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 skills add pixel-cellar/Claude-Code-Game-Studios --skill team-levelgit clone --depth 1 https://github.com/pixel-cellar/Claude-Code-Game-StudiosWrote 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/pixel-cellar/claude-code-game-studios/team-level)<a href="https://agentmods.dev/skills/pixel-cellar/claude-code-game-studios/team-level"><img src="https://agentmods.dev/badge/skills/pixel-cellar/claude-code-game-studios/team-level/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pixel-cellar/claude-code-game-studios/team-level"><img src="https://agentmods.dev/badge/skills/pixel-cellar/claude-code-game-studios/team-level.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00042 | $0.01146 |
| Opus 5.5 | $0.00017 | $0.00458 |
| Sonnet 5.5 | $0.00008 | $0.00229 |
| Haiku 4.5 | $0.00004 | $0.00115 |
Grade A, and why
team-level 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 1mo 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
当此技能被调用时:
决策节点: 在每个步骤转换时,使用 AskUserQuestion 将子代理的方案以可选项的形式呈现给用户。在对话中写出代理的完整分析,然后以简洁的标签记录决策。用户必须批准后才能进入下一步。
-
读取参数以确定目标关卡或区域(例如
新手教程、森林地牢、枢纽城镇、最终 Boss 竞技场)。 -
收集上下文:
- 读取位于
design/gdd/game-concept.md的游戏概念文档 - 读取位于
design/gdd/game-pillars.md的游戏支柱文档 - 读取
design/levels/中的现有关卡文档 - 读取
design/narrative/中的相关叙事文档 - 读取该区域所属地区/阵营的世界观设定文档
- 读取位于
如何委派
使用 Task 工具将每位团队成员生成为子代理:
subagent_type: narrative-director— 叙事目的、角色、情感弧线subagent_type: world-builder— 背景设定上下文、环境叙事、世界规则subagent_type: level-designer— 空间布局、节奏、遭遇战、导航subagent_type: systems-designer— 敌人组合、战利品表、难度平衡subagent_type: art-director— 视觉主题、调色板、光照、资产需求subagent_type: qa-tester— 测试用例、边界测试、试玩检查清单
始终在每个代理的提示中提供完整上下文(游戏概念、支柱、现有关卡文档、叙事文档)。
- 按顺序编排关卡设计团队:
步骤 1:叙事上下文(narrative-director + world-builder)
生成 narrative-director 代理以:
- 定义该区域的叙事目的(这里发生哪些故事节拍?)
- 识别关键角色、对话触发点和背景设定元素
- 指定情感弧线(玩家进入时、探索中、离开时应有什么感受?)
生成 world-builder 代理以:
- 提供该区域的背景设定上下文(历史、阵营势力、生态)
- 定义环境叙事的机会
- 指定影响该区域玩法的世界规则
步骤 2:布局与遭遇战设计(level-designer)
生成 level-designer 代理以:
- 设计空间布局(关键路径、可选路径、隐藏区域)
- 定义节奏曲线(紧张高峰、休息区域、探索区域)
- 放置具有难度递进的遭遇战
- 设计环境谜题或导航挑战
- 定义兴趣点和地标以辅助寻路
- 指定入口/出口点及与相邻区域的连接
步骤 3:系统集成(systems-designer)
生成 systems-designer 代理以:
- 指定敌人组合和遭遇战公式
- 定义战利品表和奖励放置
- 根据玩家预期等级/装备平衡难度
- 设计区域专属机制或环境危害
- 指定资源分布(生命恢复道具、存档点、商店)
步骤 4:视觉方向(art-director)
生成 art-director 代理以:
- 定义该区域的视觉主题和调色板
- 指定光照氛围和时间设置
- 列出所需美术资产(环境道具、专属资产)
- 定义视觉地标和视线
- 指定特殊视觉特效需求(天气、粒子、雾气)
步骤 5:QA 规划(qa-tester)
生成 qa-tester 代理以:
- 编写关键路径的测试用例
- 识别边界情况和极端情况(顺序越界、软锁定)
- 创建该区域的试玩检查清单
- 定义关卡完成的验收标准
-
编制关卡设计文档,将所有团队输出整合为关卡设计模板格式。
-
保存至
design/levels/[关卡名称].md。 -
输出摘要,包含:区域概述、遭遇战数量、预估资产列表、叙事节拍,以及跨团队依赖关系或待解决问题。
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.
- 1mo ago First seen · 84 lines · 42 tokens per session scan A 8601b52d0a88
team-level is a skill published in the GitHub repository pixel-cellar/Claude-Code-Game-Studios (361 stars, last pushed 6mo ago), licensed MIT. It adds 42 tokens to every session and 1,146 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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