Introduction
# Image to Prompt
Analyze images and generate detailed, reproduction-quality prompts for AI image generation.
## Workflow
**Step 1: Category Detection** First, classify the image into one of these categories: - `portrait` — People as main subject (photos, artwork, digital art) - `landscape` — Natural scenery, cityscapes, architecture, outdoor environments - `product` — Commercial product photos, merchandise - `animal` — Animals as main subject - `illustration` — Diagrams, infographics, UI mockups, technical drawings - `other` — Images that don't fit above categories
**Step 2: Category-Specific Analysis** Generate a detailed prompt based on the detected category.
## Usage
### Basic Analysis
```bash # Analyze an image (auto-detect category) openclaw message send --image /path/to/image.jpg "Analyze this image and generate a detailed prompt for reproduction" ```
### Specify Output Format
**Natural Language (default):** ``` Analyze this image and write a detailed, flowing prompt description (600-1000 words for portraits, 400-600 for others). ```
**Structured JSON:** ``` Analyze this image and output a structured JSON description with all visual elements categorized. ```
### With Dimensions Extraction
Request dimension highlights to get tagged phrases for each visual aspect: ``` Analyze this image with dimension extraction. Tag phrases for: backgrounds, objects, characters, styles, actions, colors, moods, lighting, compositions, themes. ```
## Category-Specific Elements
### Portrait Analysis Covers: - **Model/Style**: Photography type, quality level, visual style - **Subject**: Gender, age, ethnicity, skin tone, body type - **Facial Features**: Eyes, lips, face shape, expression - **Hair**: Color, length, style, part - **Pose**: Body position, orientation, leg/hand positions, gaze - **Clothing**: Type, color, pattern, fit, material, style - **Accessories**: Jewelry, bags, hats, etc. - **Environment**: Location, ground, background, atmosphere - **Lighting**: Type, time of day, shadows, contrast, color temperature - **Camera**: Angle, height, shot type, lens, depth of field, perspective - **Technical**: Realism, post-processing, resolution
### Landscape Analysis Covers: - Terrain and water features - Sky and atmospheric elements - Foreground/background composition - Natural lighting and atmosphere - Color palette and photography style
### Product Analysis Covers: - Product features and materials - Design elements and shape - Staging and background - Studio lighting setup - Commercial photography style
### Animal Analysis Covers: - Species identification and markings - Pose and behavior - Expression and character - Habitat and setting - Wildlife/pet photography style
### Illustration Analysis Covers: - Diagram type (flowchart, infographic, UI, etc.) - Visual elements (icons, shapes, connectors) - Layout and hierarchy - Design style (flat, isometric, etc.) - Color scheme and meaning
## Output Examples
### Natural Language Output (Portrait) ```json { "prompt": "A stunning photorealistic portrait of a young woman in her mid-20s with fair porcelain skin and warm pink undertones. She has striking emerald green almond-shaped eyes with long dark lashes, full rose-colored lips curved in a subtle confident smile, and an oval face with high cheekbones..." } ```
### Structured Output (Portrait) ```json { "structured": { "model": "photorealistic", "quality": "ultra high", "style": "cinematic natural light photography", "subject": { "identity": "young beautiful woman", "gender": "female", "age": "mid 20s", "ethnicity": "European", "skin_tone": "fair porcelain with pink undertones", "body_type": "slim athletic", "facial_features": { "eyes": "emerald green, almond-shaped, intense gaze", "lips": "full, rose pink, subtle smile", "face_shape": "oval with high cheekbones", "expression": "confident and serene" }, "hair": { "color": "warm honey blonde", "length": "long", "style": "soft waves", "part": "center" } }, "pose": { "position": "standing", "body_orientation": "three-quarter turn to camera", "legs": "weight on right leg, relaxed stance", "hands": { "right_hand": "resting on hip", "left_hand": "hanging naturally at side" }, "gaze": "direct eye contact with camera" }, "clothing": { "type": "flowing maxi dress", "color": "dusty rose", "pattern": "solid", "details": "V-neckline, cinched waist, silk material", "style": "romantic feminine" }, "accessories": ["delicate gold necklace", "small hoop earrings"], "environment": { "location": "outdoor garden", "ground": "cobblestone path", "background": "blooming roses, soft bokeh", "atmosphere": "dreamy and romantic" }, "lighting": { "type": "natural sunlight", "time": "golden hour", "shadow_quality": "soft diffused shadows", "contrast": "medium", "color_temperature": "warm" }, "camera": { "angle": "slightly below eye level", "camera_height": "chest height", "shot_type": "medium shot", "lens": "85mm", "depth_of_field": "shallow", "perspective": "slight compression, flattering" }, "mood": "romantic, confident, ethereal", "realism": "highly photorealistic", "post_processing": "soft color grading, subtle glow", "resolution": "8k" } } ```
### With Dimensions ```json { "prompt": "...", "dimensions": { "backgrounds": ["outdoor garden", "blooming roses", "soft bokeh"], "objects": ["delicate gold necklace", "small hoop earrings"], "characters": ["young beautiful woman", "mid 20s", "European"], "styles": ["photorealistic", "cinematic natural light photography"], "actions": ["standing", "three-quarter turn", "direct eye contact"], "colors": ["dusty rose", "honey blonde", "emerald green"], "moods": ["romantic", "confident", "ethereal", "dreamy"], "lighting": ["golden hour", "natural sunlight", "soft diffused shadows"], "compositions": ["medium shot", "85mm", "shallow depth of field"], "themes": ["romantic feminine", "portrait photography"] } } ```
## Tips for Best Results
1. **High-resolution images** produce more detailed prompts 2. **Clear, well-lit images** yield better category detection 3. **Request structured output** when you need programmatic access to individual elements 4. **Use dimensions extraction** when building prompt databases or training data 5. **Specify word count expectations** for natural language output if needed
## Integration
This skill works with any vision-capable model. For best results, use: - GPT-4 Vision - Claude 3 (Opus/Sonnet) - Gemini Pro Vision