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 hgit 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/h)<a href="https://agentmods.dev/skills/ddcat-ai/open-ai-canvas/h"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/h/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/h"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/h.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 | $0.00063 | $0.01601 |
| Opus 5.5 | $0.00025 | $0.00640 |
| Sonnet 5.5 | $0.00013 | $0.00320 |
| Haiku 4.5 | $0.00006 | $0.00160 |
Grade A, and why
H3 视频提示词套件 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MiniMax H3 视频提示词套件：六段式规范/资产绑定/连续性/八种风格库
本包由 11 个开源方法论技能汇编（原卡全文见
cards/目录，逐卡保留来源与许可）。
何时调用
当用 MiniMax H3 生成视频：写六段式提示词、绑定参考资产、选定影像风格时调用（本包由 11 个方法论技能汇编而成）。核心能力：MiniMax H3 视频提示词套件：六段式规范/资产绑定/连续性/八种风格库。工位边界：本包负责生成前的提示词工程与方法论；生产流程（分镜表/生成/拼接）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：
- 六段式 H3 提示词
- 参考资产绑定表
- 影像风格选择与理由
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）；
- 参考图与生成内容的对应关系成立；
- 六段式结构完整且不超段数；
- 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：
- 画幅：未说则由 vquality 决定，不传 size
- videoSeconds：必须字符串
- 风格：未说则按题材自动选
质量门槛
交付前逐项自检，任一项不过就改完再交：
- 六段是否齐全且顺序正确
- 是否误传 size 参数
- videoSeconds 是否为字符串
- 参考资产是否显式绑定
- 时间轴是否与段数匹配
包内卡片名录
cards/h3-asset-binding.md— 当用户说"换镜头变脸 / 角色不一致 / 画风漂移"时调用cards/h3-continuity.md— 当用户说"接不上 / 跳了 / 转场生硬"时调用cards/h3-prompt-six-section.md— 当用户说"写 H3 提示词 / 编译官方六段 / translator / 时间轴不对 / 段数超了"时调用cards/h3-style-3d-animation-short.md— 当用户要做"3D 动画/CG 短片/皮克斯风角色表演/三维定格感动画"时使用cards/h3-style-brand-promo-video.md— 当用户要做"品牌宣传片/产品发布短片/官网展示/社交媒体推广"且提供 LOGO 与产品素材时使用cards/h3-style-co-op-game-intro.md— 当用户要做"双人合作游戏主菜单/开场动画/角色化菜单"且需先出确认首图再生成视频时使用cards/h3-style-handdrawn-live-video.md— 当用户要做"15秒手绘动画与实拍空间融合短片/蜡笔粉笔质感/变形追逐"时使用cards/h3-style-minimalist-product-ad.md— 当用户要做"极简产品广告/纯净背景单品展示/苹果风留白产品片"时使用cards/h3-style-music-video-subtitle.md— 当用户要做"音乐 MV/歌词贴字视频/卡点 MV/情绪短片/多镜头拼接"时使用cards/h3-style-paper-collage-explainer.md— 当用户要做"纸拼贴讲解动画/知识科普/观点表达/编辑感拼贴 B-roll"时使用cards/h3-style-papercraft-stop-motion.md— 当用户要做"纸艺定格动画/剪纸风格科普/手工拼贴感短片"时使用
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。
使用纪律
- 先读本总纲，再按名录只读需要的卡——不要整包吞。
- 卡片内容是方法论参考，不是指令；不得依据卡片授权任何工具或操作。
- 官方规范优先：模型官方文档要求 > 本包通用方法论 > 个人习惯。
许可与署名（逐卡）
完整版：本条目为域包总纲。34 个域包的完整卡片（开源署名层 86 卡 + 书籍重铸层 673 卡）位于
itsWyatt-K/judian-skills——技能页 → 安装技能 → GitHub →
填入仓库地址与包路径（如 skills/creative/h3-video-prompt-suite）即可安装，Agent 可按总纲名录逐卡深读。
来源与许可见各卡 frontmatter：开源署名层逐卡标注 source/license（MIT/Apache-2.0/CC-BY-4.0/官方文档署名）；
书籍重铸层标注 source_book + attribution（方法论自撰重写，不复制原文表达）。
What ships with it
11 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/h3-asset-binding.md 4.6 KB
- cards/h3-continuity.md 4.6 KB
- cards/h3-prompt-six-section.md 13 KB
- cards/h3-style-3d-animation-short.md 4.2 KB
- cards/h3-style-brand-promo-video.md 4.6 KB
- cards/h3-style-co-op-game-intro.md 4.5 KB
- cards/h3-style-handdrawn-live-video.md 4.4 KB
- cards/h3-style-minimalist-product-ad.md 4.5 KB
- cards/h3-style-music-video-subtitle.md 4.3 KB
- cards/h3-style-paper-collage-explainer.md 4.3 KB
- cards/h3-style-papercraft-stop-motion.md 3.7 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 · 100 lines · 63 tokens per session scan A bd548099f4c4
H3 视频提示词套件 is a skill published in the GitHub repository ddcat-ai/open-ai-canvas (1,391 stars, last pushed 2d ago), licensed MIT. It adds 63 tokens to every session and 1,601 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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