内容创作
#video
muapi-character-story-video
Create a multi-part animated story video by first establishing a consistent character and then generating sequential scenes and animating them.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=samuraigpt-generative-media-skills-library-motion-character-story-video-skill-md&format=skill
下载 .skill
标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
slug muapi-character-story-video name muapi-character-story-video version 1.0.0 description Create a multi-part animated story video by first establishing a consistent character and then generating sequential scenes and animating them. acceptLicenseTerms true Character Story Video Create a multi-part animated story video by first establishing a consistent character and then generating sequential scenes and animating them. Inputs Name Type Required Default Description character_description text yes — Description of the main character (e.g. "a cute piglet wearing a leather aviator jacket and goggles"). story_premise text yes — The overall story arc (e.g. "building a jetpack and flying to space"). reference_image image_url no — Optional starting image of the character to maintain consistency. Steps This skill involves multiple phases to build a cohesive narrative. Phase A — Character Establishment If {{reference_image}} is NOT provided, submit the plan with ONE step to create the character: Character Creation — muapi image generate (model= nano-banana-pro ): Prompt: {{character_description}}, introducing the main character, cinematic lighting, highly detailed, Pixar 3D animation style. Aspect ratio: 4:5 or 1:1 If {{reference_image}} IS provided, use it as the established character and proceed to Phase B. After generation, ask the user to confirm the character design before proceeding. Phase B — Sequential Scene Generation Once the character is established, create the story beats (e.g., Scene 1, Scene 2, Scene 3). Submit the plan using muapi image edit (model= nano-banana-2-edit or flux-kontext-pro-i2i ) to maintain character consistency. Use the established character image as the reference for ALL these steps. Scene 1 (Beginning) Reference: Character Image Prompt: The character ({{character_description}}) in the first scene of the story: [Describe the beginning of {{story_premise}}]. Cinematic lighting, Pixar 3D animation style, storybook illustration. Scene 2 (Middle) Reference: Character Image Prompt: The character ({{character_description}}) in the second scene: [Describe the climax or middle action of {{story_premise}}]. Cinematic lighting, Pixar 3D animation style, storybook illustration. Scene 3 (End) Reference: Character Image Prompt: The character ({{character_description}}) in the final scene: [Describe the resolution of {{story_premise}}]. Cinematic lighting, Pixar 3D animation style, storybook illustration. Note: All scenes should be generated in parallel or sequentially depending on the story flow. After generating the scenes, present them to the user and ask if they are ready to animate the story. Phase C — Animation (Sequel Part 1, Part 2, Part 3) Submit the plan to animate the generated scenes using an image-to-video model (e.g., kling-v3.0-pro-image-to-video or veo3.1-image-to-video ). Part 1 Video Input: Scene 1 Image Prompt: Cinematic animation of the scene, character comes to life, subtle natural movements, high quality 3D animation. Part 2 Video Input: Scene 2 Image Prompt: Cinematic animation of the scene, character comes to life, dynamic action, high quality 3D animation. Part 3 Video Input: Scene 3 Image Prompt: Cinematic animation of the scene, character comes to life, triumphant resolution, high quality 3D animation. After generating the videos, present them to the user as a multi-part story sequence. You may also suggest using the muapi predict result + ffmpeg concat tool to merge them into a single movie if requested. Trigger Keywords character story , story video , animated story , sequel video , multi part video , sequential story Notes for the Executing Agent This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset. For model IDs without a CLI alias yet, fall back to the raw endpoint via curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id> . Substitute {{input_name}} placeholders with the user's actual inputs before issuing each call.
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下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识(skill/v1) |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类(数组) |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接(本页地址) |
| exported_at | 导出时间(每次下载生成) |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数:provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明(Coze / Dify / Claude / 自定义框架) |