.md when judging the two axes. Read references/quadrant-reads.md when describing what the position tends to feel like in practice. Read references/persona-tendencies.md when mapping likely audience appeal and rejection.

What to produce

Produce:

  1. Design read - what the concept is trying to do
  2. Systematizing position - how strongly it emphasizes logic, structure, mastery, and model-building
  3. Empathizing position - how strongly it emphasizes emotional fit, player sensitivity, warmth, expression, and humane friction handling
  4. Positioning read - what this combination tends to feel like in practice
  5. Likely persona appeal - who is likely to enjoy or value this design
  6. Likely rejection pattern - who may bounce and why
  7. Practical consequences - what follows for onboarding, retention, monetization, community, or communication
  8. Recommendation - how to lean into, counterweight, clarify, or intentionally preserve the position

Process

1. Read the design at the player-experience level

Clarify:

  • what the concept wants the player to do
  • what kind of player experience it seems to prioritize
  • whether the design is asking for mastery, comfort, expression, optimization, social belonging, emotional immersion, or some combination

2. Judge the systematizing axis

Ask how strongly the design rewards:

  • understanding rules
  • building internal models
  • predicting outcomes
  • optimizing behavior
  • mastering systems
  • engaging with strategic structure

Look for evidence such as:

  • clear rule consistency
  • explicit feedback and cause-effect legibility
  • optimization depth
  • mastery curves
  • strategic planning pressure
  • meaningful structural complexity

3. Judge the empathizing axis

Ask how strongly the design reflects understanding of:

  • player emotional reality
  • frustration tolerance
  • comfort and dignity
  • expressive identity
  • emotional readability
  • social sensitivity
  • humane handling of failure, pressure, and confusion

Look for evidence such as:

  • emotionally considerate onboarding
  • softened or well-framed failure
  • expressive play support
  • social meaning beyond pure function
  • sensitivity to frustration and player mood
  • systems that feel humane rather than merely correct

4. Describe the resulting position

Do not rank the quadrant morally. Describe what this position tends to mean. For example:

  • precise but emotionally austere
  • warm and intuitive but structurally soft
  • deep and humane but demanding to execute well
  • underdefined and low-intent on both axes

5. Map likely persona appeal

Infer which player tendencies are likely to find this attractive. Possible personas include:

  • optimizer
  • systems thinker
  • mastery-seeker
  • competitive achiever
  • tinkerer
  • cozy comfort-seeker
  • expressive identity player
  • social harmony player
  • narrative-relational player
  • low-friction casual
  • routine habit player

Use these as practical shorthand, not rigid psychological categories.

6. Map likely rejection

Ask which players are likely to bounce and why. Common causes include:

  • too much cold optimization pressure
  • too little structural depth
  • not enough emotional cushioning
  • too much emotional softness for mastery-seekers
  • social sterility
  • unclear or weak challenge identity

7. Extract practical implications

Describe what this position tends to imply for:

  • onboarding
  • retention
  • monetization tolerance
  • community behavior
  • feature communication
  • audience targeting
  • risk of mismatch between fantasy and system

8. Recommend intentionality

End with a practical recommendation such as:

  • lean further into this audience fit
  • add a small counterweight on the weaker axis
  • stop pretending the design is for everyone
  • communicate the positioning more honestly
  • preserve the current direction because it matches the concept well
  • simplify a mismatch between emotional promise and structural reality

Response structure

Design Read

  • ...

Systematizing Position

  • ...

Empathizing Position

  • ...

Positioning Read

  • ...

Likely Persona Appeal

  • ...

Likely Rejection Pattern

  • ...

Practical Consequences

  • ...

Recommendation

  • ...

Fast mode

  • How logic-driven and mastery-oriented is this design?
  • How emotionally considerate and player-attuned is it?
  • What kind of player is most likely to love this?
  • What kind of player is most likely to bounce?
  • What is the most important consequence of that positioning?
  • Should the team lean in, counterbalance, or clarify?

Style rules

  • Do not assume one quadrant is best.
  • Do not confuse warmth with weakness.
  • Do not confuse rigor with quality.
  • Tie judgments to observable design consequences.
  • Use personas as tendencies, not as deterministic labels.
  • If the design is mismatched, say whether the mismatch feels intentional or accidental.

Working principle

A design does not appeal to everyone in the same way. This skill exists to clarify what kind of player the design is really speaking to, and what structural-emotional tradeoffs come with that choice.

安全使用建议
This is a low-risk, instruction-only skill that contains a sensible rubric for evaluating game designs. Before installing, consider: (1) any proprietary design documents you give the agent while using this skill will be processed by the agent—don't submit sensitive PII or secrets; (2) review the included reference files yourself to confirm the rubric aligns with your team's values and terminology; (3) monitor outputs for inadvertent leakage of confidential details if you paste real design assets into prompts. Otherwise, the skill's permissions and behavior are proportionate to its stated purpose.
功能分析
Type: OpenClaw Skill Name: game-design-systematizing-empathizing-audit Version: 1.0.0 The skill bundle is a framework for auditing game designs based on systematizing and empathizing axes. It consists of structured instructions (SKILL.md) and reference documents (references/*.md) that guide an AI agent through a qualitative analysis process. There is no executable code, no evidence of data exfiltration, and no malicious prompt injection attempts; the content is entirely aligned with its stated purpose of game design evaluation.
能力评估
Purpose & Capability
The name, description, and SKILL.md all describe a design-audit rubric. There are no required binaries, env vars, or external services that would be unexpected for this purpose.
Instruction Scope
Runtime instructions are limited to reading the included reference markdown files and producing a structured analysis. They do not instruct the agent to read arbitrary system files, access external endpoints, or exfiltrate data.
Install Mechanism
No install specification or code files beyond reference docs and the SKILL.md. Nothing will be downloaded or written to disk by an installer.
Credentials
The skill declares no environment variables, credentials, or config path requirements; its needs are proportional to its stated function as a text-based audit rubric.
Persistence & Privilege
always is false and there is no indication the skill modifies system or other-skill configuration. Autonomous model invocation is permitted by default but not combined with other concerning privileges.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install game-design-systematizing-empathizing-audit
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /game-design-systematizing-empathizing-audit 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release: positions game concepts on systematizing and empathizing axes, maps likely player appeal, and explains the consequences of that positioning.
元数据
Slug game-design-systematizing-empathizing-audit
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Game Design Systematizing Empathizing Audit 是什么?

Evaluate a game, feature, system, UX flow, progression loop, live-ops mechanic, monetization surface, or design concept on two axes: systematizing and empath... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 89 次。

如何安装 Game Design Systematizing Empathizing Audit?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install game-design-systematizing-empathizing-audit」即可一键安装,无需额外配置。

Game Design Systematizing Empathizing Audit 是免费的吗?

是的,Game Design Systematizing Empathizing Audit 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Game Design Systematizing Empathizing Audit 支持哪些平台?

Game Design Systematizing Empathizing Audit 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Game Design Systematizing Empathizing Audit?

由 Stanislav Stankovic(@stanestane)开发并维护,当前版本 v1.0.0。

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