A sophisticated AI pipeline that reimagines your personal photos as surrealist masterpieces using a multi-model workflow and deep macOS integration.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install photo-alchemy
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 photo-alchemy using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
photo-alchemy is a powerful tool designed to breathe new life into your digital photography. By leveraging a dual-model pipeline, it first utilizes Claude to interpret your image and craft a unique surrealist narrative and detailed prompt. Subsequently, Google Gemini takes this creative spark to generate a brand-new high-fidelity image in one of over 35 distinct visual styles.
This skill is particularly optimized for the Apple ecosystem, featuring seamless integration with macOS Photos. It allows users to pull from existing albums, apply character mappings for personalized storytelling, and even automate the creation of a living art gallery for Apple TV. As a standout entry in the Openclaw Skills library, it bridges the gap between raw AI generation and practical home automation.
Install the tool instantly using uvx without a full environment setup:
uvx imagemine path/to/photo.jpg
For a permanent installation on your system, use the following command:
curl -LsSf uvx.sh/imagemine/install.sh | sh
After installation, configure your Anthropic and Google Gemini API keys:
imagemine --config
The skill manages its operations through a local SQLite database located at ~/.imagemine.db. This database tracks the following attributes for every generation:
| Data Point | Description |
|---|---|
| Input Path | The source file location or Photos ID |
| Generated Story | The unique narrative created by Claude |
| Style Used | The specific artistic filter applied to the run |
| Timing Data | Millisecond-level performance metrics for each pipeline step |
| Output Path | The location of the final generated AI art |
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