zenstory-ai/drama-skills is a collection of AI coding-agent skills for producing short dramas and animated stories from an idea or source material, covering scripts, visual assets, storyboards, and image, video, and audio prompts. It is intended for screenwriters, animation studios, and directors using Claude Code, Codex, or other agents that support the Agent Skill format. The catalogue skills are the project's own workflow for planning, reviewing, and producing these projects.
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/drama-skills --skill short-drama-knowhowgit clone --depth 1 https://github.com/zenstory-ai/drama-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/zenstory-ai/drama-skills/short-drama-knowhow)<a href="https://agentmods.dev/skills/zenstory-ai/drama-skills/short-drama-knowhow"><img src="https://agentmods.dev/badge/skills/zenstory-ai/drama-skills/short-drama-knowhow/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/drama-skills/short-drama-knowhow"><img src="https://agentmods.dev/badge/skills/zenstory-ai/drama-skills/short-drama-knowhow.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 | $0.00109 | $0.02076 |
| Opus 5 | $0.00055 | $0.01038 |
| Sonnet 5 | $0.00022 | $0.00415 |
| Haiku 4.5 | $0.00011 | $0.00208 |
Grade A, and why
short-drama-knowhow 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
短剧 Know-how 学习
把学习过程当作编剧、漫剧工作室与编导共同参与的定性研究,而不是词频挖掘。 先理解完整项目如何把戏剧意图传递到可执行的文本制作规格,再决定哪些知识可迁移。
开始前锁定边界
只在维护者主动调用本 skill,且当前会话已经提供授权的只读访问时读取非公开来源。 把连接细节、凭据、源名称和私有卡片留在维护者指定的隔离工作区;不得写入本 skill、 公开技能树、公共候选或评测夹具。没有授权或无法确认只读时停止来源读取,只能用合成材料演练流程。
本流程只研究文本及其版本关系。可读创意简报、故事结构、剧本、资产/连续性台账、图片提示词、 分镜/关键帧文字、视频提示词、审查与修订记录;不生成、渲染、下载或检查图像、音频或视频。 公开运行时永不连接非公开来源,也不承担任何媒体生成。
核心原则
- 让 agent 判断题材语境、人物欲望、因果、信息差、情绪推进、表演动作、镜头意图与制作取舍。
- 只把校验格式、比较文件、查重与隐私扫描等确定性步骤交给现有工具;不要为创作语义写规则匹配脚本。
- 统计只用于描绘可选样本、发现覆盖缺口、寻找异常与安排下一次阅读;不得从频次、排名或相关性直接推出创作规则。
- 一条候选知识必须同时携带证据、反例或失败情形、冲突解释、适用边界和置信度;“常见”不等于“有效”。
- 每次只提出可审查的最小公共改动;未经盲测与独立审查不得直接进入公共 skill。
工作流
1. 确立只读授权与隔离工作区
记录本轮目的、允许读取的文本范围、禁止读取的内容、输出位置和销毁要求。测试一次只读行为, 但不回显连接信息。建立私有研究目录与单独的去标识候选目录;公共目录起初保持不变。
2. 以覆盖缺口选样
先查看维护者允许的最小索引或目录信息。按题材、受众承诺、核心机制、篇幅阶段、制作形态、 视觉语言、场景功能、提示词职责、完成度与修订状态寻找缺口、对照组和反常样本。统计只能帮助 “下一部读什么”,不能回答“应该怎么写”。 优先形成有差异的目的性样本,而不是只读最热门、最多见或最方便查询的项目。
3. 定性通读完整项目链
逐个项目从上游意图读到下游文本交付:创意/改编约束 → 故事引擎与分集设计 → 单集剧本 → 角色、场景、道具与连续性 → 图片提示词 → 分镜与关键帧文字 → 视频提示词 → 审查、修订和交付说明。 若某层不存在,明确记为缺失,不用别的项目或常识补齐。
沿同一承诺追踪:怎样建立期待、怎样制造阻碍和信息差、人物用什么可表演行动推进、转折怎样被铺垫, 以及镜头/提示词怎样保留而不是改写剧作意图。完整阅读后才写卡,不用批量关键词命中代替阅读。 卡片字段与合成例子见 cards-and-coverage.md。
4. 写私有 observation / decision cards
先写 observation card,严格区分直接观察、agent 解释与未知;再写 decision card,记录是否形成候选、 为什么缩小适用范围、有哪些替代解释。每张卡用不泄漏内容的稳定内部引用绑定同一项目链, 但私有卡片永不进入公共技能包。
5. 构建题材 × 机制 coverage matrix
用“题材/受众承诺”作为一轴,用“期待建立、冲突升级、信息释放、关系转向、反转兑现、集尾牵引、 表演动作、镜头承接、连续性”等机制作为另一轴;提示词研究再叠加制作形态/视觉语言、场景功能、 提示词职责与版本角色。每格记录支持卡、反例卡、边界、缺口和下一样本, 不填出现次数或成功率。矩阵格式见 cards-and-coverage.md。
6. 用反例、冲突与适用边界压缩结论
主动寻找同机制失败、同题材例外、同结果的替代解释,以及上游有效但下游不可制作的情形。 冲突未解释时保持候选,不做多数表决。把“总是如此”改写成“在这些观众承诺、人物状态和制作约束下, 这项机制可能解决这个问题;出现这些信号时应换法或不用”。详细判别见 synthesis-and-promotion.md。
7. 去标识与 de-copy
删除或抽象源名称、人物名、专有设定、原句、罕见组合、精确数值、路径、地址、标识符和连接信息。 把表层桥段拆成“观众状态 → 戏剧问题 → 可选机制 → 可观察效果 → 失败边界”,再用全新的题材、人物关系、 场景和措辞重建例子。若只有复述原内容才能表达,就不进入公共候选。
What ships with it
6 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.
- 13d ago First seen · 117 lines · 109 tokens per session scan A cb17c0bcd2de
short-drama-knowhow is a skill published in the GitHub repository zenstory-ai/drama-skills (1,798 stars, last pushed yesterday), licensed MIT. It adds 109 tokens to every session and 2,076 once invoked, about $0.0005 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
cinematic-director
A prompt generator for image models that turns a scene description into an English cinematic photography prompt and a negative prompt, which lists details to avoid.
drama-skills
A collection of AI skills for creating short dramas and animated comics, from an idea or long source material to scripts and visual-production instructions. It covers episode scripts, story assets, image prompts, storyboard frames, and video prompts.
happiness-skill
A Chinese-language guide to happiness based on reducing unmet wants, focusing on the present, and treating happiness as a trainable skill.
aesthetic-audit
A commercial visual and user-experience review workflow for websites, product pages, landing pages, and visual assets. It examines clarity, hierarchy, spacing, typography, color, trust signals, conversion paths, and mobile behavior.
setup-matt-pocock-skills
A setup skill that configures engineering skills for a repository, including its issue tracker, labels, and documentation layout. A repository is the project folder managed by version control.
tapcanvas-prompt-specialists
A set of role and output rules for specialists that create image prompts, video prompts, and pacing reviews in TapCanvas. It explains what evidence to pass to each specialist and what results they should return.