Agent-Skills.md

Agent Skills: Vision Bench — LLM Image Evaluation

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.

UncategorizedID: glebis/claude-skills/vision-bench

Repository

glebis/claude-skills
glebisLicense: MIT
37956

Install this agent skill to your local

pnpm dlx add-skill https://github.com/glebis/claude-skills/tree/HEAD/vision-bench

Skill Files

Browse the full folder contents for vision-bench.

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vision-bench/SKILL.md

Skill Metadata

Name
vision-bench
Description
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.

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 point
  • judge.py — Multi-provider LLM judge logic
  • report.py — Report generation
  • vault.py — SOPS secrets decryption
  • criteria/ — 11 YAML preset files
  • .sops.yaml — Age key config for encryption
  • secrets.enc.yaml — Encrypted API keys