图片分析与识别,可分析本地图片、网络图片、视频、文件。适用于 OCR、物体识别、场景理解等。当用户发送图片或要求分析图片时必须使用此技能。
npx skills add https://github.com/countbot-ai/CountBot --skill image-analysis
支持智谱 GLM-4V 和千问 Qwen-VL 两种视觉模型。
当用户发送图片或要求分析图片时,必须使用此技能,不要使用 PIL、pytesseract 等其他方法。
编辑 skills/image-analysis/scripts/config.json:
{
"default_model": "zhipu",
"zhipu": {
"api_key": "your-zhipu-api-key",
"model": "glm-4.6v-flash"
},
"qwen": {
"api_key": "your-qwen-api-key",
"model": "qwen3-vl-plus"
}
}
API Key 获取:
# 分析本地图片(最常用)
python3 skills/image-analysis/scripts/vision.py analyze --image 图片路径 --prompt "描述图片内容"
# 分析网络图片
python3 skills/image-analysis/scripts/vision.py analyze --image https://example.com/image.jpg --prompt "描述图片"
# 多图对比
python3 skills/image-analysis/scripts/vision.py analyze --image img1.jpg --image img2.jpg --prompt "对比差异"
# 指定模型
python3 skills/image-analysis/scripts/vision.py analyze --image image.jpg --prompt "描述图片" --model qwen
# 开启思考模式(仅智谱,提升准确度)
python3 skills/image-analysis/scripts/vision.py analyze --image image.jpg --prompt "详细分析" --thinking
# 视频分析
python3 skills/image-analysis/scripts/vision.py analyze --video video.mp4 --prompt "总结视频内容"
# JSON 输出
python3 skills/image-analysis/scripts/vision.py analyze --image image.jpg --prompt "描述图片" --json
用户发送图片后,系统下载到本地(如 data/temp/images/xxx.jpg):
# 图片描述
python3 skills/image-analysis/scripts/vision.py analyze --image data/temp/images/xxx.jpg --prompt "描述这张图片的内容"
# OCR 识别
python3 skills/image-analysis/scripts/vision.py analyze --image data/temp/images/xxx.jpg --prompt "提取图片中的所有文字信息"
# 物体定位(开启思考模式)
python3 skills/image-analysis/scripts/vision.py analyze --image data/temp/images/xxx.jpg --prompt "找出物体位置,返回坐标" --thinking
| 场景 | 推荐 |
|------|------|
| 简单描述 | 任意 |
| 复杂推理、物体定位 | 智谱 + --thinking |
| 高精度识别、文档解析 | 千问 |
| 成本敏感 | 智谱(免费) |
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take countbot-ai/image-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.