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Harness-native integration for agent-vision-toolkit: image Q&A, OCR, grounding, UI restoration, pixel diff, Artifacts, and Web UI.","maintainers":[{"name":"anionex","email":"davidyang042@gmail.com"}],"readme":"<p align=\"center\">\n  <img src=\"assets/hero-v2.png\" alt=\"DSH Vision Toolkit：让纯文本 DeepSeek Harness Agent 看懂图片并完成视觉任务\" />\n</p>\n\n<div align=\"center\">\n\n# DSH Vision Toolkit\n\n<a href=\"https://trendshift.io/repositories/149708?utm_source=trendshift-badge&amp;utm_medium=badge&amp;utm_campaign=badge-trendshift-149708\" target=\"_blank\" rel=\"noopener noreferrer\"><img src=\"https://trendshift.io/api/badge/trendshift/repositories/149708/daily?language=TypeScript\" alt=\"Anionex%2Fdsh-vision-toolkit | Trendshift\" width=\"250\" height=\"55\"/></a>\n\n[![由 dshfind 推荐](https://img.shields.io/badge/%E7%94%B1%20dshfind-%E6%8E%A8%E8%8D%90-FFD700?style=flat-square)](https://dshfind.com/zh/plugins/Anionex/dsh-vision-toolkit)\n[![dshfind 评分：94——最高分插件](https://img.shields.io/badge/dshfind%20%E8%AF%84%E5%88%86-94%20%7C%20%E6%9C%80%E9%AB%98%E5%88%86%E6%8F%92%E4%BB%B6-5B4CF0?style=flat-square)](https://dshfind.com/zh/plugins/Anionex/dsh-vision-toolkit)\n[![npm](https://img.shields.io/npm/v/@anionex/dsh-vision-toolkit?style=flat-square&color=5B4CF0)](https://www.npmjs.com/package/@anionex/dsh-vision-toolkit)\n\n[![MIT](https://img.shields.io/badge/license-MIT-0B7285?style=flat-square)](LICENSE)\n[![DSH](https://img.shields.io/badge/DSH-Web%20%2B%20Headless-5B4CF0?style=flat-square)](cordis.patch.yml)\n\n**更强大的视觉工具箱——给 DeepSeek Harness 里的纯文本模型装上眼睛：图片问答、长图 OCR、前端 UI 还原、GUI 视觉任务，一套视觉工具箱和一个 Skill。**\n\n🚀 粘贴图片，直接提问 ｜ 一行命令安装即用 ｜ 场景丰富\n\n[亮点](#亮点) ｜ [快速开始](#快速开始三步完成) ｜ [工具一览](#工具一览) ｜ [配置与限制](#配置与限制) ｜ [常见问题](#常见问题) ｜ [交流群](#开发与社区)\n\n🌐 [English](README.md) ｜ **中文**\n\n</div>\n\n🏆 本项目为deepseek harness生态首个综合性视觉工具插件：内测前已立项，并在内测期间参考本人的[`agent-vision-toolkit`](https://github.com/Anionex/agent-vision-toolkit)做出。\n\n> **原创声明：** 这套视觉工具的体系和划分方式，以及 `vision-skills` Skill，均由作者个人原创并持续打磨，相关工具、方法和工作流来自长期的真实使用与反复迭代。\n\n> 如果这个项目对你有帮助，或给了你一些灵感，欢迎 Star 🌟 & Fork。\n\n## 亮点\n\n- **粘贴图片，直接提问。** 在 DSH Web 里粘贴图片，文本模型会自动切换到看图模式变体，不需要手动复制路径或更换模型。图片保留原生缩略图、会话记录和工作区路径；Web 可以预览产物。\n- **一行命令安装即用。** 安装插件后即可在 **设置 → 视觉工具** 中配置视觉模型并开始使用。