Token导航 LogoToken导航

Agent Skill · 开发

game-performance-profiler(游戏性能分析器)

game-performance-profiler 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

game-performance-profiler 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 12,990。

总安装 1,056周安装 44GitHub Stars 11下载 352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
unknown

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-performance-profiler(游戏性能分析器)
来源仓库:https://github.com/akillness/oh-my-skills
仓库路径:skills/game-performance-profiler
安装命令:
npx skills add https://github.com/akillness/oh-my-skills --skill game-performance-profiler
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/akillness/oh-my-skills --skill game-performance-profiler

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于处理 GitHub 仓库、Issue 和代码协作信息。适合在 Codex、Claude 等宿主中围绕代码变更进行整理。可结合来源仓库和 README 进一步核验具体功能。安装前建议确认权限范围和维护状态。game-performance-profiler 属于开发类 Skill,可作为该场景下的辅助能力补充。

注意检查是否会触发命令执行或文件读写操作。

来源仓库内路径:skills/game-performance-profiler

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/akillness/oh-my-skills --skill game-performanc…;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-performance-profiler 怎么安装?

在终端执行 npx skills add https://github.com/akillness/oh-my-skills --skill game-performance-profiler。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

game-performance-profiler 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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-performance-profiler 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-performance-profiler 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

game-performance-profiler 用的人多吗?

公开数据是累计安装 1,056 次,近一周安装 44 次,下载 352 次,GitHub 11 星。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-performance-profiler 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 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

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
game-performance-profiler

游戏性能分析器 · 本页

1,05611只读未明确通过

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

Game Performance Profiler

Use this skill when the main question is "what packet do we trust, which bottleneck family is most likely, and what is the next capture or review artifact worth producing?"

The job is not to dump generic optimization advice. The job is to normalize the current packet, choose one operating mode, name one primary bottleneck family, recommend the smallest next capture that can improve confidence, and return one profiling artifact teams can actually act on.

Read references/mode-selection-and-route-outs.md before handling an unfamiliar request shape. Read references/capture-packets-and-benchmark-routes.md before designing a reproducible pass. Read references/device-review-and-steam-deck.md before trusting editor numbers for handheld or target-device review. Read references/profiling-patterns.md before classifying weak or ambiguous evidence. Read references/escalation-ladder.md before jumping from screenshots or stat packets to deeper engine or GPU tools.

When to use this skill

  • Unity or Unreal projects with low FPS, frame-time spikes, hitches, or performance regressions where the bottleneck is not yet isolated
  • Profiler screenshots, stat unit / stat gpu output, Unreal Insights traces, overlay captures, route notes, or benchmark complaints that need interpretation
  • Steam Deck, handheld, mobile, console, VR, or low-spec review requests where packaged-on-device behavior matters more than editor impressions
  • Requests for a profiling plan, benchmark route, device review packet, CPU/GPU split note, or escalation choice instead of immediate code edits
  • Mixed performance packets where the next owner is still unclear and the first job is choosing between quick packet, deeper trace, or neighboring game skill

When not to use this skill

  • The main issue is a Unity/Unreal build, cook, package, editor, or CI failure with no runtime frame-time diagnosis yet → use game-build-log-triage
  • The main issue is generic web/app/service performance rather than engine-specific runtime capture interpretation → use performance-optimization
  • The real task is broad milestone or production coordination across bugs, playtest notes, launch goals, and roadmap tradeoffs → use bmad-gds
  • The packet is mainly playtest/demo/community feedback and the question is fix-first prioritization → use game-demo-feedback-triage
  • The next move is already a deep implementation change with a confirmed cause; route to the implementation skill after producing the profiling brief

Instructions

Step 1: Frame the packet

Capture the minimum useful context before diagnosing anything.

Record:

  • engine: Unity | Unreal | Unknown
  • target: PC | Steam Deck / handheld | console | mobile | VR | low-spec laptop | unknown
  • environment: editor | packaged/dev build | release/shipping build | unknown
  • symptom: low average FPS | intermittent hitch | traversal hitch | combat spike | loading stall | thermal drift | unknown
  • evidence available: profiler screenshot, trace/capture file, stat-command screenshot, overlay screenshot, video, benchmark notes, reproduction steps
  • reproduction shape: exact scene, save slot, encounter, traversal path, menu, cutscene, or unknown
  • quality variables: resolution, preset, frame cap, upscaler state, power mode, battery state if relevant

Quick frame:

Engine: Unreal
Target: Steam Deck
Environment: packaged build unknown
Symptom: traversal hitch after two minutes in market square
Evidence: `stat unit` screenshot + overlay photo
Repro: route not yet fixed

Rule: if the packet is thin, keep confidence low and make the next capture smaller, not broader.

Step 2: Choose one primary mode

Use references/mode-selection-and-route-outs.md.

Pick exactly one primary mode:

  • quick-triage-packet
  • bottleneck-classification
  • benchmark-route-plan
  • device-review
  • tool-escalation

Rule: one primary mode, optional secondary note. Do not try to handle every mode at once.

Step 3: Name the likely bottleneck family before proposing fixes

Choose one primary family and an optional secondary family.

