Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/misonl/ling/game-designnpx skills add MisonL/Ling --skill game-designgit clone --depth 1 https://github.com/MisonL/LingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/misonl/ling/game-design)<a href="https://agentmods.dev/skills/misonl/ling/game-design"><img src="https://agentmods.dev/badge/skills/misonl/ling/game-design.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00023 | $0.00867 |
| Opus 5 | $0.00012 | $0.00434 |
| Sonnet 5 | $0.00005 | $0.00173 |
| Haiku 4.5 | $0.00002 | $0.00087 |
Grade A, and why
game-design scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
游戏设计原则
让游戏更有吸引力的设计思维。
1. 核心循环设计
30 秒测试
每个游戏都需要一个有趣的 30 秒循环：
1. ACTION -> 玩家做某事
2. FEEDBACK -> 游戏响应
3. REWARD -> 玩家感觉良好
4. REPEAT
循环示例
| 类型 | 核心循环 |
|---|---|
| Platformer（平台） | 跑 -> 跳 -> 落地 -> 收集 |
| Shooter（射击） | 瞄准 -> 射击 -> 击杀 -> 拾取 |
| Puzzle（解谜） | 观察 -> 思考 -> 解开 -> 前进 |
| RPG（角色扮演） | 探索 -> 战斗 -> 升级 -> 装备 |
2. 游戏设计文档（GDD）
核心部分
| 部分 | 内容 |
|---|---|
| Pitch | 一句话描述 |
| Core Loop | 30 秒玩法 |
| Mechanics | 系统如何工作 |
| Progression | 玩家如何前进 |
| Art Style | 视觉方向 |
| Audio | 声音方向 |
原则
- 保持它是鲜活的（定期更新）
- 视觉辅助交流
- 少即是多（从小开始）
3. 玩家心理学
动机类型
| 类型 | 驱动力 |
|---|---|
| Achiever（成就者） | 目标、完成度 |
| Explorer（探索者） | 发现、秘密 |
| Socializer（社交者） | 互动、社区 |
| Killer（杀手） | 竞争、支配 |
奖励计划
| 计划 | 效果 | 用途 |
|---|---|---|
| Fixed（固定） | 可预测 | 里程碑奖励 |
| Variable（可变） | 易上瘾 | 掉落战利品 |
| Ratio（比率） | 基于努力 | 刷刷刷游戏 |
4. 难度平衡
心流状态
太难 -> 沮丧 -> 退出
太易 -> 无聊 -> 退出
刚刚好 -> 心流 -> 投入
平衡策略
| 策略 | 如何做 |
|---|---|
| Dynamic（动态） | 适应玩家技能 |
| Selection（选择） | 让玩家选择 |
| Accessibility（无障碍） | 为所有人提供选项 |
5. 进度设计
进度类型
| 类型 | 示例 |
|---|---|
| Skill（技能） | 玩家变得更好 |
| Power（力量） | 角色变得更强 |
| Content（内容） | 新区域解锁 |
| Story（故事） | 叙事推进 |
节奏原则
- 早期胜利（快速钩住）
- 逐渐增加挑战
- 强度之间的休息节拍
- 有意义的选择
6. 反模式
| [FAIL] 禁止 | [OK] 推荐 |
|---|---|
| 孤立设计 | 持续进行游戏测试 |
| 有趣之前先打磨 | 首先制作原型 |
| 强制一种玩法 | 允许玩家表达 |
| 过度惩罚 | 奖励进步 |
记住： 乐趣是通过迭代发现的，而不是在纸上设计出来的。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 130 lines · 23 tokens per session scan A 0ffc13944146
game-design is a skill published in the GitHub repository MisonL/Ling (8 stars, last pushed 5mo ago), licensed MIT. It adds 23 tokens to every session and 867 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…