/video-generation

name: video-generation
description: Use this skill when the user requests to generate, create, or imagine videos. Supports structured prompts and reference image for guided generation.

Video Generation Skill

Overview

This skill generates high-quality videos using structured prompts and a Python script. The workflow includes creating JSON-formatted prompts and executing video generation with optional reference image.

Core Capabilities

• Create structured JSON prompts for AIGC video generation
• Support reference image as guidance or the first/last frame of the video
• Generate videos through automated Python script execution

Workflow

Step 1: Understand Requirements

When a user requests video generation, identify:

• Subject/content: What should be in the image
• Style preferences: Art style, mood, color palette
• Technical specs: Aspect ratio, composition, lighting
• Reference image: Any image to guide generation
• You don't need to check the folder under `/mnt/user-data`

Step 2: Create Structured Prompt

Generate a structured JSON file in `/mnt/user-data/workspace/` with naming pattern: `{descriptive-name}.json`

Step 3: Create Reference Image (Optional when image-generation skill is available)

Generate reference image for the video generation.

• If only 1 image is provided, use it as the guided frame of the video

Step 3: Execute Generation

Call the Python script:

python /mnt/skills/public/video-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/prompt-file.json \
  --reference-images /path/to/ref1.jpg \
  --output-file /mnt/user-data/outputs/generated-video.mp4 \
  --aspect-ratio 16:9

Parameters:

• `--prompt-file`: Absolute path to JSON prompt file (required)
• `--reference-images`: Absolute paths to reference image (optional)
• `--output-file`: Absolute path to output image file (required)
• `--aspect-ratio`: Aspect ratio of the generated image (optional, default: 16:9)

[!NOTE] Do NOT read the python file, instead just call it with the parameters.

Video Generation Example

User request: "Generate a short video clip depicting the opening scene from "The Chronicles of Narnia: The Lion, the Witch and the Wardrobe"

Step 1: Search for the opening scene of "The Chronicles of Narnia: The Lion, the Witch and the Wardrobe" online

Step 2: Create a JSON prompt file with the following content:

{
  "title": "The Chronicles of Narnia - Train Station Farewell",
  "background": {
    "description": "World War II evacuation scene at a crowded London train station. Steam and smoke fill the air as children are being sent to the countryside to escape the Blitz.",
    "era": "1940s wartime Britain",
    "location": "London railway station platform"
  },
  "characters": ["Mrs. Pevensie", "Lucy Pevensie"],
  "camera": {
    "type": "Close-up two-shot",
    "movement": "Static with subtle handheld movement",
    "angle": "Profile view, intimate framing",
    "focus": "Both faces in focus, background soft bokeh"
  },
  "dialogue": [
    {
      "character": "Mrs. Pevensie",
      "text": "You must be brave for me, darling. I'll come for you... I promise."
    },
    {
      "character": "Lucy Pevensie",
      "text": "I will be, mother. I promise."
    }
  ],
  "audio": [
    {
      "type": "Train whistle blows (signaling departure)",
      "volume": 1
    },
    {
      "type": "Strings swell emotionally, then fade",
      "volume": 0.5
    },
    {
      "type": "Ambient sound of the train station",
      "volume": 0.5
    }
  ]
}

Step 3: Use the image-generation skill to generate the reference image

Load the image-generation skill and generate a single reference image `narnia-farewell-scene-01.jpg` according to the skill.

Step 4: Use the generate.py script to generate the video

python /mnt/skills/public/video-generation/scripts/generate.py \
  --prompt-file /mnt/user-data/workspace/narnia-farewell-scene.json \
  --reference-images /mnt/user-data/outputs/narnia-farewell-scene-01.jpg \
  --output-file /mnt/user-data/outputs/narnia-farewell-scene-01.mp4 \
  --aspect-ratio 16:9

> Do NOT read the python file, just call it with the parameters.

Output Handling

After generation:

• Videos are typically saved in `/mnt/user-data/outputs/`
• Share generated videos (come first) with user as well as generated image if applicable, using `present_files` tool
• Provide brief description of the generation result
• Offer to iterate if adjustments needed

Notes

• Always use English for prompts regardless of user's language
• JSON format ensures structured, parsable prompts
• Reference image enhance generation quality significantly
• Iterative refinement is normal for optimal results