\n- **不只是看图描述，是获取图中真正需要关注的内容。** 模型不只是生成通用描述，而是围绕“报错在哪里”“按钮在哪”等当前任务提取证据。\n- **一套经过实战验证的视觉任务方法论**：项目提供的skill，会告诉 agent 面对不同视觉任务时应该看什么、选择哪个工具、按什么步骤推进，以及最后如何验证结果。\n\n[`agent-vision-toolkit`](https://github.com/Anionex/agent-vision-toolkit) 的视觉能力不只停留在图片描述：Agent 可以读取、定位、裁剪、描摹、还原和验证视觉内容。DSH Vision Toolkit 是这套工具箱面向 DeepSeek Harness 的原生接入，让它进入 Web 和 Headless Profile。\n\n本项目提供两层能力：\n\n1. **视觉工具和 Skill**：让 Agent 知道什么时候该看图、定位、OCR、裁剪、描摹或做像素对比。\n2. **DSH 原生接入**：把这些能力放进 Profile、会话、Settings、Artifacts 和 Web 界面。\n\n```sh\ndsh plugin --profile web add @anionex/dsh-vision-toolkit\n```\n\n**上游工具箱：** [Anionex/agent-vision-toolkit](https://github.com/Anionex/agent-vision-toolkit) · **项目网站：** [agent-vision.anionex.me](https://agent-vision.anionex.me)\n\n## ❤️ 赞助\n\n> 想赞助本项目？详见 [FUNDING.md](FUNDING.md) 或发送邮件到 davidyang042@gmail.com。\n\n<details open>\n<summary>点击折叠</summary>\n\n<table>\n<tr>\n<td width=\"220\" align=\"center\" valign=\"middle\"><a href=\"https://aihubmix.com/?aff=sinZ\"><img src=\"assets/logo_aihubmix.png\" alt=\"AIHubMix\" height=\"48\"></a></td>\n<td valign=\"middle\">感谢 <a href=\"https://aihubmix.com/?aff=sinZ\">AIHubMix</a> 赞助本项目！AIHubMix 是稳定、高并发的 AI 大模型 API 聚合平台，一个 API Key 即可接入 Claude、GPT、Gemini、DeepSeek 等主流模型，兼容多种协议，并提供<b>免费模型选择</b>。注册时，海外用户请使用 <a href=\"https://aihubmix.com/?aff=sinZ\">AIHubMix 入口</a>，中国大陆用户请使用 <a href=\"https://inferera.com/?aff=sinZ\">Inferera 入口</a>。</td>\n</tr>\n<tr>\n<td width=\"220\" align=\"center\" valign=\"middle\"><a href=\"https://api.ewo.so/register?aff=U6PT7J\"><img src=\"assets/logo_eapi_dark.png\" alt=\"E-API\" height=\"48\"></a></td>\n<td valign=\"middle\">感谢 <a href=\"https://api.ewo.so/register?aff=U6PT7J\">E-API</a> 赞助本项目！E-API 聚合主流 AI 模型，兼容 OpenAI、Anthropic 与 Codex 接口；部分 Claude 模型相比官方价<b>最高优惠约 98%</b>，DeepSeek V4 系列<b>优惠约 25%</b>。</td>\n</tr>\n</table>\n\n</details>\n\n**目录**\n\n- [亮点](#亮点)\n- [最近更新](#最近更新)\n- [适合谁用](#适合谁用)\n- [实际效果](#实际效果)\n- [快速开始：三步完成](#快速开始三步完成)\n- [工具一览](#工具一览)\n- [配置与限制](#配置与限制)\n- [常见问题](#常见问题)\n- [赞赏](#赞赏)\n- [开发与社区](#开发与社区)\n\n## 最近更新\n\n- **2026-08-20 · AIHubMix 申请教程：** 新增通过 Inferera 入口申请 API Key 并配置 Gemini 3.7 Flash 视觉模型的图文教程，并在视觉工具设置中直接提供入口。\n- **2026-08-19 · 透明变体路由默认开启：** 模型选择器默认只显示每个模型一项并保留原模型名，粘贴图片、历史图片和内置 `read_image` 工具都能直接使用，不再需要手动切换到 `(Vision Toolkit)` 变体；如需恢复显式条目，可在 设置 → 高级设置 → 图片输入 关闭“透明变体路由”。