AGENT SKILL

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.

使用场景图像与多媒体
License 许可
Apache-2.0
最近更新
2026-11-11
Stars
★ 1.6k
Forks
468

01 前言 Front Matter

name
muapi-character-story-video
slug
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

03 SKILL.md 正文

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:

  1. 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.

  1. 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.
  2. 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.
  3. 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).

  1. Part 1 Video
    • Input: Scene 1 Image
    • Prompt: Cinematic animation of the scene, character comes to life, subtle natural movements, high quality 3D animation.
  2. Part 2 Video
    • Input: Scene 2 Image
    • Prompt: Cinematic animation of the scene, character comes to life, dynamic action, high quality 3D animation.
  3. 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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