According to AlphaMoat, as of 2026-09, Image2Prompt has 4,758 downloads and 9 stars — ranked #1,987 of 5,078 AI skills overall, and #147 of 362 in Content.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 146 | 4.3K | 5 | - |
| SkillHub ↗ | 319 | 4.8K | 9 | — |
Platforms count differently and update at different times.
It classifies an image into categories such as portrait, landscape, product, animal, or illustration, then produces natural-language or structured JSON prompts, with optional dimension tags for building prompt libraries. It is meant to be used with any vision-capable model.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 4.8K | — | 9 |
| 2026-08 | 4.3K | +3.1% | 9 |
| 2026-07 | 4.2K | +7.1% | 9 |
| 2026-06 | 3.9K | +4.7% | 7 |
| 2026-05 | 3.7K | — | 7 |
| 2026-03 | 3.2K | — | 5 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
---
name: image2prompt
description: Analyze images and generate detailed prompts for image generation. Supports portrait, landscape, product, animal, illustration categories with structured or natural output.
homepage: https://docs.openclaw.ai/tools/image2prompt
user-invocable: true
metadata: {"openclaw":{"emoji":"🖼️","primaryEnv":"OPENAI_API_KEY","requires":{"anyBins":["openclaw"]}}}
---
# 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
Detects and removes AI writing traces so text reads more like a person wrote it.
A writing-editor skill that finds the usual marks of AI-generated text and rewrites them into more natural prose without losing the meaning.
A Chinese copy-editing skill that spots and removes the traces of AI-generated text.
wechat-search finds popular WeChat articles and organizes results by relevance, popularity and recency.
Extracts titles, descriptions, and spoken scripts from public short-video share links, with rewritten versions.
humanizer guides natural expression and style editing for Chinese and English text.
Writes web-novel chapters into files and replies in chat with summaries and pointers only.
Bundle a set of marketing playbooks into one skill and route each request to the module that fits.
Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.