vision-bench
Score and compare images using vision LLMs as judges. YAML-defined criteria presets for 11 use cases (text-to-image, photorealism, document OCR, charts, UI, portrait, product, scientific, invoice, alt-text, artistic style). Supports OpenAI, Anthropic, Gemini, Mistral, and OpenRouter as judge providers. Keys auto-decrypted via SOPS + age.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/glebis/claude-skills --skill vision-bench- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads vision-bench automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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 |
API Keys
Keys are loaded from secrets.enc.yaml (SOPS + age encrypted) with fallback to environment variables.
Supported keys: OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, OPENROUTER_API_KEY
To encrypt your own keys:
sops --config .sops.yaml --encrypt --input-type yaml --output-type yaml secrets.yaml > secrets.enc.yaml
Output Formats
--output markdown (default) · --output json · --output table
Files
bench.py— CLI entry pointjudge.py— Multi-provider LLM judge logicreport.py— Report generationvault.py— SOPS secrets decryptioncriteria/— 11 YAML preset files.sops.yaml— Age key config for encryptionsecrets.enc.yaml— Encrypted API keys
FAQ
What does the vision-bench skill do?
Score and compare images using vision LLMs as judges. YAML-defined criteria presets for 11 use cases (text-to-image, photorealism, document OCR, charts, UI, portrait, product, scientific, invoice, alt-text, artistic style). Supports OpenAI, Anthropic, Gemini, Mistral, and OpenRouter as judge providers. Keys auto-decrypted via SOPS + age.
How do I install vision-bench?
Run npx skills add https://github.com/glebis/claude-skills --skill vision-bench in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/vision-bench/ (personal) or .claude/skills/vision-bench/ (project).
Which AI agents can use vision-bench?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is vision-bench safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains vision-bench?
It is published by glebis in glebis/claude-skills. It has 172 recorded installs.
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