Primary families:

  • cpu-gameplay-scripting
  • cpu-render-thread-draw-call-pressure
  • gpu-rendering-shaders-postfx
  • memory-gc-allocation-churn
  • loading-streaming-io
  • physics-animation-simulation
  • platform-config-thermal-device-specific
  • unknown-needs-better-capture

Good bottleneck statements:

  • "The strongest signal points to streaming / IO hitching during traversal, not steady-state GPU load."
  • "The packet suggests GC/allocation spikes during combat more than rendering saturation."
  • "This looks device/config-bound because the team is still relying on editor impressions instead of packaged-on-target evidence."

Avoid: "performance is bad overall."

Step 4: Recommend the smallest next capture

Pick the cheapest capture that can materially separate the likely causes.

Typical next captures:

  • one better Unity Profiler CPU/GPU/Memory packet from a representative player build
  • one stat unit + stat gpu pair on the exact Unreal repro route
  • one fixed traversal route with warm-up and repeat counts
  • one packaged-on-device capture instead of more editor screenshots
  • one deeper trace (Unreal Insights, GPU Visualizer, Frame Debugger, or external GPU tool) only after the first packet justifies it

Rule: prefer engine-native captures before vendor GPU tools unless the packet is already clearly render-path specific.

Step 5: Make route and device context explicit

If reproducibility is missing, define the smallest repeatable pass.

Specify:

  • save slot / checkpoint / scene
  • start point and traversal path
  • warm-up pass count
  • measured repeat count or duration
  • graphics preset / frame cap / power mode
  • whether the packet is editor-only, packaged-only, or target-device

Do not treat "the market area feels bad" as a durable benchmark route.

Step 6: Return one profiling brief

Always return one concise artifact with this shape:

# Game Performance Profiling Brief

## Scope
- Mode: ...
- Engine: ...
- Target: ...
- Environment: ...
- Symptom: ...
- Confidence: high | medium | low

## Evidence packet
- What exists now: ...
- What's missing: ...
- Editor vs packaged / device note: ...

## Primary bottleneck hypothesis
- Bucket: ...
- Why it fits: ...
- Evidence: ...

## Secondary hypothesis
- Bucket: ...
- Why it still matters: ...

## Next capture
1. ...
2. ...
3. ...

## Benchmark route / device review
- Repro route or save: ...
- Repeat / warm-up guidance: ...
- Device or packaged-build checks: ...

## Escalation path
- Stay with quick packet | move to engine profiler | escalate to GPU tool
- Why: ...

## Recommended next artifact
- Choose one: quick triage packet | profiling plan | benchmark route brief | CPU/GPU split note | memory/GC checklist | streaming hitch checklist | device review brief

## What not to do yet
- 1-3 bullets that prevent premature optimization or blind rewrites

Output format

Required qualities:

  • classify the bottleneck before talking about fixes
  • separate evidence from hypothesis
  • recommend the next capture, route, or device review step instead of a giant backlog
  • make editor-vs-packaged and packet-vs-trace boundaries explicit
  • keep the report roughly 300-550 words unless the user asks for more
  • use engine-native terms such as Unity Profiler, Frame Debugger, Unreal Insights, stat unit, stat gpu, frame time, draw-call pressure, GC, streaming, and packaged build

Examples

Example 1: Unity combat spike

Input: "Our Unity game drops from 120 to 45 FPS in combat. We have Profiler screenshots and someone suspects GC spikes. Triage what to look at first."

Expected shape: classify around memory-gc-allocation-churn or cpu-gameplay-scripting, keep the current screenshots as a real packet, recommend the smallest next capture, and avoid jumping to rendering advice first.

Example 2: Unreal open-world traversal hitch

Input: "Unreal is fine indoors but frame time explodes in our open world area. Help me triage whether this is CPU, GPU, streaming, or shaders."

Expected shape: use bottleneck-classification or benchmark-route-plan, recommend stat unit / stat gpu or Unreal Insights as appropriate, and define a reproducible traversal route instead of guessing fixes.

Example 3: Steam Deck review packet

Input: "We need a Steam Deck performance review plan before our demo release. The editor feels rough but we have not profiled the packaged build on device yet."

Expected shape: choose device-review, keep confidence limited, prioritize packaged-on-device evidence, and return a device review brief or benchmark route brief.

Example 4: Route-out to build failure triage

Input: "Our Unreal packaged build crashes during cook and we do not even have runtime numbers yet."

Expected shape: route to game-build-log-triage instead of pretending this is already a profiling problem.

Best practices

  1. Start from the packet the team already has instead of demanding an ideal trace immediately.
  2. Name one primary bottleneck family before discussing optimizations.
  3. Treat reproducibility as part of the diagnosis, not an optional extra.
  4. Prefer packaged-on-device evidence over editor impressions when the release target is a handheld or constrained machine.
  5. Escalate from screenshot/stat packets to engine-native profilers before jumping to GPU-vendor tooling.
  6. Recommend one next artifact, not a giant optimization backlog.
  7. Keep route-outs explicit so game-performance work does not sprawl into build triage, generic app tuning, or production coordination.

References

平台分布

Codex

36.53%
按下载量换算129

Claude

29.78%
按下载量换算105

Cursor

19.6%
按下载量换算69

Gemini CLI

9.42%
按下载量换算33

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。

继续浏览同类 Skills