What problem does it solve?
AI-generated video and images often look washed out, flat, or color-cast, and judging color by eye on a contact sheet is unreliable. This Skill replaces guesswork with objective measurement: it reads black/white points, contrast, saturation, and clipping statistics, then prescribes the correct fix (contrast stretch vs. reference color-match) and verifies the result by re-measuring.
Core Features & Use Cases
- Objective color analysis: Uses the get_image (action:"analyze_color") MCP tool to read scopes-like stats (blackPoint, whitePoint, contrast, saturation, cast, clipping) and heuristic flags such as washedOut and lowContrast.
- Measure-fix-remeasure loop: Diagnoses the washed-out signature, applies core ComfyUI nodes like AdjustContrast or CurveEditor, tunes the factor against measured targets, and confirms with a second measurement.
- Side-by-side sandbox testing: Builds a small comparison workflow that renders multiple candidate corrections (contrast factors, ColorMatchV2, ImageColorMatchAdobe+) in one run for numeric comparison.
- Use Case: A WAN-Animate render looks milky and dull. Extract a middle frame with cv2, measure it (blackPoint 45, whitePoint 191, contrast 43), apply AdjustContrast at ~1.4 after WanVideoDecode, and re-measure to confirm whitePoint near 250 with under 2% clipped highlights.
Quick Start
Ask the agent to analyze the color of my latest ComfyUI render with get_image analyze_color and fix it if it looks washed out.