\n- **2026-08-16 · Windows Python：** 支持 Microsoft Store Python，解决 Windows 用户首次创建隔离环境失败的问题。\n- **2026-08-17 · 视觉升级：** 默认模型切换到 Gemini 3.7 Flash，并修复 Qwen/Gemini 检测框坐标顺序错位的问题。\n- **2026-08-16 · 图片粘贴：** 文本模型自动切换到 `(Vision Toolkit)` 变体并保留工作区路径，解决粘贴图片被拦截或后续无法复用的问题。\n- **2026-08-16 · 服务稳定性：** 扩大服务容量，减少高峰期出现 `429` 的情况。\n- **2026-08-16 · 真实模型测试：** Settings 新增完整图片请求测试，解决 `/models` 可访问却不能证明模型真的会看图的问题。\n\n## 适合谁用\n\n1. 想获得类似多模态模型一样的交互体验：直接粘贴图片，提出要求或疑问\n2. 不只是看图问答，想要完成更复杂、更有价值的视觉任务，例如草图变前端页面，图片转html，提取长截图里的聊天信息等等；后续也会不断补充更多的场景。\n\n随附的 `vision-skills` Skill 携带完整的上游 playbook，说明每个工作流何时使用、按什么顺序调用工具，以及如何验证结果：\n\n| 手册 | Agent 学会做什么 |\n| --- | --- |\n| [读取长截图、聊天记录和滚动页面](assets/skill/references/long-screenshot-ocr.md) | 找到低内容切割带、按顺序 OCR 每个分块、保留聊天发言人/时间戳/引用、只合并重复的重叠部分，并标出有风险的边界供验证 |\n| [根据截图或设计重建 UI](assets/skill/references/restore-ui.md) | 优先复用项目组件和素材，再用代码原生 UI、提取的视觉素材、渲染截图和视觉对比来对齐页面或组件 |\n| [还原图标、Logo、插画或其他图形](assets/skill/references/restore-graphic.md) | 从源图像提取透明 PNG，或按需重建可编辑/可缩放 SVG，然后验证形状、颜色和 alpha 边缘 |\n| [把草图、示意图或白板转成结构化代码](assets/skill/references/restore-structure.md) | 把节点、标签、连接和方向恢复为可编辑的 Mermaid、Graphviz 或其他结构化表示 |\n| [通过截图操作 GUI](assets/skill/references/gui.md) | 定位控件、执行一个动作、再次截图，并先验证结果状态再继续 |\n\n\n## 实际效果\n\n### 在 DSH 里直接粘贴图片提问\n\n<p align=\"center\">\n  <img src=\"assets/dsh-view-example.png\" width=\"82%\" alt=\"DSH Web 中，纯文本 DeepSeek 模型通过 Vision Toolkit 回答用户粘贴图片里的内容\" />\n</p>\n\n*用户粘贴一张图片，纯文本模型自动切换到对应的* `Vision Toolkit` *变体，并围绕用户的问题读取画面。*\n\n### 从截图到可编辑页面\n\n<p align=\"center\">\n  <img src=\"assets/upstream/infographic-reference.webp\" width=\"49%\" alt=\"用于还原的信息图原始截图\" />\n  <img src=\"assets/upstream/infographic-result.webp\" width=\"49%\" alt=\"根据截图还原出的可编辑 HTML 和 CSS 页面\" />\n</p>\n\n> 提示词示例：“（使用vision-skills），把这张图片还原成html”\n\n*左：参考截图；右：用 HTML/CSS 还原出的可编辑结果。视觉结果可以继续进入截图和像素对比流程，而不是停在“描述图片”。*\n\n### 从手绘稿到可用界面\n\n<p align=\"center\">\n  <img src=\"assets/upstream/ui-sketch.webp\" width=\"49%\" alt=\"作为 UI 还原输入的手绘 JupyterLab 界面草图\" />\n  <img src=\"assets/upstream/ui-result.webp\" width=\"49%\" alt=\"根据手绘参考还原出的 JupyterLab 工作区界面\" />\n</p>\n\n*左：手绘参考；右：根据参考还原的可用界面。