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 zenstory-ai/novel-to-game --skill game-conceptgit clone --depth 1 https://github.com/zenstory-ai/novel-to-gameWrote 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/zenstory-ai/novel-to-game/game-concept)<a href="https://agentmods.dev/skills/zenstory-ai/novel-to-game/game-concept"><img src="https://agentmods.dev/badge/skills/zenstory-ai/novel-to-game/game-concept/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/zenstory-ai/novel-to-game/game-concept"><img src="https://agentmods.dev/badge/skills/zenstory-ai/novel-to-game/game-concept.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00139 | $0.00937 |
| Opus 5 | $0.00069 | $0.00468 |
| Sonnet 5 | $0.00028 | $0.00187 |
| Haiku 4.5 | $0.00014 | $0.00094 |
Grade A, and why
game-concept 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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- game-concept — 100% identical, 24 lines differ
What it actually says
游戏概念设计
决定做成什么游戏，不写代码、数值表或逐场关卡脚本。
读取 concept-method.md。输入必须包含 SOURCE_BIBLE.md 与
PRODUCT_BRIEF.md；缺任一项就停止并说明缺口，不代替上游补写。
产物语言由 PRODUCT_BRIEF.md 锁定；未锁定时跟随对话语言，不默认产出中文。
决策边界
直接继承 brief 已锁的平台、输入、受众、分级、时长、目标市场和非目标；发现它们与原作明显冲突 时回总入口修订，不在概念阶段静默改值。原作语言、文化语境、目标市场和界面语言分别处理，不从 其中一个自动推出另一个。
互动叙事是成熟玩法，不因以阅读和对白承载就降低标准；它的动词可以是读局面、追问、比对信息、 表态、隐瞒、交出与承担回响。先例提供语法，原作规则负责改变动作对象、顺序、代价和世界回应， 不能只做 IP 换皮。
概念还要判断验证形态：可用成熟交互语法表达时说明“已有语法 + 原作如何改变它”；核心依赖实时手感、 空间、视线、物理或独特操作时明确走自定义灰盒。不要把文本原型当所有体验的统一前置层，也不要因有 模板可用就让模板替原作决定玩法。
比较范围
先列 brief 的锁定维度和仍开放的维度，只比较能揭示真实取舍的方向，不凑概念数量或差异维度。 仅在方向仍开放时考察可信替代；用户已选定方向或不存在有效替代时说明原因，改为验证该方向的风险， 不重开选择。
选择
先按 concept-method 的硬否决逐个淘汰，不计算总分，再按其比较维度选择。quick 自动选证据最强的
方向；director 只对尚未决定的方向给出推荐并等待用户选择。
输出
生成一个 concepts/CONCEPT.md，只含：
- brief 锁定值与开放维度；
- 体验承诺及其可观察现象和失败现象；
- 紧凑先例说明；
- 候选概念卡：主类型、
experienceProfile、借用/拒绝的玩法先例、核心动作与循环、压力、失败、 世界响应；说明玩家身份如何形成反复行动、执行阻力与可观察变化，不要求独立的固定键名。 仅当自然语言能体现玩家身份时写signature_command的id、label与候选intents，否则写N/A； 最后说明原作张力如何由玩家亲手 enact、招牌画面、最小验证切片、最大风险 和否决观察； - 硬否决结果、关键取舍、推荐与选择状态；
- 选定方向的玩家能动性合同、不可妥协体验承诺；
- 最小验证问题与开放问题。
narrative-led 或 hybrid 的叙事层还要选择一种分支结构语法并说明内容预算。交接前确认选定方向
可在 brief 范围内做成完整切片。
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago Changed · -10 tokens per session 6e933a4de634
- 7d ago Changed · -16 lines a8d321eb3d64
- 13d ago First seen · 73 lines · 149 tokens per session scan A 7080002eb9aa
game-concept is a skill published in the GitHub repository zenstory-ai/novel-to-game (775 stars, last pushed yesterday), licensed MIT. It adds 139 tokens to every session and 937 once invoked, about $0.0007 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.
Other skills, from other repositories
reversi-master
One-click install + model switch:Claude Code,Codex CLI (OpenAI), Grok Build (xAI), DeepSeek Harness, Kimi Code (Moonshot) ,Qwen Code,Aider,OpenCode,MiMo Code (Xiaomi),ZCode (Z.AI),OpenClaw,Pi,OpenScience,Vibe-Trading,Claude Desktop (3P profile),ChatGPT desktop,OpenCode Desktop.
design-system
Guided, section-by-section GDD authoring for a single game system. Gathers context from existing docs, walks through each required section collaboratively, cross-references dependencies, and writes incrementally to file.
prototype
Concept prototype — validate the core idea is worth designing before writing GDDs. Run right after /brainstorm and /setup-engine. Routes to HTML, Engine, or Paper path based on game type. Produces a throwaway build and a PROCEED/PIVOT/KILL verdict.
setup-engine
Configure the project's game engine and version. Pins the engine in CLAUDE.md, detects knowledge gaps, and populates engine reference docs via WebSearch when the version is beyond the LLM's training data.
ux-design
Guided, section-by-section UX spec authoring for a screen, flow, or HUD. Reads game concept, player journey, and relevant GDDs to provide context-aware design guidance. Produces ux-spec.md (per screen/flow) or hud-design.md using the studio templates.
architecture-review
Validates completeness and consistency of the project architecture against all GDDs. Builds a traceability matrix mapping every GDD technical requirement to ADRs, identifies coverage gaps, detects cross-ADR conflicts, verifies engine compatibility consistency across all decisions, and produces a PASS/CONCERNS/FAIL…