Sorwcyra/ds-vision-plugin
将图片粘贴到DeepSeek Harness中,支持四模型视觉竞赛、OCR及自动文本桥接功能。
Project Overview项目介绍
This is a native plugin built exclusively for DeepSeek Harness that adds vision support to text-only DeepSeek models. It requires Node.js 22.19 or 24 to build and run, and works without any forks or modifications to the core DeepSeek Harness source code. After installation, users can paste or drop images directly into the Harness web composer in PNG, JPEG, WebP, or GIF format, and the plugin will automatically convert images to grounded text using a multi-model race approach. It also exposes two tool endpoints, vision_analyze for workspace image processing and vision_status for diagnostics without exposing private API keys.
The default configuration runs a four-model race where Agnes 2.5, Agnes 2.0, GLM-4V-Flash, and GLM-4.1V-Thinking-Flash all start processing the same image at the same time. The first valid result returned is used, which maximizes availability and minimizes latency for end users. Setup is made easy by a guided command-line interface that handles configuration, API key storage, status checks, and adding custom models, so users never have to manually edit YAML files. This plugin is ideal for any DeepSeek Harness user that regularly works with image input like screenshots, diagrams, or photos.
This plugin is released under the open-source MIT license, so it is free to use, modify, and distribute. It supports adding custom OpenAI-compatible vision models, either as part of the concurrent race or as an ordered fallback, and users can choose between two failure modes: annotate, which marks failed conversions visibly, or error, which stops the request on failure. For privacy-sensitive images, users can disable automatic conversion or use local VLM or OCR runtimes to avoid sending image data to third-party cloud providers. To build and verify the plugin locally, you can run pnpm install followed by pnpm run build, pnpm run check, and pnpm pack to generate the installable bundle.
这是一款专为 DeepSeek Harness 开发的原生视觉插件,能够让纯文本版的 DeepSeek 模型原生支持图片输入与 OCR 识别。用户只需在 Harness 的网页编辑器中粘贴或拖入图片,插件就会自动调用配置好的多模型并发竞态识别,将图片转换为带上下文的文本输出给 DeepSeek,无需修改 Harness 源码即可使用,同时还暴露了接口支持分析工作区图片和诊断状态,也支持用户自定义添加私有模型或本地运行时,满足不同隐私需求。
对于普通用户来说,使用流程非常简单:启动网页配置文件,选择 DeepSeek 提供商,粘贴或拖入一张或多张 PNG/JPEG/WebP/GIF 格式图片,按需添加问题后发送即可。用户还可以通过引导式 CLI 完成配置、密钥设置、状态检查和模型自定义,全程无需手动编辑 YAML 文件。插件默认采用四模型竞态模式,能最大程度保证低延迟和高可用性,适合关注识别速度和稳定性的 DeepSeek Harness 用户。
本插件基于 TypeScript 开发,要求 Node.js 版本为 22.19 或 24,采用 MIT 许可证开源,完全免费使用。隐私方面,网页上传的图片仅通过 Harness 验证的私有附件服务读取,配置的云渠道只会获得图片字节,用户如果处理敏感图片,可以使用本地 VLM 或关闭自动转换。失败处理默认采用标注模式,不会静默丢弃图片,保障问题可追溯。
请帮我安装这个 DSH 插件。安装前先完成【兼容性检查 + 安全性检查】,检查通过再动手。
插件:ds-vision-plugin(Sorwcyra/ds-vision-plugin)
仓库:https://github.com/Sorwcyra/ds-vision-plugin
本站详情页:https://www.yhbd.top/plugins/sorwcyra-ds-vision-plugin/
本站登记:类型 plugin · 归类 原生 DSH 插件 · 许可证 MIT · ⭐ 4 · 最近提交 2026-08-14 · 主语言 JavaScript
按下面顺序执行,每步先把结论告诉我,再进入下一步:
【1 兼容性检查】
① 我这边:DSH 版本、Node 版本、操作系统、当前 profile(web / desktop)。
② 读它的 README、package.json、插件 manifest,列出它要求的 DSH 版本 / Node 版本 / 操作系统 / 外部依赖 / 需要另外先装的运行时。
③ 逐条比对,结论只写「满足 / 不满足 / 未知」三种;不满足的给出可行替代方案。
④ 检查是否和我已装的插件冲突:命令名重复、skill / tool 重名、端口占用、重复注册的 MCP server。
【2 安全性检查】
① 仓库可信度:和上面「本站登记」是否一致;star / fork 数、创建时间、最近提交,是否归档或长期停更。