*\n\n> 提示词示例：“（使用vision-skills），把这张草稿图做成可用的前端页面”\n\n### 快速 UI 还原：先出一版近似稿\n\n<p align=\"center\">\n  <img src=\"assets/upstream/ui-fast-restore-reference.webp\" width=\"49%\" alt=\"快速 UI 还原参考图：YouMind 首页原图\" />\n  <img src=\"assets/upstream/ui-fast-restore-result.webp\" width=\"49%\" alt=\"使用快速 UI 还原模式生成的近似首页\" />\n</p>\n\n> 提示词示例：“（使用vision-skills），把这张图片 快速 还原成html”\n\n*左：原始页面；右：保留主要布局、内容和视觉层级的快速还原稿，允许颜色和图标库近似。快速模式的目标是约三分钟内产出首版截图。*\n\n## 快速开始：三步完成\n\n### 1. 安装\n\n```sh\ndsh plugin --profile web add @anionex/dsh-vision-toolkit\n```\n\nHeadless Profile 也可以安装：\n\n```sh\ndsh plugin --profile headless add @anionex/dsh-vision-toolkit\n```\n\n使用 **DSH Desktop 桌面版**？桌面版自带 `dsh` 命令行，但有意不写入系统 PATH，请不要在系统终端里执行上面的命令。请从托盘打开 **DSH 终端（Open DSH Terminal）**，在桌面版自己的终端中安装到 Desktop Profile：\n\n```sh\ndsh plugin --profile desktop add @anionex/dsh-vision-toolkit\n```\n\n安装完成后重启 DSH Desktop。DSH Desktop 2.0.1 内置插件市场的“一键安装”存在已知问题，修复前请优先使用上面的终端命令安装。\n\n完整的桌面版安装、更新与排查步骤见 [DSH Desktop 安装与更新指南](docs/dsh-desktop-install.zh.md)。\n\n### 2. 重启并确认\n\n重启正在运行的 Web Profile，打开 **设置 → 视觉工具**，配置视觉模型，然后运行**测试视觉模型**确认连接。\n\n首次启动会自动准备隔离运行环境：插件优先使用系统已有的 Python 3.11+；如果系统没有，会自动从国内镜像（`dsh-vision-python-bootstrap-1317715800.cos.ap-guangzhou.myqcloud.com`）下载一个带完整性校验的托管 Python（约 35MB，仅首次需要网络），镜像不可用时自动回退到 GitHub 官方发布源。锁定依赖（Pillow、NumPy、vtracer）会优先从腾讯云 PyPI 镜像（`mirrors.cloud.tencent.com/pypi/simple`）安装，镜像不可用时回退到官方 PyPI。普通安装不需要下载 `agent-vision-toolkit` 源码，也不需要设置本地路径。\n\n### 3. 粘贴图片，直接说你要做什么\n\n在会话中粘贴截图，或把图片放进会话工作区，然后调用 `/vision-skills`。例如：\n\n```text\n看看这张截图，告诉我报错原因和最值得先修的地方。\n找到右上角的登录按钮，返回原图像素坐标并生成带框预览图。\n把这个图标裁出来并转成 SVG。\n按照 reference.png 还原页面，每轮截图后做像素对比，直到主要差异消失。\n```\n\n## 工具一览\n\n插件提供 10 个可以单独调用、也可以组合使用的视觉工具：\n\n| 工具 | 最适合解决的问题 | 主要结果 |\n| --- | --- | --- |\n| `vision_glance` | “这张图里发生了什么？” | 针对性回答、描述、OCR、多图比较 |\n| `vision_ground` | “我要找的东西在哪？” | 原图像素坐标、可选带框预览 |\n| `vision_detect` | “图里有哪些按钮/图标/元素？” | 编号元素清单、坐标、可选预览 |\n| `vision_crop` | “把这块区域单独取出来” | PNG 或 JPEG 裁剪图 |\n| `vision_trace` | “把这个图形变成可编辑矢量” | SVG |\n| `vision_pixel_diff` | “实现和参考图到底差在哪？” | 差异比例、重点区域、热力图、JSON |\n| `vision_long_screenshot_ocr` | “读完这张很长的截图” | Markdown、分块图、清单和审计结果 |\n| `vision_extract_foreground` | “把主体抠出来” | 透明 PNG |\n| `vision_dominant_colors` | “这块区域用了哪些主要颜色？” | 主色板或候选色排序 |\n| `vision_html_screenshot` | “按精确视口渲染本地页面，或一次捕获整页” | PNG 和可选的 CSS `pageHeight` |\n\n坐标始终使用原图像素格式 `x1,y1,x2,y2`，因此定位结果可以直接交给裁剪、描摹或后续自动化。\n\n对于长 HTML 文档，传入 `fullPage=true`。