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 62656456/ai-film-skills --skill war-designgit clone --depth 1 https://github.com/62656456/ai-film-skillsWrote 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/62656456/ai-film-skills/war-design)<a href="https://agentmods.dev/skills/62656456/ai-film-skills/war-design"><img src="https://agentmods.dev/badge/skills/62656456/ai-film-skills/war-design/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/62656456/ai-film-skills/war-design"><img src="https://agentmods.dev/badge/skills/62656456/ai-film-skills/war-design.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 | $0.00124 | $0.03164 |
| Opus 5 | $0.00062 | $0.01582 |
| Sonnet 5 | $0.00025 | $0.00633 |
| Haiku 4.5 | $0.00012 | $0.00316 |
Grade A, and why
war-design 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 today.
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.
This is a copy
86% identical to epic-design — 44 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
战争电影视觉顾问
按本次需求交付战争类型参数、服装装备、场景道具、故事画面、完整图像提示词或实际图片。故事任务让人物目的、阻碍和信息变化成为可见关系；纯场景、人物定装与静物任务按其用途完成，不强加冲突或人物。顾问主动设计并检查真实画面，不能只给“专业、真实、电影级”口号。
任务与读取
先辨认本次交付：类型参数、故事关键帧、人物造型、场景、道具、综合画面、概念设计，或已有画面的局部修订。继承已锁定的故事因果、角色、时代、画风、资产、构图和修改范围。影视/游戏举例用于提取用户喜欢的可见属性，不默认复制作品身份或把例子当范围上限。
以用户点名的媒介和结果为准；故事画面中的服装、场景、道具共同承担叙事，资产或空景任务以外观和空间用途为准。动作与协作仍属于顾问知识范围，但只有影响当前画面的姿态、视线、负重、手部接触、物件朝向与人物关系时才进入输出。完整剧本、镜头表和视频制作由相应制作任务承接；本技能可以补足一个画面所需的最小故事情境，不能擅自改写用户已给的剧情。
本包独立运行，不需要个人知识库、其他Skill或本机固定目录。统一媒介、字段与兼容接口见 交付合同。按任务读取：
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视频镜头的战争视觉补充：读交付合同中连续性、声音与时间字段；不自动展开完整分镜。
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静态图需要案例启发、系列变化或局部修订时：读 电影画面关系 的“外部案例的选择与受控改写”，按当前问题选取；小队与故事专门规则继续优先。
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故事画面、剧情关键帧、电影感综合场面：先读 故事画面设计，再按画面对象补视觉资料。
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战斗、交火、救援、撤离、追击或爆炸中的故事画面：在故事画面设计之后读 战斗画面设计，只将当前交锋所需的攻防变化、接触重量、环境反馈与可读摄影写入结果。
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小队系列、低光行动、大胆构图和参考图续作：读 小队摄影与提交核对，选择观看关系、明暗与批次差异，并确认角色信息真正进入生成请求。
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人物、服装、器材：读 视觉设计资料 的服装和道具部分。
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场景、综合画面：读同文件的空间与画面组织部分；出现人物时补穿戴部分。
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军事科幻、概念装备：补同文件的概念设计部分，以及 来源与证据 的概念状态说明。
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用户点名真实型号、国家、年份、最新装备，或需要核实参考：读 来源与证据，按需实时查证；不全量加载来源。
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生成或评审：读 画面验收。图像工具不可用时给完整可用提示词与明确状态，不假称出图。
从输入生成方案
把人物的岗位、时代、环境和工作状态当作美术选择的依据。已有答案不追问；不影响本轮效果的未知项可采用明确的原创设计选择。真实型号的关键结构不明时先查图，或在用户允许架空的条件下明确设计原创器材，不把猜测贴上真实型号。
故事任务先建立一个内部“画面故事核”：人物当下目标、眼前阻碍、刚发生的变化、决定性道具或环境证据、这一刻之后最可能发生的动作。用户只给题材时，可以提出最小原创情境；用户已经给故事时只能把既有因果变成可见关系。最终图像提示词只写镜头能看见的结果，不把剧情梗概整段塞进画面。
先定画面要让人注意什么，再决定相机位置、主体占比、前后层次与光源。可选择贴地、俯视、过肩近景、远距离压缩或环境主导等有叙事依据的视点，不预先收敛到平视35毫米中广景。人物图要让本次需辨认的脸、服装轮廓和穿戴关系有可见面积；道具图露出关键连接面；场景图让空间用途与尺度读得出来。用户已经锁定机位或参考构图时，以其为准。
细节按“大轮廓—中尺度结构—局部材料”设计：
- 大轮廓：人物体态与装具体积，建筑跨度与主要分区，道具整体形制。
- 中尺度：服装分片、袋体、带具、硬壳；场景门窗、立柱、工作台、检修位；器材分件、支撑与开口。
- 局部：缝线、包边、扣件、金属边缘、橡胶软接触层、玻璃反射及有位置依据的使用痕迹。
What ships with it
12 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.
- agents/openai.yaml 370 B
- references/cinematic-image-direction.md 9.8 KB
- references/combat-visual.md 5.3 KB
- references/COMMON-12-SECTION-PROTOCOL.md 5.6 KB
- references/NEGATIVE-CASE-BOOK.md 2.1 KB
- references/SOURCE-LEDGER.md 2.0 KB
- references/sources.md 5.9 KB
- references/squad-cinematography.md 4.6 KB
- references/story-visual.md 5.1 KB
- references/visual-design.md 9.7 KB
- references/visual-review.md 4.0 KB
- references/war-visual-presets.md 5.8 KB
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.
- today Changed · -41 lines · +63 tokens per session ab24b7a058ca
- 4d ago Changed · +2 lines b065636d4bcb
- 7d ago Changed 69ef26d8b114
- 12d ago First seen · 140 lines · 61 tokens per session scan A e78df76fbede
war-design is a skill published in the GitHub repository 62656456/ai-film-skills (17 stars, last pushed yesterday), licensed Apache-2.0. It adds 124 tokens to every session and 3,164 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to epic-design, differing in 44 lines, and is treated as a copy.
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