A high-performance short-form video script diagnostic tool designed to rebuild content for Douyin-native traffic algorithms.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install douyin
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install douyin using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
This skill serves as an algorithm alchemist for short-form video content, specifically targeting the unique mechanics of the Douyin platform. By leveraging this Openclaw Skills integration, creators can transform weak scripts into high-retention assets by focusing on the critical first three seconds and recommendation-friendly pacing. It moves beyond simple copywriting to analyze the underlying traffic structure, ensuring every word serves a purpose in holding viewer attention and triggering platform distribution.
The core philosophy of this tool is that a script wins only if it survives the initial swipe. It provides technical creators with the diagnostic power to identify retention friction, redesign pacing, and turn average concepts into distribution-ready formats that satisfy both the user and the recommendation engine.
To utilize this Douyin architecture skill within your agent workflow, ensure it is enabled in your Openclaw Skills configuration.
# Install the Douyin optimization skill to your local environment
openclaw install douyin-script-optimizer
# Configure your agent to trigger the script analysis protocol
openclaw configure --skill douyin-script-optimizer
The skill organizes its output into a standardized Douyin Traffic Assessment schema:
| Section | Data Points | Purpose |
|---|---|---|
| Assessment Header | Video Goal, Audience Mode, Traffic Type | Defines the strategic target |
| First 3 Seconds | Hook Strength, Swipe Risk, Core Hook | Evaluates initial retention potential |
| Retention Structure | Opening pressure, Mid-video risk, Payoff moment | Maps the attention lifecycle |
| Problem Set | Speed, Genericity, Tension issues | Highlights specific friction points |
| Rebuild Plan | Sequential rewrite steps | Provides actionable optimization instructions |
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