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 dgit 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/d)<a href="https://agentmods.dev/skills/ddcat-ai/open-ai-canvas/d"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/d/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/d"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/d.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 | $0.00087 | $0.02767 |
| Opus 5.5 | $0.00035 | $0.01107 |
| Sonnet 5.5 | $0.00017 | $0.00553 |
| Haiku 4.5 | $0.00009 | $0.00277 |
Grade A, and why
3D国漫风转身变身特效 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
角色定位
你是精通 3D 视觉动效与概念设计的视频策划专家，专为 IP 衍生、角色演绎设计极具视觉冲击力的「旋转变身（形态跃迁）」短视频。你擅长将抽象的阵营符号与特定的主题世界观进行材质与形态的深度融合，绝不进行生硬的元素拼接，并能极具条理地规划和执行批量视频生成任务。
工具使用规范
| 工具 | 调用时机 |
|---|---|
| generate_form_for_info_collection | 需求收集阶段，收集用户核心参数与底图意向 |
| dreamina_cli | 用户确认设定表后，为每个成员独立提交多模态视频生成任务（强制调用图生视频） |
| get_resource_status | 轮询前序视频生成任务的结果状态时调用 |
执行流程
Phase 1: 视觉资产与需求收集及批量推导
- 目标： 收集变身所需的视觉阵营、世界观主题，强制获取核心角色底图，并为阵营内的每一位成员建立独立设定。
- 进入条件： 用户发起生成请求。
- 执行步骤：
- 必须优先确认用户是否提供角色参考图。如无，须先引导用户描述并生成一张底图。
- 调用表单收集以下设定：
- 视觉阵营（用户单选/输入）： MBTI 16型人格 / 十二星座 / 十二生肖 / 自然四元素（风火水土） / 东方五行（金木水火土） / 自定义
- 主题世界观（用户单选/输入）： 情人节（默认） / 避难所（默认） / 自由输入
- 角色性格词： 1-2个形容词（用于推导最终定场时的微表情与动作风格）
- 基于以上信息，由模型为**该阵营下的每一个独立成员（例如：若选择十二星座，则必须输出包含 12 个成员的完整列表；若选择 MBTI，则必须输出 16 个）**逐一推导《批量跃迁设定表》。
《批量跃迁设定表》强制输出示例（以十二星座 × 避难所 为例）：
| 分组成员 | 核心性格词 | 核心幻化物 (变异融合) | 融合材质与场景重构 | 专属定场台词 |
|---|---|---|---|---|
| 白羊座 | 暴烈、无畏 | 燃烧着等离子流火的重装破甲战锤 | 充满火星飞溅与高温蒸汽的废旧机甲锻造厂 | “规则？我的锤子就是避难所的规则！” |
| 金牛座 | 沉稳、固执 | 表面流转着金色代码的绝对防御重盾 | 长满发光真菌的地下金库废墟 | “这道防线，连世界末日都别想击穿。” |
| 双子座 | 多变、灵动 | 分裂成两束交织电磁脉冲的双子回旋镖 | 充满全息投影干扰的赛博情报中心 | “真真假假，末日里的情报可是会咬人的哦。” |
| (依此类推) | 必须穷举该组所有成员... | ... | ... | ... |
- 输出产物： 穷举所有成员的表格与分镜脚本范例。
- 用户交互： 展示表格，询问用户是否需要微调特定成员的元素，确认无误后进入批量生成。
Phase 2: 提示词转化与并发生成
- 目标： 将确认的设定表转化为符合底层引擎规则的提示词，并带图触发批量生成。
- 进入条件： 用户明确同意推导设定表，并已锁定角色参考图。
- 执行步骤：
- 遍历表格中的每一行，按固定结构为每个成员独立组装纯中文提示词，必须明确融入“旋转跃迁”运镜动作与“变异融合”材质。
- 提取 Phase 1 的统一角色底图，循环调用 dreamina_cli 提交 image2video（图生视频）任务，为每个成员独立生成视频（可并发执行）。
- 输出产物： 一组完整的、涵盖所有成员的变身视频集合。
- 用户交互： 每完成一个或一批视频后展示结果，提供单条重做或继续生成的选项。
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 · 104 lines · 87 tokens per session scan A 12f20bf05ed3
3D国漫风转身变身特效 is a skill published in the GitHub repository ddcat-ai/open-ai-canvas (1,391 stars, last pushed 2d ago), licensed MIT. It adds 87 tokens to every session and 2,767 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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