An automated evaluation tool that analyzes ad images and videos to score effectiveness and extract messaging patterns.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ad-creative-analysis
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 ad-creative-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The Ad Creative Analysis skill is a sophisticated tool designed for marketers and developers to dissect competitor or reference ad assets. By utilizing vision-based analysis, it evaluates design dimensions like visual hierarchy and color usage while scoring creatives on attention-grabbing potential and message clarity. This skill is a core part of the Openclaw Skills library, enabling teams to transform folders of raw media into structured, actionable intelligence.
Beyond simple observation, it interprets the psychological triggers behind ads, identifying emotional appeals such as urgency or social proof. Whether you are dealing with static images or short-form video content, this skill provides a standardized framework for understanding what makes an ad successful in a crowded digital landscape.
To get started with this skill, ensure your ad assets are organized in a local directory. You can optionally include a metadata.json file to provide additional context like spend or impressions.
# Trigger the analysis by pointing the agent to your creative folder
openclaw run ad-creative-analysis --dir ./path/to/your/ads
The agent will then list all detected files and proceed with the multi-step vision and script analysis.
The output is provided as a comprehensive JSON array. Below are the key data points captured during the process:
| Field | Type | Description |
|---|---|---|
scale_score |
Integer | A 1-10 score representing viral potential (Video only). |
attention_grab |
Integer | Scroll-stop effectiveness score (Image only). |
emotion_appeal |
String | The detected psychological trigger (e.g., aspiration, fear). |
hook_text |
String | The text or dialogue used to capture initial attention. |
platform_fit |
Array | Recommended platforms based on aspect ratio and format. |
summary |
Object | Aggregated insights and recommendations for the entire batch. |
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