Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add glebis/claude-skills --skill vision-benchgit clone --depth 1 https://github.com/glebis/claude-skillsWrote 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/glebis/claude-skills/vision-bench)<a href="https://agentmods.dev/skills/glebis/claude-skills/vision-bench"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/vision-bench/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/glebis/claude-skills/vision-bench"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/vision-bench.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 78 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00086 | $0.00906 |
| Opus 5 | $0.00043 | $0.00453 |
| Sonnet 5 | $0.00017 | $0.00181 |
| Haiku 4.5 | $0.00009 | $0.00091 |
Grade A, and why
vision-bench 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 6d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vision Bench — LLM Image Evaluation
Compare images by scoring them with one or more vision LLM judges against structured rubric criteria.
Quick Start
# Install dependencies
pip install pyyaml openai anthropic mistralai
# Score a single image
python bench.py image.png --criteria photorealism --judge gemini-2.5-flash
# Compare two AI-generated images
python bench.py img_a.png img_b.png \
--criteria text_to_image \
--prompt "a fox in a snowy forest" \
--judge gpt-4o
# Multi-judge consensus
python bench.py img.png \
--criteria portrait \
--judges gpt-4o gemini-2.5-flash claude-opus-4-5-20251022
# OpenRouter models (any vision-capable model)
python bench.py img_a.png img_b.png \
--criteria artistic_style \
--judges "openrouter/meta-llama/llama-4-maverick" "openrouter/mistralai/pixtral-large-2411"
# List all presets
python bench.py --list-presets
# Save report to file
python bench.py img.png --criteria chart_analysis --save report.md
Presets
| Preset | Use Case |
|---|---|
text_to_image |
Compare AI image generators (Midjourney, DALL-E, Flux) |
photorealism |
How convincingly an image looks like a photo |
artistic_style |
Style consistency, composition, color harmony |
portrait |
AI-generated portrait quality and realism |
product_photo |
E-commerce product image quality |
document_ocr |
Document text extraction and layout understanding |
chart_analysis |
Chart and data visualization comprehension |
invoice |
Financial document field extraction accuracy |
ui_screenshot |
App/web screenshot understanding |
scientific |
Scientific/medical image accuracy |
alt_text |
Accessibility image description quality |
Custom criteria: pass any .yaml file as --criteria path/to/my.yaml.
Judge Providers
| Prefix | Provider | Example |
|---|---|---|
gpt-, o1, o3, o4 |
OpenAI | gpt-4o |
claude- |
Anthropic | claude-sonnet-4-5-20251022 |
gemini- |
Google Gemini | gemini-2.5-flash |
pixtral-, mistral-, ministral- |
Mistral | pixtral-12b-2409 |
openrouter/ |
OpenRouter (any model) | openrouter/meta-llama/llama-4-maverick |
What ships with it
19 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.
- .claude-plugin/plugin.json 294 B
- .gitignore 59 B
- .sops.yaml 112 B
- bench.py 2.8 KB runs code
- criteria/alt_text.yaml 2.0 KB
- criteria/artistic_style.yaml 1.8 KB
- criteria/chart_analysis.yaml 1.7 KB
- criteria/document_ocr.yaml 1.7 KB
- criteria/invoice.yaml 1.9 KB
- criteria/photorealism.yaml 1.9 KB
- criteria/portrait.yaml 1.9 KB
- criteria/product_photo.yaml 1.8 KB
- criteria/scientific.yaml 1.8 KB
- criteria/text_to_image.yaml 1.9 KB
- criteria/ui_screenshot.yaml 1.8 KB
- judge.py 7.3 KB runs code
- report.py 2.4 KB runs code
- requirements.txt 55 B
- vault.py 1.3 KB runs code
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.
- 6d ago First seen · 94 lines · 86 tokens per session scan A 3cf25b03deff
vision-bench is a skill published in the GitHub repository glebis/claude-skills (374 stars, last pushed 8d ago), licensed MIT. It adds 86 tokens to every session and 906 once invoked, about $0.0004 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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