Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add oaustegard/claude-skills/plugin install media-processingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oaustegard/claude-skills/seeing-images)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/seeing-images"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/seeing-images/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oaustegard/claude-skills/seeing-images"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/seeing-images.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00070 | $0.01277 |
| Opus 5 | $0.00035 | $0.00639 |
| Sonnet 5 | $0.00014 | $0.00255 |
| Haiku 4.5 | $0.00007 | $0.00128 |
Grade A, and why
seeing-images scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Seeing Images
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
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago Changed · +4 lines 1388304951ed
- 9d ago First seen · 103 lines · 70 tokens per session scan A a3a4e15b53e0
seeing-images is a skill published in the GitHub repository oaustegard/claude-skills (148 stars, last pushed yesterday), licensed MIT. It adds 70 tokens to every session and 1,277 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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