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 aigit 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/ai)<a href="https://agentmods.dev/skills/ddcat-ai/open-ai-canvas/ai"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/ai/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/ai"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/ai.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 | $0.00064 | $0.01813 |
| Opus 5.5 | $0.00026 | $0.00725 |
| Sonnet 5.5 | $0.00013 | $0.00363 |
| Haiku 4.5 | $0.00006 | $0.00181 |
Grade A, and why
AI 表演实验室 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 28d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI 角色表演实验室：表情/动作/焦点/资产一致性
本包由 19 张方法论重铸卡汇编重铸（卡片明细与出处见各卡 frontmatter 的 source_card / source_book）。
何时调用
当让 AI 生成的角色演得对、长得住时调用（本包由 19 张方法论卡汇编而成，整理自《AI角色演技怎么救？我总结了5条好用的提示词技巧》、《【IP 配图 Skill 必看】全网独一份的架构详解》）。核心能力：AI 角色表演实验室：表情/动作/焦点/资产一致性。关键触发：“有情绪变化”、“镜头怎么引导观众看谁”、“情绪变化戏”、“假脸/表情笼统/AI 随机发挥”、“情绪反转/权力变化”、“脸像塑料/太干净/不真实”。工位边界：本包负责创作方法论层；提示词语法与模型参数走影策官方市场技能，两者接力不抢戏。
本包交付什么
用户提出需求时，本包最终交付：
- 微表情与眼神参数表（面部区域 / 强度 / 持续帧）
- 表演基本功清单（姿态、视线、身体承接）
- 面部特写镜头的提示词段落
内容保真优先级
冲突时按此顺序裁决，禁止圆场：
- 用户明确指定（时长/风格/禁项/参考职责）；
- 表演符合角色此刻的欲望与处境；
- 微表情可被模型稳定复现；
- 风格与质感装饰。
默认决策
用户未指定时采用，并在交付前用一句话说明选了什么：
- 表情强度：未说则中等，先保可控再加强度
- 特写镜头：默认先出确认首图再生成视频
- 面部区域：未指定则先调眉眼与嘴角
质量门槛
交付前逐项自检，任一项不过就改完再交：
- 每个表情是否对应一个可观测的面部区域
- 是否避免同时要求多个互斥表情
- 眼神是否有明确注视目标
- 是否给出参考图锚点以保跨镜一致
包内卡片名录
cards/afa-acting-fundamentals.md— Ed Hooks 核心理论：动画角色的「表演」不是画得好不好看，而是角色是否有思维过程(Thinking)、身体行动(Pcards/afa-character-movement.md— 动画角色每动作须传递信息cards/afa-dialogue-timing.md— 动画台词不仅是口型同步，而是完整表演系统cards/afa-emotional-toolkit.md— 系统解决「角色该有什么情绪」cards/afa-gaming-animation.md— 动画表演在游戏中的独特挑战：玩家输入不可预测、动作需无缝循环与中断、表演必须模块化可组合cards/afa-scene-analysis.md— Hooks实战核心——对六部经典动画做逐场深度拆解，揭示顶级动画如何运用表演原理cards/ai-acting-master.md— AI 角色演技总控：十项公式cards/character-ip-master.md— 跨媒介角色 IP 表现总控cards/dof-focus-cinematography.md— 景深跟焦服务演技cards/emotion-action-beats.md— 情绪拆成动作节拍cards/facial-part-spec.md— 面部拆到具体部位cards/head-body-movement.md— 头身承接表情cards/ip-action-library.md— IP 动作库：表演层·动作cards/ip-character-bible.md— IP 角色圣经：身份层构建cards/ip-illustration-master.md— IP 配图总控：从普通配图到 IP 配图cards/ip-performance-faces.md— IP 表演变量：表情/视线/人物尺度cards/ip-style-decoupling.md— IP 画风解耦：身份与渲染层分离cards/physiological-skin.md— 生理皮肤去塑料感cards/yinxiaowai-family-master.md— 尹小歪 AI 提示词家族 · 跨媒介总控
证据等级说明
本包卡片证据等级为 E4（成熟专业方法重铸）：方法论来自出版书籍与行业方法的独立重铸，尚未在当前模型上逐条做真实成片验证
使用时请知悉：标注 E4/E5 的规则表示"专业上成立"或"可作启发"，
不表示当前模型已能稳定执行。若某条规则在你的实测中失效，按卡内 frontmatter 的
evidence 字段记录实际等级并回报，不要静默虚标为 E1/E2。
What ships with it
19 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.
- cards/afa-acting-fundamentals.md 1.8 KB
- cards/afa-character-movement.md 1.8 KB
- cards/afa-dialogue-timing.md 1.7 KB
- cards/afa-emotional-toolkit.md 1.6 KB
- cards/afa-gaming-animation.md 1.8 KB
- cards/afa-scene-analysis.md 1.6 KB
- cards/ai-acting-master.md 6.2 KB
- cards/character-ip-master.md 7.5 KB
- cards/dof-focus-cinematography.md 4.3 KB
- cards/emotion-action-beats.md 4.9 KB
- cards/facial-part-spec.md 4.3 KB
- cards/head-body-movement.md 4.3 KB
- cards/ip-action-library.md 5.6 KB
- cards/ip-character-bible.md 6.6 KB
- cards/ip-illustration-master.md 7.4 KB
- cards/ip-performance-faces.md 5.6 KB
- cards/ip-style-decoupling.md 5.8 KB
- cards/physiological-skin.md 4.4 KB
- cards/yinxiaowai-family-master.md 6.5 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.
- 28d ago First seen · 109 lines · 64 tokens per session scan A f81879de0f1d
AI 表演实验室 is a skill published in the GitHub repository ddcat-ai/open-ai-canvas (1,391 stars, last pushed 2d ago), licensed MIT. It adds 64 tokens to every session and 1,813 once invoked, about $0.0003 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 2026-09-27.
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