Analyze images to generate highly detailed, reproduction-quality prompts for AI image generation across multiple categories.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install image2prompt
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 image2prompt 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 Image to Prompt?
The Image to Prompt skill is a professional-grade tool designed to bridge the gap between visual inspiration and technical execution. By leveraging advanced vision-capable models, this addition to Openclaw Skills allows developers and creators to reverse-engineer any image into a precise, descriptive prompt. It is engineered to handle various visual styles, from photorealistic portraits to technical illustrations, providing the level of detail necessary for high-fidelity image reproduction.
This skill is particularly valuable for those looking to standardize their image generation workflows. Whether you need a flowing natural language description for creative brainstorming or a strictly structured JSON schema for programmatic use, this component of Openclaw Skills delivers consistent, high-quality results across all major vision models.
Image to Prompt Use Cases
- Reverse-engineering existing artwork to understand the prompt structures required for similar outputs.
- Building a searchable database of visual assets with tagged metadata using Openclaw Skills.
- Creating training datasets for fine-tuning image generation models or LoRAs.
- Automating the generation of alt-text and highly descriptive metadata for digital asset management.
- Enhancing creative workflows by translating visual concepts into actionable text prompts for multi-agent systems.
How Image to Prompt Works
- The skill first performs category detection to classify the image into types like portrait, landscape, product, or illustration.
- It executes category-specific analysis to identify critical visual elements such as lighting, composition, camera settings, and subject details.
- The agent generates the requested output format, supporting both expansive natural language and structured data formats.
- If requested, it extracts specific dimensions, tagging phrases for colors, moods, styles, and technical attributes for easy parsing within Openclaw Skills.
Image to Prompt Setup
To utilize this skill, ensure you have a vision-capable API key configured and the core CLI tool installed. Openclaw Skills work best when your environment is properly authenticated.
# Ensure your environment variable is set
export OPENAI_API_KEY='your_api_key_here'
# Basic command to analyze an image
openclaw message send --image /path/to/image.jpg "Analyze this image and generate a detailed prompt for reproduction"
Image to Prompt Data Schema & Taxonomy
The skill organizes its output based on the specific category detected and the requested format. Below is the schema for structured portrait analysis:
| Attribute | Description |
|---|---|
| subject | Detailed identity, ethnicity, and facial features |
| pose | Body orientation, leg/hand positions, and gaze direction |
| lighting | Type, time of day, color temperature, and contrast |
| camera | Lens type (e.g., 85mm), angle, and depth of field |
| environment | Location details, ground type, and atmosphere |
| dimensions | Tagged arrays for backgrounds, objects, and themes |
Image to Prompt Advanced Features
- Specialized analysis modules for distinct categories including portraits, animals, and commercial products.
- Dimension extraction for building granular prompt databases with specific tags for lighting and mood.
- Support for high-resolution image processing to ensure no visual detail is overlooked by Openclaw Skills.
- Customizable output formats ranging from raw strings to deeply nested JSON for automated pipelines.
- Seamless compatibility with GPT-4 Vision, Claude 3 (Opus/Sonnet), and Gemini Pro Vision models.
SKILL.md
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