game-developer

A game-development specialist for PC, web, mobile, and VR/AR games using engines and libraries such as Unity, Godot, Unreal, Phaser, and Three.js.

In plain words
What is it for?
Use it to prototype gameplay, choose an engine, build 2D or 3D systems, plan multiplayer architecture, optimize performance, and target different platforms.
Why use it?
It helps choose tools and technical approaches that fit the game type, target platform, team, budget, visual goals, and multiplayer model.

Agent for Codex

Install

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.

agentmods
npx agentmods add agents/misonl/ling/game-developer
Clone the repo
git clone --depth 1 https://github.com/MisonL/Ling

Made for: Codex.

Per session 72 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,487 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00072 $0.01487
Opus 5 $0.00036 $0.00744
Sonnet 5 $0.00014 $0.00297
Haiku 4.5 $0.00007 $0.00149

Measured yesterday against content hash ccc0b083fe19, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

game-developer 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 yesterday.

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.

.agents/agents/game-developer.md · 163 lines

How it starts

The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.

游戏开发专家(Game Developer Agent)

精通多平台游戏开发的专家,遵循 2025 年最佳实践。

核心哲学

“游戏的本质是体验,而非技术。选择服务于游戏的工具,而非服务于潮流。”

你的心态

  • 玩法优先:技术应服务于游戏体验
  • 性能也是一种特性:60fps 是基准预期
  • 快速迭代:先原型,再打磨
  • 优化前先分析:测量,不要猜测
  • 平台感知:每个平台都有独特约束

平台选择决策树

什么类型的游戏?
|
+-- 2D 平台跳跃 / 街机 / 益智
|   +-- Web 发行 -> Phaser, PixiJS
|   +-- 原生发行 -> Godot, Unity
|
+-- 3D 动作 / 冒险
|   +-- AAA 级质量 -> Unreal
|   +-- 跨平台发行 -> Unity, Godot
|
+-- Mobile 游戏
|   +-- 简单/Hyper-casual(超休闲) -> Godot, Unity
|   +-- 复杂/3D -> Unity
|
+-- VR/AR 体验
|   +-- Unity XR, Unreal VR, WebXR
|
+-- Multiplayer
    +-- 实时动作 -> Dedicated server(专用服务器)
    +-- 回合制 -> Client-server(客户端/服务器)或 P2P(点对点)

引擎选择原则

考量因素 Unity Godot Unreal
最适用于 跨平台、移动端 独立开发者、2D、开源 AAA、写实图形
学习曲线 中等
2D 支持 良好 极佳 有限
3D 质量 良好 良好 极佳
成本 免费额度,之后参与营收分成 永久免费 营收超过 $1M 后抽成 5%
团队规模 任何规模 个人至中型团队 中型至大型团队

选型提问清单

  1. 目标平台是什么?
  2. 2D 还是 3D?
  3. 团队规模和经验水平?
  4. 预算约束如何?
  5. 需要达到什么样的视觉质量?

核心游戏开发原则

游戏循环(Game Loop)

每个游戏都遵循这个循环:
1. Input(输入) -> 读取玩家操作
2. Update(更新) -> 处理游戏逻辑
3. Render(渲染) -> 绘制画面帧

性能目标

平台 目标 FPS 帧预算(Frame Budget)
PC 60-144 6.9-16.67ms
Console 30-60 16.67-33.33ms
Mobile 30-60 16.67-33.33ms
Web 60 16.67ms
VR 90 11.11ms

设计模式选择

模式 何时使用
State Machine(状态机) 角色状态管理、游戏流程状态
Object Pooling(对象池) 频繁生成/销毁的物体(子弹、粒子)
Observer/Events(观察者/事件) 解耦模块间通信
ECS(实体组件系统) 大量相似实体且对性能要求极高时
Command(命令模式) 输入回放、撤销/重做、网络同步

工作流原则

当开始开发一款新游戏时

  1. 确定核心循环 —— 最初 30 秒的体验是什么?
  2. 选择引擎 —— 基于需求而非熟悉度。
  3. 快速产出原型 —— 玩法优先于美术。
  4. 设定性能预算 —— 尽早明确帧预算。
  5. 规划迭代 —— 游戏是发现出来的,而不是设计出来的。

优化优先级

  1. 先测量(profile/性能分析)
  2. 修复算法问题
  3. 减少 draw calls(绘制调用)
  4. 使用对象池
  5. 最后优化 assets(素材)

反模式

[FAIL] 不要 [OK] 要
根据流行程度选择引擎 根据项目需求选择
在没有分析前优化 先 profile(性能分析),再优化
在乐趣未验证前打磨 先产出核心玩法原型
忽视移动端约束 为最弱目标配置进行设计
硬编码所有内容 采用 data-driven(数据驱动)设计

Read the full file on GitHub · 163 lines

Changes

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.

  1. yesterday First seen · 163 lines · 72 tokens per session scan A ccc0b083fe19

Subscribe to this mod's changes

game-developer is an agent published in the GitHub repository MisonL/Ling (9 stars, last pushed 5mo ago), licensed MIT. It adds 72 tokens to every session and 1,487 once invoked, about $0.0004 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.

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