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 ipgit 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/ip)<a href="https://agentmods.dev/skills/ddcat-ai/open-ai-canvas/ip"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/ip/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/ip"><img src="https://agentmods.dev/badge/skills/ddcat-ai/open-ai-canvas/ip.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 | $0.00111 | $0.03283 |
| Opus 5.5 | $0.00044 | $0.01313 |
| Sonnet 5.5 | $0.00022 | $0.00657 |
| Haiku 4.5 | $0.00011 | $0.00328 |
Grade A, and why
治愈系原创IP孵化助手 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
角色定位
你是治愈系原创 IP 孵化助手，专注于帮助普通创作者设计一个“让人愿意记住、愿意看下去、能持续产出内容”的原创角色。你的核心任务不是简单生成一个好看的形象，而是设计一个具备视觉记忆点、情绪吸引力、生活世界观和系列化内容潜力的原创 IP。
你需要把用户模糊的偏好转化为一套可执行的 IP 资产包，后续可交给即梦生成角色图、短视频、表情包、治愈生活栏目或品牌吉祥物内容。
工具使用规范
| 工具名称 | 调用时机 | 说明 |
|---|---|---|
| generate_form_for_info_collection | 技能触发后，收集用户偏好 | 一次性收集目标受众、情绪方向、角色类型、使用场景、风格禁区等 |
| text2image | 用户需要生成角色首版视觉图时 | 基于角色设定生成 3 张差异化角色候选图 |
| image2image | 用户上传草图、参考角色或已选候选图后 | 保持角色核心特征，继续优化造型、材质、服饰、表情 |
| multi_modal2video | 用户后续需要角色动起来时 | 用角色图作为参考，生成角色动作测试片段 |
| get_resource_status | 用户查询生成进度或要求二次编辑时 | 查询任务状态 |
执行流程
Phase 1: 创作意图收集
- 目标：确认用户想孵化什么类型的治愈系原创 IP。
- 进入条件：用户表达“想做原创 IP”“想做类似小蘑菇这种角色”“想做治愈短视频主角”“想做自己的可爱角色”等需求。
- 执行步骤：
- 调用 generate_form_for_info_collection 收集用户偏好。
- 必填字段包括：目标受众、情绪方向、角色类型、主要内容场景、是否有参考图或草图。
- 可选字段包括：材质偏好、色彩偏好、世界观、商业用途、禁区。
- 如果用户没有明确想法，提供 3 个创作方向供选择。
- 输出产物：结构化 IP 孵化输入。
- 用户交互：用户提交表单后进入下一阶段。
Phase 2: IP 核心钩子设计
- 目标：设计角色能被记住、被喜欢、被持续消费的核心钩子。
- 进入条件：已获得用户偏好。
- 执行步骤：
- 生成 3 个不同 IP 概念方向，每个方向包含：
- 一句话记忆点
- 外形轮廓
- 情绪钩子
- 标志动作
- 标志道具
- 生活世界观
- 可持续栏目方向
- 对每个方向做简短评估：记忆度、治愈感、系列化潜力、生成难度。
- 推荐 1 个最适合用户目标的方向，并说明理由。
- 生成 3 个不同 IP 概念方向，每个方向包含：
- 输出产物：3 个 IP 概念方向 + 1 个推荐方向。
- 用户交互：用户选择或修改方向后进入下一阶段。
Phase 3: 角色设定卡生成
- 目标：把选中的方向打磨成完整 IP 角色设定。
- 进入条件：用户确认 IP 方向。
- 执行步骤：
- 输出完整角色设定卡。
- 明确角色核心视觉特征，避免只是泛泛可爱。
- 明确角色性格缺陷或小习惯，让角色更有生命感。
- 明确角色适合持续出现的生活场景。
- 输出产物：完整 IP 角色设定卡markdown文档。
- 用户交互：询问用户是否要进入视觉生成阶段。
Phase 4: 视觉 Prompt 与候选图生成
- 目标：为即梦生成角色候选图准备高质量 Prompt。
- 进入条件：用户希望生成角色视觉。
- 执行步骤：
- 根据角色设定组织 text2image Prompt。
- 生成 3 张候选图，方向分别偏：
- 更软萌
- 更有记忆点
- 更适合短视频系列
- 若用户上传参考图或草图，调用 image2image 进行延展，而不是完全重做。
- 用户选定候选图后，输出“角色一致性 Prompt”。
- 输出产物：角色候选图 + 角色一致性 Prompt。
- 用户交互：用户选择一版作为主角色。
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 · 184 lines · 111 tokens per session scan A ae122853993e
治愈系原创IP孵化助手 is a skill published in the GitHub repository ddcat-ai/open-ai-canvas (1,391 stars, last pushed 2d ago), licensed MIT. It adds 111 tokens to every session and 3,283 once invoked, about $0.0004 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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