scripts/check_env.py

python3 <skill-dir>/scripts/check_env.py

• Success (exit code 0): Capture

  PYTHON_PATH=<path>

  from the output, record this path for Step 7, continue
• Failure (exit code 1): Parse each check result from the output

  ENV_RESULT=...

  JSON, show users clear and easy-to-understand error descriptions and repair guidance (see the troubleshooting guide below), terminate the workflow

Whiteboard animation video generation requires RunningHub API to call AI models to generate images first. You need to configure the API Key in the

.env

file under the skill directory.

Fix method:

1. Add a line to the

   <skill-dir>/.env

   file:

   RUNNINGHUB_API_KEY=your API key

2. If the file does not exist, create it first
3. The API Key can be obtained from RunningHub
4. Rerun after the configuration is complete

Whiteboard animation relies on Python libraries such as OpenCV, NumPy, and PyAV to process videos. Automatic installation failed.

Fix method:

1. Please confirm that your Python version is 3.9 or higher
2. Then rerun this workflow, and the script will try to install automatically again

• If the user does not specify

  outputDir

  , use the directory where

  srtPath

  is located as the output root directory
• Convert

  outputDir

  to an absolute path

Use the Read tool to read the file

<replace this skill directory with the actual absolute path>/references/storyboard-parser.md

, and execute according to the workflow steps in it.

Input parameters:

• srtPath =

  <replace srtPath with the actual absolute path>

• projectRoot =

  <replace storyboardDir with the actual absolute path>

• skill-dir =

  <replace this skill directory with the actual absolute path>

  (used to locate scripts)

Return the absolute path of storyboard.json and the number of scenes after completion.

Note: The main agent must fill in the actual path value into the instruction, do not pass the variable name, the subagent cannot access the context of the main agent.

Use the Read tool to read the file

<replace this skill directory with the actual absolute path>/references/image-generator.md

, and execute according to the workflow steps in it.

Use batch mode, pass the following JSON string array as the prompt parameter:

<paste the actual content of the prompt JSON array output in Step 4 here>

Parameters:

• skill-dir =

  <replace this skill directory with the actual absolute path>

  (used to locate scripts)
• Output directory =

  <replace imageDir with the actual absolute path>

• Aspect ratio = "16:9"

Note: The main agent must fill in the actual prompt content and path value into the instruction, do not pass the variable name, the subagent cannot access the context of the main agent.

Important: Return the list of paths of all generated images, and the order must be consistent with the order of the prompt array.

Call the whiteboard-animation skill, use batch mode. Skip environment pre-check (the main agent has confirmed that the environment is ready), and run the batch generation script directly:

<replace PYTHON_PATH with the actual value obtained in Step 0> <replace the whiteboard-animation skill directory with the actual path>/scripts/batch_generate.py \
  --images <imagePaths[0]> <imagePaths[1]> ... \
  --durations <durations[0]> <durations[1]> ... \
  --output-dir <absolute path of videoDir>

The main agent must fill in the actual absolute path of

PYTHON_PATH

and the skill directory into the instruction, do not pass the variable name, the subagent cannot access the context of the main agent.

Parameters:

• --images

  : List of image paths arranged in storyboard order (separated by spaces)

• --durations

  : List of durations corresponding to images one by one (unit: milliseconds, directly use the duration value in the storyboard, no conversion required)

• --output-dir

  :

  <absolute path of videoDir>

Important: After completion, collect all generated video file paths in the

<videoDir>

directory, sort them by file name timestamp, and return the complete video path list to the main agent. The order must be consistent with the order of input images.

scripts/workflow_helper.py

PYTHON_PATH

<PYTHON_PATH> <skill-dir>/scripts/workflow_helper.py merge-videos "<outputDir>" <videoPath1> <videoPath2> ...

• The first parameter is the output directory (the merged video is saved in this directory)
• The subsequent parameters are the paths of video clips arranged in storyboard order

mergedVideo

totalSegments

sizeMB

• Step 0 must be executed before any work starts. The

  check_env.py

  script will automatically detect and install repairable dependencies, and only report unreparable problems to the large model
• Steps 3, 5, and 7 must be executed by subagent, and the main agent waits for the result
• The

  PYTHON_PATH

  obtained in Step 0 must be passed to the subagent of Step 7 for use, and also used for video merging in Step 8 (the PyAV dependency is in the virtual environment) to avoid repeated environment checks
• Step 7 uses the batch mode of whiteboard-animation (batch_generate.py), the subagent passes in all images and durations at one time, the script generates serially internally, and returns all video paths after the subagent completes
• The order of images and videos must strictly correspond to the order of scenes in the storyboard
• Duration is used in milliseconds throughout the entire link, from storyboard to batch_generate.py to generate_whiteboard.py, unified as milliseconds to avoid loss of precision in floating point conversion
• Step 8 uses PyAV (Python library) to merge video clips, and re-encodes through H.264 to output the final video in a unified format

• storyboard-parser.md

  - SRT storyboard parsing workflow instruction, read and executed by the subagent in Step 3

• image-generator.md

  - Banana2 image generation workflow instruction, read and executed by the subagent in Step 5

• check_env.py

  - One-time environment pre-check, check Python virtual environment, API Key, automatically install repairable dependencies

• workflow_helper.py

  - Provides three subcommands:

  init-dirs

  (create directory structure),

  gen-prompts

  (parse storyboard to generate prompts),

  merge-videos

  (merge video clips)

• generate-storyboard.py

  - Parse SRT + groups.json to generate storyboard.json

• generate-image.py

  - Banana2 model text-to-image, supports single and batch concurrent modes

• banana_prompt_template.py

  - Whiteboard style prompt template