A structured multi-turn workflow for transforming rough ideas into high-quality AI-generated videos using Volcengine Ark.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install video-intent-studio
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install video-intent-studio using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Video Intent Studio?
Video Intent Studio is a specialized framework designed to bridge the gap between vague creative ideas and professional video output. As a premier entry in the Openclaw Skills library, it provides a guided experience that prevents the common pitfalls of one-shot AI video generation. It prioritizes user intent by facilitating a staged journey through style selection, parameter tuning, and prompt previewing before committing to the final render.
By leveraging bundled Python scripts and a comprehensive type catalog, this skill ensures that technical parameters like motion intensity, aspect ratios, and visual styles are correctly mapped to the underlying Volcengine Ark model. It is an essential tool for creators who require transparency and control over their AI video production pipeline within an automated environment.
Video Intent Studio Use Cases
- Refining rough conceptual ideas into specific cinematic or animated video styles.
- Automating the parameter selection for Volcengine Ark video generation.
- Creating reproducible video generation workflows with consistent state tracking.
- Developing prompt previews to ensure visual alignment before utilizing API credits.
- Managing multi-turn creative dialogues between developers and AI agents.
How Video Intent Studio Works
- Capture Intent: The skill records the user's raw idea and initializes a state object to track the session.
- Type Ranking: It executes a backend script to suggest 3 to 5 optimized video types based on the user's input.
- Prompt Building: Once a type is selected, it generates a prompt preview and summarizes default parameters like duration and ratio.
- Parameter Tuning: Users can adjust settings such as motion, brightness, and style, which triggers a prompt rebuild.
- Confirmation: The system presents the final prompt for explicit user approval to prevent accidental generation.
- Generation: Upon confirmation, it invokes the Ark video generator script to produce the MP4 file.
- Reporting: The workflow completes by providing the file path, task ID, or detailed error categorization.
Video Intent Studio Setup
To integrate this component of Openclaw Skills, ensure your environment is configured with the necessary API credentials:
# Set your Volcengine Ark credentials
export ARK_API_KEY="your_api_key_here"
export VOLCENGINE_ARK_API_KEY="your_api_key_here"
# Optional: Customize the model or task URL
export ARK_VIDEO_MODEL="your_model_name"
Ensure Python is installed and navigate to the skill directory to access the bundled scripts located in /scripts/.
Video Intent Studio Data Schema & Taxonomy
Video Intent Studio utilizes a structured state management system and external reference files to maintain workflow integrity:
| Data Component | Description |
|---|---|
| Memory State | A JSON-like object tracking user_input, selected_type, params, and stage. |
| video-types.md | A reference catalog containing fixed video categories and their associated default values. |
| Prompt Metadata | Parameters like --motion, --ratio, and --duration are injected directly into the prompt payload. |
| Output Files | Final video results are saved as .mp4 files with associated task metadata. |
Video Intent Studio Advanced Features
- Multi-turn state persistence for complex creative revisions.
- Dynamic prompt reconstruction based on natural language feedback (e.g., "make it more realistic").
- Support for custom environment variables to toggle models and task URLs.
- Robust error handling and bucketing for API, network, or remote task failures.
- Transparent prompt previews that allow users to see exactly what will be sent to the LLM.
SKILL.md
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