请求的宽高仍作为布局视口，生成的 PNG 会覆盖完整文档，并以 CSS 像素返回 `pageHeight`。\n\n## 工作原理\n\n插件把远程图片理解和可重复的本地图片处理放进同一套 Agent 工作流。下面的流程图展示了具体的职责边界。\n\n### 让描述始终围绕当前任务\n\n多数文本模型视觉桥接的做法是让多模态模型生成一段通用描述，再把描述交给文本模型，这等于多了一层必然有损的语义转换。Vision Toolkit 反过来恢复 **Agent 为什么想看这张图**：把用户消息或模型给出的调用原因作为 focus hint（聚焦提示）传给视觉模型，得到的是围绕当前步骤的重点描述——更少 token、更准确、响应更快。\n\n<p align=\"center\">\n  <img src=\"assets/upstream/focus-hint-comparison-1.webp\" width=\"49%\" alt=\"通用图片描述与带 focus hint 的任务感知描述对比（一）\" />\n  <img src=\"assets/upstream/focus-hint-comparison-2.webp\" width=\"49%\" alt=\"通用图片描述与带 focus hint 的任务感知描述对比（二）\" />\n</p>\n\n**架构与图片输入行为**\n\n```mermaid\nflowchart LR\n    Image[\"截图或本地 HTML\"] --> Skill[\"vision-skills Skill\"]\n    Skill --> Agent[\"文本 Agent 选择任务\"]\n    Agent --> Vision[\"需要理解图片时调用视觉模型\"]\n    Agent --> Local[\"裁剪、SVG、像素等任务在本地处理\"]\n    Vision --> Result[\"回答、OCR、坐标\"]\n    Local --> Artifact[\"PNG、SVG、热力图、JSON\"]\n    Result --> Session[\"继续推理和行动\"]\n    Artifact --> Session\n```\n\n视觉能力来自打包的固定版本 `agent-vision-toolkit`。DSH 插件负责安装、会话级工具暴露、Credential、路径校验、取消、超时、结果文件和 Web 展示。运行时不会在后台拉取上游 `main`。\n\n`vision-skills` Skill（上游原名 `vision-tools`）现在以上游 `SKILL.md` 和全部 5 篇上游 SOP 为明确底稿：\n只适配工具名、结构化参数、Artifact 交付、渐进式暴露，以及 DSH 的路径和生命周期边界；\n上游的工具选择规则、由粗到细方法和任务流程保持不变。精确的上游 Skill commit、\n源文件哈希、适配后哈希和可审查补丁分别记录在 `assets/skill/UPSTREAM.json` 与\n`patches/vision-tools-dsh.patch`。\n\n对于明确标记为纯文本的模型，插件会注册 `<模型名> (Vision Toolkit)` 变体。默认情况下，在 DSH Web 粘贴图片时会自动切换到该变体，并把图片路径与带当前任务重点的视觉描述一起交给模型。\n\n## 配置与限制\n\n### 配置视觉模型\n\n在 **设置 → 视觉工具** 中配置视觉模型提供方，并把 API Key 保存为 DSH Credential。Settings 只保存 Credential 引用，不会回显密钥。\n\n**AIHubMix 图文教程：** [申请 AIHubMix API Key 并配置 Gemini 3.7 Flash 识图](docs/aihubmix-gemini-vision.zh.md)。教程包含账号与 API Key 获取截图、Vision Toolkit 的准确配置、模型选择和常见问题排查。\n\n也可以在 Profile patch 中配置：\n\n```yaml\n- id: vision-toolkit\n  config:\n    provider:\n      baseUrl: https://api.example.com/v1\n      credential: MY_VISION_KEY\n      model: your-vision-model\n      protocol: openai\n```\n\n支持 OpenAI Chat Completions 兼容端点、OpenAI Responses 和 Anthropic Messages。已有配置仍默认使用 Chat Completions。若要使用兼容 Responses 的端点，请设置 `protocol: responses`；插件会在 API 地址后拼接 `/responses`。可选的 `reasoningEffort` 仅随 Responses 请求发送：\n\n```yaml\n      protocol: responses\n      reasoningEffort: medium\n```\n\n`reasoningEffort` 留空时使用模型或代理的默认值。