影策 is an open-source workspace for AI film and short-video production, using a freeform canvas and structured workflows to organize scripts, characters, scenes, storyboards, media assets, and generated text, images, video, and audio. It is for creators who manage audiovisual projects through local or trusted deployments, with features for task tracking, asset management, timelines, and AI-agent workflows.
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 ddcat-ai/open-ai-canvas --skill 01git clone --depth 1 https://github.com/ddcat-ai/open-ai-canvasWrote 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/ddcat-ai/open-ai-canvas/01)<a href="https://agentmods.dev/skills/ddcat-ai/open-ai-canvas/01"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/01/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/ddcat-ai/open-ai-canvas/01"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/01.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 | $0.00123 | $0.03416 |
| Opus 5.5 | $0.00049 | $0.01366 |
| Sonnet 5.5 | $0.00025 | $0.00683 |
| Haiku 4.5 | $0.00012 | $0.00342 |
Grade A, and why
影视剧作工坊-01-触发与任务识别 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 11d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
触发与任务识别（模块 01）
主链定位：四轴判定（工作模式/视频模式/渲染引擎/输出模型）+ 最小链路与组合触发，只做路由。
做什么
本模块是整个工坊的唯一入口。任何请求先进这里，过两道闸门，然后被路由出去：
- 闸门一 · 值不值得做 —— 场景审计（数据层
audit/）。只跑在文本上，先于任何生成。 - 闸门二 · 怎么做 —— 四轴判定。
- 出口 —— 最小链路 / 组合触发 → 按路由表调 02–13。
本模块不生产画面内容、不写提示词、不做技术决策。它只判定与分派；一旦判定完成，领域工作一律交给对应模块。
闸门一：场景审计（生成前审查）
一句话：结构上已死的场景照样能生成得干净利落——模型对"一个节拍配不配待在那里"没有意见；失效只在素材已经生成、剪辑却立不起来之后才浮出水面。所以最便宜的审查发生在生成之前。
每个镜头都消耗额度与迭代次数，本闸门唯一的职责是阻止你花钱渲染一场不可能成立的戏：它决定哪些镜头值得这笔开销。
五要素引擎（因果链按序检查）
目标（Goal）→ 障碍（Obstacle）→ 策略（Tactic）→ 反转（Reversal）→ 价值位移（Value Shift）
| 要素 | 判定要点 |
|---|---|
| 目标 | 主角为之战斗的唯一且不变的那件事。大目标从不改变；他可以在策略上犯错，绝不在目标上犯错。每场戏的目标必须是指向故事目标的因果一环——删掉这场戏、链条依然成立 → 不合格，不允许孤立的戏 |
| 障碍 | 一个危及路径阶段或整个目标的环境态势——是威胁，不只是代价。要说清"什么处于风险 + 规模（局部/全局）"。什么都没处于风险 → 障碍是假的，戏会松掉 |
| 策略 | 威胁逼主角选出的那一招，是基于当前不完整知识的合情合理的猜测。引擎本质是知识缺口。每一个失败的策略都必须返回信息——失败的策略只有在失败什么都没教会时才构成弱点 |
| 反转 | 违背预期的转折，三种形式：① 我自己的动作反噬我 ② 隐藏的施动者被揭露 ③ 局面转向策略所意图的反面。每个已解决的序列 ≥1 个 |
| 价值位移 | 拱顶石：观众对一个角色的解读因反转而发生的改变。它不发生在纸面上，而发生在观看者的头脑里——每次反转都逼观众重新评判这个角色 |
两条硬规则
- **反转与价值位移在序列层面评判，不在单场戏层面。**单场戏可以是纯粹的升级，但一个已解决的序列如果零反转，就不合格。序列可以嵌套，每个收束单元都需要自己的反转——审计时标明你在哪个规模上审计。
- 没有价值位移的反转是惰性的。一次结构转折若不能移动观众的判决，就不算反转。说不出观众的前判决和后判决，这个转折就是死重——无论情节翻了多大。
审计输出与处置
审计按目标 → 障碍 → 策略 → 反转 → 价值位移的顺序走，输出固定格式（链条检查 + 最弱点 + 三层修改建议）。
不合格不直接打回重写，而是给三层建议，三个层级各有意义、不得合并成一次重写：
- 最小改动 —— 保留用户的版本
- 干净版 —— 一个完全成立的版本，即使离原版更远
- 可选 —— 仅当它确实有所增益时
使用边界：本引擎的定义刻意不采用教科书术语，按原文使用，不要替换成标准编剧术语的宽松解释（那些会让判不过关的场景蒙混过关）。判例来源未在自有素材上系统验证——作强启发式使用。
执行细节、审计输出格式与完整流程见 audit/01-场景审计.md。
链条成立之后——它在链路中的位置
| 下一步 | 归属 |
|---|---|
| 这些人是谁，以及他们的前史 | 04 场景与人物设计 |
| 共享的场景方向与每个角色的燃料 | 05 人物表演 |
| 把谈定的场景拆成镜头 | 07 分镜头设计 |
| 写镜头本身 | 12 技术语言转换（prompt/12-提示词结构.md） |
（原文的"归属"栏指向《表演行为主模块》《镜头组接·纯技术文档》等外部文档名，上表已换成本工坊内的模块号。）
闸门二：四轴判定
四轴独立判定、可叠加、可默认兜底。四轴的判定细则本体见 spec/01-四轴判定与触发机制.md §3。
固定解析顺序：工作模式 → 视频模式 → 渲染引擎 → 输出模型（先定产出范围，再定输入形态，再定光影体系，最后定执行端；后轴不覆盖前轴）。
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.
- 11d ago First seen · 144 lines · 123 tokens per session scan A b743e65d1d8f
影视剧作工坊-01-触发与任务识别 is a skill published in the GitHub repository ddcat-ai/open-ai-canvas (1,391 stars, last pushed 2d ago), licensed MIT. It adds 123 tokens to every session and 3,416 once invoked, about $0.0005 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 2026-10-14.
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