A comprehensive automation skill for generating professional, device-framed, and marketing-ready mobile app screenshots for various app stores.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install screenshots
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 screenshots 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 Screenshots?
The Screenshots skill for Openclaw Skills is a robust solution for developers and marketers looking to streamline the creation of high-quality mobile app marketing assets. By integrating automated device framing, marketing copy overlays, and multi-device sizing, it transforms raw simulator captures into polished assets ready for App Store and Google Play submissions.
This skill goes beyond simple image processing by utilizing a persistent memory system to learn user style preferences over time. Built with a focus on visual consistency, it leverages advanced vision models to ensure that every generated image meets strict quality standards and legibility requirements across all device sizes supported by Openclaw Skills.
Screenshots Use Cases
- Creating a full set of App Store and Google Play screenshots from a single set of raw captures.
- Automating the application of brand colors and marketing typography across multiple device generations.
- Iteratively refining store assets based on visual feedback while maintaining version history.
- Ensuring all marketing text remains within safe zones and is readable at thumbnail sizes.
- Managing complex asset pipelines for multiple apps using a centralized template system.
How Screenshots Works
- The skill begins with an intake phase where raw screenshots, icons, and brand assets are collected.
- It calculates required dimensions based on the latest platform specifications found in specs.md.
- Backgrounds, device frames, and marketing text overlays are applied according to defined visual templates.
- An integrated vision model reviews the output to verify legibility, safe-zone compliance, and style consistency.
- The user provides feedback, which the skill uses to iteratively adjust the visual output and update its persistent memory.
- Final assets are exported into versioned folders, with a symlink pointing to the latest approved set for easy access.
Screenshots Setup
To start using this skill within Openclaw Skills, you must first initialize the local workspace directory for persistent storage:
mkdir -p ~/screenshots
Ensure that your app-specific branding requirements are defined in a config.md file within your project folder to guide the AI on color schemes, font preferences, and desired layout styles.
Screenshots Data Schema & Taxonomy
The skill organizes data in a hierarchical workspace to ensure that Openclaw Skills can maintain project history and persistent preferences.
| File/Folder | Purpose |
|---|---|
~/screenshots/memory.md |
Stores persistent style preferences, learned patterns, and successful templates. |
~/screenshots/{app-slug}/config.md |
Contains app-specific brand settings like colors, fonts, and tone. |
~/screenshots/{app-slug}/raw/ |
Storage for unedited device or simulator captures. |
~/screenshots/{app-slug}/v{n}/ |
Versioned export folders for finalized screenshot sets. |
templates.md |
Defines reusable visual templates for different app categories. |
Screenshots Advanced Features
- Persistent Style Memory: Automatically remembers your favorite font and color combinations to maintain brand consistency across updates.
- Vision-Based QA: Uses AI vision to programmatically check for text readability and layout errors before presenting results to the user.
- Versioned Iteration: Never overwrites previous work, allowing for easy comparison between different design directions or rollbacks to earlier styles.
- Automated Device Framing: Intelligently detects the appropriate device frame based on the source image dimensions and store requirements within the Openclaw Skills ecosystem.
SKILL.md
Loading
Related Openclaw Skills
Scrape
A specialized framework for ethical web scraping that prioritizes robots.txt compliance, rate limiting, and data privacy laws.
SciPy
A comprehensive scientific computing toolkit for solving optimization, statistics, signal processing, and linear algebra problems with production-ready code.
Science
A versatile skill that enables AI agents to calibrate scientific communication and research support based on the user's specific level of expertise.
School
A comprehensive AI-driven educational framework for K-12 students featuring adaptive learning, exam preparation, and robust parental oversight.
Search Engine
A comprehensive framework for designing and building production-grade search engines with optimized indexing, retrieval logic, and relevance controls.
Seattle City Guide
A comprehensive intelligence layer for navigating Seattle as a tech professional, resident, or visitor, providing deep insights into neighborhoods, careers, and local culture.
METADATA
Featured*
