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Agent Skill · 开发

team-polish(团队打磨)

team-polish 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。

team-polish 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;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 13,037。

总安装 1,035周安装 44GitHub Stars 16,796下载 363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:team-polish(团队打磨)
来源仓库:https://github.com/donchitos/claude-code-game-studios
仓库路径:skills/team-polish
安装命令:
npx skills add https://github.com/donchitos/claude-code-game-studios --skill team-polish
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/donchitos/claude-code-game-studios --skill team-polish

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。

team-polish 用于处理 GitHub 仓库、Issue、Pull Request 等协作数据。适用于 Codex、Claude、Cursor、Gemini CLI 中优化代码或协作流程的场景。通过 npx skills add 命令安装,功能细节需查阅原始项目文档。

安装前建议核实权限、维护状态及是否执行系统命令或读写文件。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/team-polish

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/donchitos/claude-code-game-studios --skill tea…;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

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

team-polish 怎么安装?

在终端执行 npx skills add https://github.com/donchitos/claude-code-game-studios --skill team-polish。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

team-polish 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。

team-polish 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/donchitos/claude-code-game-studios --skill team-polish 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

team-polish 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。

team-polish 用的人多吗?

公开数据是累计安装 1,035 次,近一周安装 44 次,下载 363 次,GitHub 16,796 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。

team-polish 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。
  • 来源仓库有 16,796 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
team-polish

团队打磨 · 本页

1,03516,796执行命令未明确通过

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

If no argument is provided, output usage guidance and exit without spawning any agents:

Usage: /team-polish [feature or area] — specify the feature or area to polish (e.g., combat, main menu, inventory system, level-1). Do not use AskUserQuestion here; output the guidance directly.

When this skill is invoked with an argument, orchestrate the polish team through a structured pipeline.

Decision Points: At each phase transition, use AskUserQuestion to present the user with the subagent's proposals as selectable options. Write the agent's full analysis in conversation, then capture the decision with concise labels. The user must approve before moving to the next phase.

Team Composition

  • performance-analyst — Profiling, optimization, memory analysis, frame budget
  • engine-programmer — Engine-level bottlenecks: rendering pipeline, memory, resource loading (invoke when performance-analyst identifies low-level root causes)
  • technical-artist — VFX polish, shader optimization, visual quality
  • sound-designer — Audio polish, mixing, ambient layers, feedback sounds
  • tools-programmer — Content pipeline tool verification, editor tool stability, automation fixes (invoke when content authoring tools are involved in the polished area)
  • qa-tester — Edge case testing, regression testing, soak testing

How to Delegate

Use the Task tool to spawn each team member as a subagent:

  • subagent_type: performance-analyst — Profiling, optimization, memory analysis
  • subagent_type: engine-programmer — Engine-level fixes for rendering, memory, resource loading
  • subagent_type: technical-artist — VFX polish, shader optimization, visual quality
  • subagent_type: sound-designer — Audio polish, mixing, ambient layers
  • subagent_type: tools-programmer — Content pipeline and editor tool verification
  • subagent_type: qa-tester — Edge case testing, regression testing, soak testing

Always provide full context in each agent's prompt (target feature/area, performance budgets, known issues). Launch independent agents in parallel where the pipeline allows it (e.g., Phases 3 and 4 can run simultaneously).

Pipeline

Phase 1: Assessment

Delegate to performance-analyst:

  • Profile the target feature/area using /perf-profile
  • Identify performance bottlenecks and frame budget violations
  • Measure memory usage and check for leaks
  • Benchmark against target hardware specs
  • Output: performance report with prioritized optimization list

Phase 2: Optimization

Delegate to performance-analyst (with relevant programmers as needed):

  • Fix performance hotspots identified in Phase 1
  • Optimize draw calls, reduce overdraw
  • Fix memory leaks and reduce allocation pressure
  • Verify optimizations don't change gameplay behavior
  • Output: optimized code with before/after metrics

If Phase 1 identified engine-level root causes (rendering pipeline, resource loading, memory allocator), delegate those fixes to engine-programmer in parallel:

  • Optimize hot paths in engine systems
  • Fix allocation pressure in core loops
  • Output: engine-level fixes with profiler validation

Phase 3: Visual Polish (parallel with Phase 2)

Delegate to technical-artist:

  • Review VFX for quality and consistency with art bible
  • Optimize particle systems and shader effects
  • Add screen shake, camera effects, and visual juice where appropriate
  • Ensure effects degrade gracefully on lower settings
  • Output: polished visual effects

Phase 4: Audio Polish (parallel with Phase 2)

Delegate to sound-designer:

  • Review audio events for completeness (are any actions missing sound feedback?)
  • Check audio mix levels — nothing too loud or too quiet relative to the mix
  • Add ambient audio layers for atmosphere
  • Verify audio plays correctly with spatial positioning
  • Output: audio polish list and mixing notes

Phase 5: Hardening

Delegate to qa-tester:

  • Test all edge cases: boundary conditions, rapid inputs, unusual sequences
  • Soak test: run the feature for extended periods checking for degradation
  • Stress test: maximum entities, worst-case scenarios
  • Regression test: verify polish changes haven't broken existing functionality
  • Test on minimum spec hardware (if available)
  • Output: test results with any remaining issues

Phase 6: Sign-off

  • Collect results from all team members
  • Compare performance metrics against budgets
  • Report: READY FOR RELEASE / NEEDS MORE WORK
  • List any remaining issues with severity and recommendations

Error Recovery Protocol

If any spawned agent (via Task) returns BLOCKED, errors, or cannot complete:

  1. Surface immediately: Report "[AgentName]: BLOCKED — [reason]" to the user before continuing to dependent phases
  2. Assess dependencies: Check whether the blocked agent's output is required by subsequent phases. If yes, do not proceed past that dependency point without user input.
  3. Offer options via AskUserQuestion with choices:

- Skip this agent and note the gap in the final report - Retry with narrower scope - Stop here and resolve the blocker first

  1. Always produce a partial report — output whatever was completed. Never discard work because one agent blocked.

Common blockers:

  • Input file missing (story not found, GDD absent) → redirect to the skill that creates it
  • ADR status is Proposed → do not implement; run /architecture-decision first
  • Scope too large → split into two stories via /create-stories
  • Conflicting instructions between ADR and story → surface the conflict, do not guess

File Write Protocol

All file writes (performance reports, test results, evidence docs) are delegated to sub-agents spawned via Task. Each sub-agent enforces the "May I write to [path]?" protocol. This orchestrator does not write files directly.

Output

A summary report covering: performance before/after metrics, visual polish changes, audio polish changes, test results, and release readiness assessment.

Next Steps

  • If READY FOR RELEASE: run /release-checklist for the final pre-release validation.
  • If NEEDS MORE WORK: schedule remaining issues in /sprint-plan update and re-run /team-polish after fixes.
  • Run /gate-check for a formal phase gate verdict before handing off to release.

平台分布

Codex

40.29%
按下载量换算146

Claude

29.06%
按下载量换算105

Cursor

18.33%
按下载量换算67

Gemini CLI

9.5%
按下载量换算34

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/donchitos/claude-code-game-studios --skill team-polish 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。

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