Compensatory vision tools based on blindspots measured by vision diagnostic v1-v4 on 2026-03-25. Which model it ran on is not recorded here and it has not been re-run since, so read the thresholds as measured-then: they say a tool exists for each failure, not that the current model fails at exactly that number. Re-run the diagnostic before relying on a specific threshold.
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)
Measured, not guessed:
| 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. Call it first: 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.
Accuracy Notes
Call grid() first on a complex image. Verify colors near context boundaries
with sample() or isolate(), counts above 15 with count_elements(),
gradients with gradient_map(), and faint features with enhance() before
describing them. Each of these is a row of the blindspot table, so the tool
call is the evidence — perception alone is not.