常见值包括 `none`、`minimal`、`low`、`medium`、`high` 和 `xhigh`；为兼容第三方代理和未来扩展，该字段也接受由字母、数字、`.`、`_`、`-` 组成且不超过 64 位的提供方自定义值。实际支持范围与计费由模型或代理决定；较高强度可能增加推理 token、延迟和费用。Responses 请求会设置 `store: false`，但该标记不能替代核查服务商自己的数据保留政策。Web Settings 页面还可以调整超时、图片限制、并发、运行时和图片输入变体。\nProfile patch 还可以通过 `provider.headers` 配置非秘密的部署元数据。需要按会话路由的网关可在 `provider.sessionHeaders` 中列出一个或多个运行时生成的请求头；例如 OpenCode Zen 要求 `x-opencode-session`：\n\n```yaml\n- id: vision-toolkit\n  config:\n    provider:\n      headers:\n        x-tenant: acme\n      sessionHeaders:\n        - x-opencode-session\n```\n\n静态请求头是保存在 Settings 中的普通明文，不是 Credential。不要在 `provider.headers` 中填写 API Key、Bearer Token、Cookie 或其他秘密；视觉 API Key 仍应使用 `provider.credential`。插件会拒绝客户端自有的鉴权头和 HTTP 路由/请求帧控制头、大小写不敏感的重复项、两个字段间的冲突、非法名称或值、合计超过 32 项、名称超过 128 字节、值超过 4096 字节，或总量超过 16384 字节的配置。\n\n每个会话请求头都会获得 HMAC-SHA256（带密钥的不可逆摘要）的前 32 位十六进制值，密钥在 DSH 进程启动时随机生成。同一进程内，相同 operation identity（一次调用采用的会话身份）得到稳定值；重启后会轮换。有 Session 的调用只把 Session id 作为内部 HMAC 输入；确实没有 Session 时才回退到 `workspace:<绝对路径>`，原始 Session id 和 workspace 路径都不会发送。同一次 Settings 健康检查的 `GET /models` 与真实模型请求共用派生值。请求头只注入配置 provider 的同源 URL 且路径必须位于其 base path 下。两个字段均为空时，不创建额外请求头环境变量，provider 的网络请求头语义保持原样。\n\n高级设置中的 **默认保存目录** 可以把产物、粘贴图片和缓存放到 `/tmp/dsh-vision-toolkit` 等 POSIX 绝对共享根目录下；插件会为当前用户和工作区创建权限为 0700 的私有子目录。留空时继续使用工作区内原有的 `.dsh-vision-toolkit` 目录。Windows 目前会拒绝配置共享根目录，因为插件尚不能安全校验其所有权和访问控制列表。\n\n配置的保存目录变更后，插件会把之前验证过的根目录保留为只读输入位置。Web Profile 会把这段历史保存在插件自有的 `vision_toolkit_storage` storage-domain sidecar 中；即使当前 Settings 提供方只读，Profile 重启后原有粘贴图片路径仍可继续使用。使用配置共享存储的自定义 Profile 应组合 `@deepseek-ai/dsh-storage-domain`。\n\n如果受信任的内部端点使用自签证书或 MITM 代理，可在启动 DSH 进程时设置 `VISION_SSL_VERIFY=0`。插件会把该值传入隔离的 Python 运行环境；未设置或使用其他值时仍默认校验证书。还支持大小写不敏感的假值 `false`、`off`、`no`、`none` 和 `disabled`。\n\n### 配置 Python 运行时\n\n大多数用户无需配置 Python 运行时：插件会优先使用系统 Python 3.11+，找不到时自动从国内镜像下载固定版本的托管 Python；国内镜像不可用时回退到 GitHub 官方发布源。\n\n需要覆盖 `runtime.python`、使用 `runtime.mode: external`、验证运行时，或允许读取其他目录时，请参阅 [Python 运行时配置](docs/python-runtime.zh.md)。\n\n## 常见问题\n\n| 问题 | 处理方式 |\n| --- | --- |\n| 视觉模型测试失败：`Vision API returned an incompatible response structure` | 通常是 API 地址少了路径前缀。