Community indexed
为 Claude Code 赋能多模态视觉能力,支持豆包、通义千问、GPT-4o 等模型,用于截图 / UI / 图表分析;适配 DeepSeek 等无视觉底座,搭配 browser-harness 可做前端布局自动化检查。
A reusable skill for Claude Code that adds multimodal vision capabilities for analyzing screenshots, UI, and charts using various models.
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为 Claude Code 提供多模态视觉能力,支持多种视觉模型分析截图、UI、图表。
专为使用 DeepSeek 等无多模态能力的模型作为 Claude Code 底座的用户设计。
遇到 UI 报错、设计稿或数据图表?直接 alt + v 截图粘贴到 Claude Code 对话中,自动调用视觉模型分析。无需记住任何命令,像聊天一样自然。
配合 browser-harness 等工具,可实现自动截图 → AI 视觉分析 → 生成审查报告 → 修正页面的完整闭环。前端页面渲染后自动截图,由视觉模型检查布局、样式和交互问题。
| Provider | 模型 | API Key 环境变量 |
|---|---|---|
| 豆包 (Doubao) | doubao-seed-2-0-pro-260215 | DOUBAO_API_KEY |
| 通义千问 (Qwen) | qwen-vl-max | DASHSCOPE_API_KEY |
| OpenAI | gpt-4o | OPENAI_API_KEY |
| Claude (Anthropic) | claude-sonnet-5 | ANTHROPIC_API_KEY |
| 任意自定义 provider | 任意 | {NAME}_API_KEY |
不在上面四个内置厂商里的 --provider 名字,会按同一套命名约定动态解析:设置
{NAME}_API_KEY / {NAME}_BASE_URL / {NAME}_MODEL({NAME}_PROTOCOL 可选,
默认 openai,需要 Anthropic Messages API 形状时设为 anthropic),无需改一行代码即可接入任意 OpenAI 兼容端点(vLLM、Ollama、LiteLLM、OpenRouter、Azure OpenAI、自建代理等)。详见 vision/SKILL.md。
需要 Python 3.10+。
pip install -r requirements.txt
anthropic 包仅在使用 --provider anthropic 时才会被导入,其他 provider 不受影响。
When the user asks you to install this skill, follow these steps IN ORDER. Do NOT skip any step.
使用 AskUserQuestion 询问以下信息:
python install.py \
--api-key qwen:sk-xxx \
--default-provider qwen \
--merge-claude
--api-key 可重复,--merge-claude 必须带上。
如果用户选了自定义 provider,额外带上 --base-url / --model(--protocol 可选,默认 openai):
python install.py \
--api-key myapi:sk-xxx \
--base-url myapi:https://host/v1 \
--model myapi:my-vision-model \
--default-provider myapi \
--merge-claude
如果未使用 `--merge-cla
# Claude Code Vision Skill
为 Claude Code 提供多模态视觉能力,支持多种视觉模型分析截图、UI、图表。
专为使用 DeepSeek 等无多模态能力的模型作为 Claude Code 底座的用户设计。
## 截图直贴,所见即所析
遇到 UI 报错、设计稿或数据图表?**直接 alt + v 截图粘贴到 Claude Code 对话中,自动调用视觉模型分析**。无需记住任何命令,像聊天一样自然。
## 串联工具链,自动 UI 审查
配合 **browser-harness** 等工具,可实现**自动截图 → AI 视觉分析 → 生成审查报告 → 修正页面**的完整闭环。前端页面渲染后自动截图,由视觉模型检查布局、样式和交互问题。
## 支持的模型
| Provider | 模型 | API Key 环境变量 |
|----------|------|-----------------|
| 豆包 (Doubao) | doubao-seed-2-0-pro-260215 | `DOUBAO_API_KEY` |
| 通义千问 (Qwen) | qwen-vl-max | `DASHSCOPE_API_KEY` |
| OpenAI | gpt-4o | `OPENAI_API_KEY` |
| Claude (Anthropic) | claude-sonnet-5 | `ANTHROPIC_API_KEY` |
| **任意自定义 provider** | 任意 | `{NAME}_API_KEY` |
不在上面四个内置厂商里的 `--provider` 名字,会按同一套命名约定动态解析:设置
`{NAME}_API_KEY` / `{NAME}_BASE_URL` / `{NAME}_MODEL`(`{NAME}_PROTOCOL` 可选,
默认 `openai`,需要 Anthropic Messages API 形状时设为 `anthropic`),无需改一行代码即可接入任意 OpenAI 兼容端点(vLLM、Ollama、LiteLLM、OpenRouter、Azure OpenAI、自建代理等)。详见 [vision/SKILL.md](vision/SKILL.md#any-custom-provider)。
## 安装依赖
需要 Python 3.10+。
```bash
pip install -r requirements.txt
```
`anthropic` 包仅在使用 `--provider anthropic` 时才会被导入,其他 provider 不受影响。
---
## Claude Code 安装流程(给 Claude Code 看的指令)
> **When the user asks you to install this skill, follow these steps IN ORDER. Do NOT skip any step.**
### Step 1 — 询问用户
使用 AskUserQuestion 询问以下信息:
1. **选择 provider**:doubao / qwen / openai / anthropic / 自定义(可多选;自定义需额外要 base URL、model,可选 protocol)
2. **API Key**:每个 provider 的 API key
3. **默认 provider**(多选时):选哪个作为默认
### Step 2 — 运行安装脚本
```bash
python install.py \
--api-key qwen:sk-xxx \
--default-provider qwen \
--merge-claude
```
`--api-key` 可重复,`--merge-claude` **必须带上**。
如果用户选了自定义 provider,额外带上 `--base-url` / `--model`(`--protocol` 可选,默认 openai):
```bash
python install.py \
--api-key myapi:sk-xxx \
--base-url myapi:https://host/v1 \
--model myapi:my-vision-model \
--default-provider myapi \
--merge-claude
```
### Step 3 — 合并 CLAUDE.md(如果 install.py 未自动完成)
如果未使用 `--merge-claSource structure unverified
A repository listing is not proof of an installable skill. Review its instructions before proposing any installation.
Review before install: Avoid automatic install
License: MIT
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
74/100
Strong
Trust
62/100
Sandbox only
Audit
77
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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}
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