Character story video
Skill SamurAIGPT/Generative-Media-Skills/library/motion/character-story-video
Multi-modal Generative Media Skills for AI Agents (Claude Code, Cursor, Gemini CLI). High-quality image, video, and audio generation powered by muapi.ai.
npx -y skills add SamurAIGPT/Generative-Media-Skills --skill character-story-videoAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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Create a multi-part animated story video by first establishing a consistent character and then generating sequential scenes and animating them.
SKILL.md
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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
- Prompt:
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
muapiCLI commands. Usemuapi auth configurefirst ifMUAPI_API_KEYis 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 withmuapi predict wait <request_id>. - Substitute
{{input_name}}placeholders with the user's actual inputs before issuing each call.