# Gzupark Claude Plugin Pack Image Insight > Image Insight - Skill: `tomevault-io/gzupark-claude-plugin-pack-image-insight` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/gzupark-claude-plugin-pack-image-insight` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/gzupark-claude-plugin-pack-image-insight/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/gzupark-claude-plugin-pack-image-insight --- # Image Insight ## Overview Analyze uploaded images and return structured JSON profiles containing composition, color, lighting, subject, and background analysis with actionable recreation parameters for AI image generation. ## Triggers - `image-insight` - Primary trigger for image analysis - "analyze this image" - Natural language trigger - "extract visual style" - Style extraction request - "generate image profile" - Profile generation request - "what's in this image" - Detailed breakdown request ## Workflow ### Step 1: Receive Image Accept the uploaded image file. Verify it's a valid image format. ### Step 2: Multi-Category Analysis Analyze across all schema categories: 1. **metadata** - Confidence, image type, purpose 2. **composition** - Rule, layout, focal points, hierarchy 3. **color_profile** - Dominant colors with hex, palette, temperature 4. **lighting** - Type, direction, shadows, highlights 5. **technical_specs** - Medium, style, texture, depth of field 6. **artistic_elements** - Genre, influences, mood, atmosphere 7. **typography** - Fonts, placement (if text present) 8. **subject_analysis** - Expression, hair, hands, positioning 9. **background** - Setting, surfaces, objects catalog 10. **generation_parameters** - Recreation prompts, keywords ### Step 3: Apply Critical Area Rules For portraits, apply detailed analysis per [references/critical-areas.md](references/critical-areas.md): - Hair: exact length, cut style, natural imperfections - Hands: each hand separately, finger positions, tension - Background: wall material distinction (drywall vs concrete vs brick) - Lighting: directionality, shadow characteristics ### Step 4: Generate JSON Output Return structured JSON following [references/json-schema.md](references/json-schema.md). **Output requirements:** - Valid JSON only - no markdown, no commentary - All sections populated with specific values - Hex codes for colors - Actionable generation prompts ## Quick Reference ### Color Profile ```json { "color": "coral pink", "hex": "#FF7F7F", "percentage": "35%", "role": "primary subject" } ``` ### Lighting Assessment - **Directional**: Strong shadows, sculpted appearance - **Diffused**: Soft minimal shadows, even illumination - Assess: type, direction, shadow edge quality, contrast ratio ### Subject Analysis Priorities 1. Facial expression: mouth, eyes, emotion, authenticity 2. Hair: length, cut, texture, natural imperfections 3. Hands: position, tension, naturalness 4. Body: posture, angle, weight distribution ## Resources ### references/ - [core-prompt.md](references/core-prompt.md) - Core analysis system prompt - [json-schema.md](references/json-schema.md) - Complete JSON output schema - [analysis-rules.md](references/analysis-rules.md) - Category-specific analysis rules - [critical-areas.md](references/critical-areas.md) - Hair, hands, background, lighting details ### scripts/ - `validate_output.py` - Validate JSON structure and completeness ## Anti-Patterns - **Vague descriptions**: Avoid "nice", "good", "beautiful" - use specific technical terms - **Perfect hair**: Never describe hair as "perfect" - real hair has flyaways, frizz, variation - **Generic backgrounds**: Don't say "wall" - specify material (painted drywall, concrete, brick) - **Skipped hands**: Always document hand positions even if hidden or out of frame - **Markdown in output**: Output pure JSON only - no code blocks, no explanatory text ## Extension Points 1. **Image Type Variants**: Create specialized schemas for landscapes, products, architecture 2. **Selective Analysis**: Add parameter to request specific categories only 3. **Batch Processing**: Extend for analyzing multiple images in sequence 4. **Confidence Thresholds**: Add configurable confidence scoring criteria ## Design Rationale This skill encapsulates 15+ years of visual analysis expertise to: 1. Enable consistent, reproducible image analysis across different contexts 2. Generate actionable prompts for AI image recreation (Midjourney, DALL-E, etc.) 3. Provide structured data for downstream processing and automation 4. Standardize style extraction with emphasis on natural imperfections over idealized descriptions 5. Support multimodal analysis leveraging Claude's vision capability --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/gzupark) — claim your Tome and manage your conversions.