seeing-images

name	seeing-images
description	Augmented vision tools for analyzing images beyond native visual capabilities. Use when tasked with describing images in detail, reproducing images as SVGs, identifying subtle features, comparing image regions, reading degraded text, or any task requiring careful visual inspection. Also use when the image-to-svg skill needs ground truth about colors, shapes, or boundaries.
metadata	{"version":"1.0.0"}

Seeing Images

Compensatory vision tools based on empirically measured blindspots (vision diagnostic v1-v4, 2026-03-25).

When to Use

Activate this skill when:

• Describing an uploaded image in detail
• Reproducing an image as SVG (use BEFORE drawing to establish ground truth)
• Comparing two images or regions for differences
• Reading text in degraded/compressed/low-contrast images
• Identifying subtle features (gradients, faint overlays, reflections)
• Any image task where accuracy matters more than speed

Known Blindspots (from diagnostics)

These are MEASURED limitations — not guesses:

Blindspot	Threshold	Compensatory Tool
Luminance contrast	~15-20 RGB steps invisible	enhance, histogram, sample
Gradients	<30-step range invisible	gradient_map, enhance
Context color bias	Dress effect, simultaneous contrast	isolate, sample
Small elements	<15px effectively invisible	crop, grid
Dense counting	Degrades >15 items, ~50% error at 30	count_elements
Subtle atmospherics	Steam, faint reflections lost in noise	enhance, denoise

Workflow

Setup (one line, every time)

import sys; sys.path.insert(0, '/mnt/skills/user/seeing-images/scripts')
from see import grid, sample, enhance, edges, histogram, isolate, palette, compare, count_elements, gradient_map, denoise, crop

Quick Analysis (2-3 tool calls)

grid(path, rows=2, cols=2)   # → view the output
sample(path, [(x1,y1), ...]) # → verify colors at points of interest

Deep Analysis (for SVG reproduction, spot-the-difference, etc.)

grid(path, rows=3, cols=3)                    # 1. Overview
palette(path, n=10)                           # 2. Dominant colors
edges(path, threshold=30)                     # 3. Shape boundaries
sample(path, [(x1,y1), (x2,y2), ...])        # 4. Exact RGB at points
enhance(path, region=(x,y,w,h), mode='auto')  # 5. Reveal low-contrast areas
isolate(path, region=(x,y,w,h))              # 6. Remove context bias

Tool Reference

All functions in scripts/see.py. Every function that produces an image saves to /home/claude/see_*.png and returns the path. Use view tool on the returned path.

grid(path, rows=3, cols=3, labels=True)

Splits image into labeled cells for systematic inspection. This is the FIRST thing to call — it reduces attentional competition.

sample(path, points, radius=3)

Returns exact RGB values at specified pixel coordinates. Use to verify what you think you see. Averages over a small radius to handle noise.

histogram(path, region=None)

Color histogram showing value distribution. Reveals bimodal distributions (hidden gradients), dominant colors, and contrast range. With region=(x,y,w,h), analyzes only that area.

enhance(path, region=None, factor=2.0, mode='contrast')

Boosts contrast in the image or a region. Modes: 'contrast', 'brightness', 'color', 'sharpness'. Use factor=3-5 for near-threshold features.

edges(path, threshold=50)

Sobel edge detection revealing shape boundaries invisible at low contrast. Lower threshold = more edges (noisier). Output is a white-on-black edge map.

gradient_map(path, region=None)

Computes local gradient magnitude across the image. Bright = high gradient, dark = flat. Reveals gradients below the 30-step detection threshold.

isolate(path, region, padding=20, bg=(128,128,128))

Extracts a region and places it on a neutral gray background. Removes surrounding context that causes simultaneous contrast and Dress-type illusions. The bg parameter defaults to mid-gray to minimize context bias.

compare(path, r1, r2)

Side-by-side comparison of two regions with diff overlay. Highlights pixel-level differences with amplification. Use for spot-the-difference tasks.

count_elements(path, region=None, color_range=None, min_size=3)

Programmatic element counting using connected component analysis. Specify approximate color_range as ((r_min,g_min,b_min), (r_max,g_max,b_max)) to count specific colored elements.

denoise(path, region=None, strength=3)

Median filter to reduce photographic noise, revealing subtle features hidden in the noise floor (like steam, faint reflections).

palette(path, n=8)

Extracts the n most dominant colors using k-means clustering. Returns RGB values and their proportions. Essential for SVG reproduction.

Anti-Patterns

• Do NOT skip grid() for complex images — your attention is the bottleneck
• Do NOT trust your color perception near context boundaries — always sample() or isolate()
• Do NOT estimate counts above 15 — use count_elements()
• Do NOT assume gradients are flat — use gradient_map() to verify
• Do NOT describe faint features without enhance() verification