godot-mcp: Skill for Claude Code

.claude/skills/godot-skills/sub-skills/godot-performance-optimizer/SKILL.md

godot-performance-optimizer is a skill for Claude Code from hhhh124hhhh/godot-mcp. It costs 42 tokens per session (5,665 once invoked), scanned A, original, MIT.

A skill for improving the performance and device compatibility of games made with Godot, a game-development engine.

In plain words
What is it for?
Use it for resolution and DPI adaptation, frame-rate and memory analysis, rendering improvements, resource loading, and platform-specific optimization.
Why use it?
It helps investigate slow frame rates, high memory use, rendering bottlenecks, and layouts that break on different screens.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is hhhh124hhhh/godot-mcp's own configuration. It tells Claude Code how to work on godot-mcp itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything godot-mcp configures →

Reuse

Borrowing it

Nothing to install: this file belongs to hhhh124hhhh/godot-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/hhhh124hhhh/godot-mcp/main/.claude/skills/godot-skills/sub-skills/godot-performance-optimizer/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/hhhh124hhhh/godot-mcp

Made for: Claude Code.

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README.md
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Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,665 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00042 $0.05665
Opus 5 $0.00021 $0.02832
Sonnet 5 $0.00008 $0.01133
Haiku 4.5 $0.00004 $0.00566

Measured 9d ago against content hash fdef3b8fecd5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

godot-performance-optimizer 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 9d 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.

.claude/skills/godot-skills/sub-skills/godot-performance-optimizer/SKILL.md · 722 lines

How it starts

The opening of the file, as written. The whole thing — 722 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Godot 性能优化专家技能

技能概述

godot-performance-optimizer 是专门用于 Godot 游戏性能优化和平台适配的智能化技能,能够根据用户的自然语言描述自动完成分辨率适配、性能分析、内存优化、帧率提升、平台适配等全面的性能优化工作。

核心功能

📱 分辨率适配系统

  • 多分辨率支持:自动适配不同屏幕分辨率和比例
  • DPI缩放:智能DPI检测和UI缩放
  • 横竖屏切换:动态横竖屏布局适配
  • 平台特定优化:针对不同平台的显示优化

⚡ 性能分析工具

  • 帧率监控:实时FPS监控和分析
  • 内存分析:内存使用情况追踪和优化建议
  • 渲染分析:Draw call、Overdraw等渲染性能分析
  • 瓶颈检测:自动检测性能瓶颈和热点代码

🧠 内存优化系统

  • 对象池管理:智能对象池和内存回收
  • 资源压缩:纹理、音频等资源的智能压缩
  • 垃圾回收优化:GC策略优化和内存碎片整理
  • 流式加载:大型资源的流式加载和卸载

🎮 渲染优化

  • 批处理优化:减少Draw call和渲染批次
  • LOD系统:细节层次距离优化
  • 遮挡剔除:智能遮挡剔除和视锥剔除
  • 着色器优化:GPU着色器性能优化

使用方法

分辨率适配优化

用户: "我的游戏在不同手机上显示不正常,需要适配各种屏幕尺寸"
系统: 自动实现:
- 检测目标设备分辨率范围
- 设计响应式布局系统
- 配置自动缩放和锚点
- 优化UI元素大小和间距
- 测试各种分辨率下的显示效果

性能分析和优化

用户: "游戏在低端设备上运行卡顿,需要优化到60FPS"
系统: 智能优化:
- 全面性能分析和瓶颈检测
- 优化渲染管线和Draw call
- 减少物理计算开销
- 优化脚本执行效率
- 调整画质设置和特效等级

内存使用优化

用户: "游戏内存占用过高,经常闪退,需要优化内存使用"
系统: 全面优化:
- 分析内存使用热点
- 实现智能对象池系统
- 优化资源加载和卸载策略
- 压缩纹理和音频资源
- 配置垃圾回收策略

平台性能适配

用户: "需要为PC、移动、主机三个平台优化性能"
系统: 多平台优化:
- 针对不同平台的性能策略
- 平台特定的渲染设置
- 输入和UI适配优化
- 性能分级和质量设置

工作流程

1. 性能分析阶段

# 全面性能检测
- 帧率和稳定性分析
- 内存使用情况检测
- 渲染性能瓶颈识别
- CPU和GPU使用率分析

2. 优化策略制定

# 制定优化方案
- 确定优化优先级
- 选择合适的优化技术
- 平衡画质和性能
- 制定分阶段优化计划

3. 自动优化实施

# 执行优化操作
- 自动调整渲染设置
- 优化代码和算法
- 配置资源压缩
- 实现内存管理策略

4. 效果验证

# 验证优化效果
- 对比优化前后性能
- 测试不同设备表现
- 验证稳定性改善
- 生成优化报告

MCP 工具集成

性能分析工具

  • get_performance_metrics - 获取性能指标
  • analyze_memory_usage - 分析内存使用
  • profile_render_performance - 分析渲染性能
  • detect_performance_bottlenecks - 检测性能瓶颈

优化操作工具

  • optimize_render_settings - 优化渲染设置
  • compress_textures - 压缩纹理资源
  • configure_object_pools - 配置对象池
  • adjust_quality_settings - 调整画质设置

平台适配工具

  • detect_platform_capabilities - 检测平台能力
  • configure_platform_settings - 配置平台设置
  • optimize_for_platform - 针对平台优化
  • test_platform_performance - 测试平台性能

监控调试工具

  • create_performance_monitor - 创建性能监控器
  • setup_debug_overlay - 设置调试界面
  • log_performance_data - 记录性能数据
  • generate_performance_report - 生成性能报告

Read the full file on GitHub · 722 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. 9d ago First seen · 722 lines · 42 tokens per session scan A fdef3b8fecd5

Subscribe to this mod's changes

godot-performance-optimizer is a skill published in the GitHub repository hhhh124hhhh/godot-mcp (42 stars, last pushed 9mo ago), licensed MIT. It adds 42 tokens to every session and 5,665 once invoked, about $0.0002 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 2026-08-30.