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:

• 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.

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

{
  "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 - Core analysis system prompt
• json-schema.md - Complete JSON output schema
• analysis-rules.md - Category-specific analysis rules
• 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