LM Studio、Ollama 等本地 OpenAI 兼容服务需填写 `http://127.0.0.1:1234/v1`（带 `/v1`）；OpenAI Chat Completions 会拼接 `/chat/completions`，OpenAI Responses 会拼接 `/responses`，只填端口号可能命中未知端点 |\n| 粘贴图片后仍提示模型不支持图片 | 重启 Web Profile 并刷新页面，确认当前模型已切换到带 `(Vision Toolkit)` 的变体；也可以把图片先放进会话工作区，再调用 `/vision-skills` |\n| 视觉服务提示 429 | 按错误中的 `Retry-After` 等待后重试；如果需要稳定高额度，切换到自己的视觉端点 |\n| 图片过大或像素超限 | 先裁剪或缩放图片；错误会明确显示是字节还是像素限制 |\n| 自定义 Credential 缺失 | 在 **设置 → 视觉工具** 填写 API Key，并确认 Credential 名称与配置一致 |\n| OpenCode Zen 返回 `400 MissingSessionID` | 在 Profile patch 的 `provider.sessionHeaders` 中加入 `x-opencode-session`，然后重启 Profile；不要把 Session id 或 API Key 写进 `provider.headers` |\n| 首次运行时准备失败 | 自动下载托管 Python 需要网络和磁盘权限（默认先走国内镜像，失败时回退 GitHub）；失败时检查网络或包缓存，也可以安装 Python 3.11+ 或在 Settings 中配置 `runtime.python`，然后重新测试 |\n| 找不到 Chrome | 安装 Chrome、Chromium 或 Edge；只有 HTML 截图不可用，其他工具不受影响 |\n| DSH Desktop 提示找不到 `dsh` 命令，或内置插件市场安装失败 | 从托盘打开 **DSH 终端**，运行 `dsh plugin --profile desktop add @anionex/dsh-vision-toolkit`，再重启 DSH Desktop。桌面版 2.0.1 的内置市场存在已知安装问题，当前请优先使用终端安装 |\n| 产物无法预览 | 使用“打开文件”或结果中的工作区路径；预览 URL 只在 Web 路由可用时存在 |\n\n## FAQ\n\n**视觉模型会怎样影响成本？**\n\n每次检查都是一条独立的多模态请求，只携带必要意图和图片，调用之间不会累积上下文。实际费用取决于服务商、图片、模型，以及 Responses 的 `reasoningEffort`；较高强度可能消耗更多推理 token 并增加延迟。留空可使用提供方默认值；若更看重可预测的本地成本，也可以使用本地部署的小型多模态侧模型（例如 Gemma 4 或 Qwen 3.5/3.6 系列）。\n\n## 赞赏\n\n如果本项目对你有价值，欢迎请开发者喝杯咖啡☕️\n\n<img width=\"240\" alt=\"微信收款码\" src=\"assets/wechat-reward.png\" />\n\n## 开发与社区\n\n- 贡献前请阅读 [CONTRIBUTING.md](CONTRIBUTING.md)。\n- Bug、功能建议和使用问题请提交到 [GitHub Issues](https://github.com/Anionex/dsh-vision-toolkit/issues)；渠道说明见 [SUPPORT.md](SUPPORT.md)。\n- 安全漏洞请按 [SECURITY.md](SECURITY.md) 私下报告。\n- 版本变化见 [CHANGELOG.md](CHANGELOG.md)，赞助说明见 [FUNDING.md](FUNDING.md)。\n- 通用视觉工具、跨 Agent 接入和视觉任务方法论请访问上游 [agent-vision-toolkit](https://github.com/Anionex/agent-vision-toolkit)。\n\n<p align=\"center\">\n  <img src=\"assets/community-group-qr.png\" alt=\"agent-vision-toolkit 项目交流群二维码\" width=\"240\" />\n</p>\n\n我是 [anionex](https://anionex.me/)，一位 AI 原生开发者，曾位列 GitHub 全球开发者趋势榜第 **3** 名，项目累计超过 16k stars。想了解我后续的工作，欢迎在 [GitHub](https://github.com/Anionex) 关注我。\n\n[`agent-vision-toolkit`](https://github.com/Anionex/agent-vision-toolkit) 由 [Anionex](https://anionex.me/) 创建。本仓库维护它面向 DeepSeek Harness 的原生集成。\n\n## 许可证\n\n插件采用 [MIT License](LICENSE)。打包的上游快照保留其原始 MIT 许可证，见 [`vendor/agent-vision-toolkit/LICENSE`](vendor/agent-vision-toolkit/LICENSE)。\n","readmeFilename":"README.zh.md"}