# Gpt Multimodal

Analyze images and multi-frame sequences using OpenAI GPT series

- Repository: https://github.com/benchflow-ai/skillsbench (1888 stars)
- Category: Engineering
- License: Apache-2.0
- Last updated: 2026-10-24
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add benchflow-ai/skillsbench --skill gpt-multimodal -g -a claude-code -y`
Codex: `npx skills add benchflow-ai/skillsbench --skill gpt-multimodal -g -a codex -y`
Cursor: `npx skills add benchflow-ai/skillsbench --skill gpt-multimodal -g -a cursor -y`
Gemini CLI: `npx skills add benchflow-ai/skillsbench --skill gpt-multimodal -g -a gemini-cli -y`

## Description

Analyze images and multi-frame sequences using OpenAI GPT series

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# OpenAI Vision Analysis Skill

## Purpose
This skill enables image analysis, scene understanding, text extraction, and multi-frame comparison using OpenAI's vision-capable GPT models (e.g., `gpt-4o`, `gpt-5`). It supports single and multiple images analysis and sequential frames for temporal analysis.

## When to Use
- Analyzing image content (objects, scenes, colors, spatial relationships)
- Extracting and reading text from images (OCR via vision models)
- Comparing multiple images to detect differences or changes
- Processing video frames to understand temporal progression
- Generating detailed image descriptions or captions
- Answering questions about visual content

## Required Libraries

The following Python libraries are required:

```python
from openai import OpenAI
import base64
import json
import os
from pathlib import Path
```

## Input Requirements
- **File formats**: JPG, JPEG, PNG, WEBP, non-animated GIF
- **Image quality**: Clear and legible; minimum 512×512px recommended
- **File size**: Under 20MB per image recommended
- **Maximum per request**: Up to 500 images, 50MB total payload
- **URL or Base64**: Images can be provided as URLs or base64-encoded data

## Output Schema
All analysis results should be returned as valid JSON conforming to this schema:

```json
{
  "success": true,
  "model": "gpt-5",
  "analysis": "Detailed description or analysis of the image content...",
  "metadata": {
    "image_count": 1,
    "detail_level": "high",
    "tokens_used": 850,
    "processing_time_ms": 1234
  },
  "extracted_data": {
    "objects": ["car", "person", "building"],
    "text_found": "Sample text from image",
    "colors": ["blue", "white", "gray"],
    "scene_type": "urban street"
  },
  "warnings": []
}
```

### Field Descriptions

The rest: https://github.com/benchflow-ai/skillsbench/blob/HEAD/tasks-extra/pedestrian-traffic-counting/environment/skills/gpt-multimodal/SKILL.md