Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Image2Prompt
Analyze images and generate detailed prompts for image generation. Supports portrait, landscape, product, animal, illustration categories with structured or natural output.
skill openclawclawhub Free
Analyze images and generate detailed prompts for image generation. Supports portrait, landscape, product, animal, illustration categories with structured or natural output.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- Zhang-Shubo
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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.
Basic Analysis
# 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.
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
Natural Language Output (Portrait)
{ "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)
{ "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
{ "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
High-resolution images produce more detailed prompts Clear, well-lit images yield better category detection Request structured output when you need programmatic access to individual elements Use dimensions extraction when building prompt databases or training data 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
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md