② 安装脚本:逐行看 package.json 的 preinstall / install / postinstall,以及 install.sh、setup.ps1 之类脚本。出现 curl|bash、下载后直接执行、混淆代码、访问与插件功能无关的域名,立刻停下来告诉我,不要继续装。
③ 依赖:列出新增依赖,标出无人维护、或与知名包拼写近似的可疑包(typosquatting)。
④ 权限与副作用:它会读写哪些目录、访问哪些域名、需要哪些 DSH 权限(filesystem / network / shell / clipboard 等),以及怎么卸载和回滚。
⑤ 如果它要求 sudo / 管理员权限,或权限明显超出功能所需,先停下来问我。
【3 安装】
上面两步没有「不满足」和「高危项」时才执行;用官方推荐方式安装,不要自行提权。
【4 汇报】
用表格输出:检查项 / 结论 / 依据 / 是否需要我决策。拿不准的一律写「未知」并说明要我怎么确认——不要猜,也不要替我决定。
Send this message to DSH in your current session: it verifies compatibility and security first (answering met / not met / unknown item by item) and only installs once everything checks out — it will stop and ask you if it finds a high-risk item. The box scrolls; the copy is the full prompt. CLI install commands may not be accurate across systems, so DSH is the safer route.把上面这条消息直接发给当前会话里的 DSH:它会先核对兼容性与安全性(逐条给「满足 / 不满足 / 未知」),确认没问题再安装,有高危项会停下来问你。框内可滚动,复制到的是完整提示词;安装命令不一定准确,发给 DSH 更稳。
- Only 4 stars - very few users, little community feedback星标只有 4,几乎没人在用,遇到问题缺少社区反馈
DSH walks through these 9 checksDSH 会逐条核对这 9 项
Compatibility兼容性
- DSH, Node, OS and profile requirementsDSH 版本 / Node 版本 / 操作系统 / profile 是否满足要求
- External dependencies and runtimes (Electron / Python / Docker, ...)外部依赖与运行时(Electron / Python / Docker 等)是否齐备
- Conflicts with installed plugins: command names, skill / tool names, ports, duplicate MCP registration与已装插件是否冲突:命令名、skill / tool 重名、端口占用、重复 MCP 注册
Security安全性
- Repo matches the facts registered here; archived or abandoned?仓库是否与页面登记一致,是否归档或长期停更
- Safety of preinstall / install / postinstall and install.sh / setup.ps1preinstall / install / postinstall 与 install.sh、setup.ps1 是否安全
- curl|bash, download-then-execute, obfuscation, unrelated domains → stop immediatelycurl|bash、下载即执行、混淆代码、无关域名 → 立刻停止
- Typosquatting or unmaintained packages among the new dependencies新增依赖里有没有 typosquatting 或无人维护的包
- Requested permissions vs. what the feature actually needs申请了哪些权限、是否超出功能所需(filesystem / network / shell / clipboard)
- Any sudo / admin requirement, plus uninstall and rollback是否要求 sudo / 管理员权限,以及卸载与回滚方式
Anything uncertain must be marked unknown with a note on how to confirm it. This site's signal screen is a static snapshot, not a security audit.拿不准的必须标「未知」并说明要我怎么确认。本站的信号筛查是静态快照,不能替代安全审计。
Or use CLI install (for developers)或使用命令行安装(适合开发者)
CLI Install命令行安装
npx -y @deepseek-ai/dsh plugin --profile web add "github:Sorwcyra/ds-vision-plugin"
把 Sorwcyra/ds-vision-plugin 加入你的 DSH 配置(web profile)即可启用。
READMEREADME
ds-vision-plugin
Paste an image. Let four vision models race. Keep DeepSeek text-only.
简体中文 · Quick start · Routing · Verification · License
paste → attachment → race ×4 → grounded text → DeepSeek
An installable DeepSeek Harness bundle that gives a text-only DeepSeek model a natural image-input experience. Paste or drop an image into the Web composer; the plugin reads Harness's verified attachment, races configured vision models or OCR, replaces the image with grounded text at agent/pre-step, and lets DeepSeek continue normally.
[!NOTE] No Harness source fork is required. The plugin also exposes
vision_analyzeandvision_statusfor workspace files and diagnostics.
Why it exists
Showing the opening section of the README — the full document lives in the repository以上为 README 开头摘要,完整文档在仓库内 · View the full README on GitHub →在 GitHub 查看完整 README →
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