ds-vision-plugin
Automatic Web image-to-text bridge plus vision and OCR tools for DeepSeek Harness
278 results
Automatic Web image-to-text bridge plus vision and OCR tools for DeepSeek Harness
DeepSeek Harness plugin: let text-only models receive pasted images, and analyze them with a built-in OpenAI-compatible vision tool
DSH 鸿蒙投屏控制插件(静态 npm 包版 v2.0):设备发现 / 实时投屏 / 触控操作 / 系统按键 / hilog 日志 / AI 截图识别与操控(控件清单 / 点击 / 长按 / 文本输入)
Hands for your DeepSeek Harness agent — autonomous browser operation with a native permission model. Accessibility-ref page snapshots let a text-only model navigate, act, and test without vision or CSS-selector guessing.
Host-level vision bridge for text-only models: analyze_image tool (Ollama local / Xiaomi MiMo cloud / any OpenAI-compatible endpoint) returning structured evidence.
Start a DeepSeek Harness conversation without selecting a workspace: auto-provisions a default workspace and adds a "Chat without a workspace" option to the workspace picker
Plug-in vision for text-only DeepSeek Harness (dsh) models: a `vision` tool with built-in cheap/free VLM presets, multi-image batch analysis, paste-to-hint image admission, and a web settings page with hot-reload.
DeepSeek Harness 视觉能力全家桶:vision_understand 工具(OpenAI 兼容视觉 API,默认免费智谱 GLM-4.6V-Flash,限流自动降级 GLM-4V)+ 粘贴/拖拽/按钮三入口识图
Give DeepSeek (text-only) models vision in Claude Code, Codex, and Agent Plugins clients: describe images via any OpenAI-compatible vision endpoint.
图片识别插件:自动判断当前模型是否具备视觉能力,有则用当前模型并按插件预设提示词分析,无则调用插件配置的视觉模型。文本模型可直接在对话框粘贴/上传/拖拽图片,发送时自动写入 DSH 附件存储(永久),消息区渲染缩略图,模型自动调用 image_vision / ocr / ground / crop 系列工具识别与精读;模型选择器保持简洁。
DSH plugin: renders images inline in DeepSeek Harness Web chat and gives text-only models vision — cloud multimodal API first, local Qwen3-VL fallback.
CLIProxyAPI provider for DeepSeek Harness with local auto-discovery, dynamic model sync, model switching, and vision preprocessing.
External vision proxy plugin for DeepSeek Harness, enabling text-only models (DeepSeek-V3 / R1) to analyze and understand images via OpenAI-compatible vision APIs.
Eyes for text-only DeepSeek on DeepSeek Harness: image understanding via Antigravity IDE quota (default, flash/pro), OpenAI-compatible VLM endpoints, Gemini API, or local Ollama — model-invokable vision tool, wrapper adapters for deepseek/opencode-go, evidence memory, and a polished client panel.
DSH Web manager for global AGENTS.md with Markdown preview, reusable templates, version history, import rollback, and revision-conflict protection.
DeepSeek Harness multi-model routing plugin: route tasks to custom specialist agents (vision / image generation / translation / speech / subagent) with per-agent providers & models, multimodal account sign-in, OAuth account pools, and realtime usage stats
Windows Computer Use for DeepSeek Harness: window-bound screenshots, robust OCR, verified clicks, pure-OCR mode, pluggable vision models.
A DeepSeek Harness Mix plugin for vision routing plus GPT Image generation and editing.
DeepSeek Harness adapter that exposes each configured vision model as a selectable DeepSeek composite in Web UI.
dsh 插件:给文本模型补一双眼睛 + 给多模态模型补一对观察 UI 渲染的眼睛 —— 设置弹窗选视觉模型 + meow_vision 工具 + meow_preview 组件截图工具
Image recognition and image generation for DeepSeek Harness: model-facing tools that call any OpenAI-compatible external API (vision chat/completions and images/generations), with no built-in model defaults — a tool without a configured model fails with a clear error.
DeepSeek Harness native plugin: vision capabilities for text-only LLMs (describe, OCR, VQA, layout analysis, clipboard)
DeepSeek vision bridge for the dsh web GUI: route image attachments to a vision model (pi-ai / llama.cpp Qwen3-VL) and continue the conversation with the text description on a text-only LLM (DeepSeek)
DSH plugin: start/stop a local llama.cpp llama-server from the settings → plugins page — two model slots (A/B) with per-slot model-file selection, eight editable launch-parameter groups (text/vision × fast/long × 2 slots), and one shared DSH provider carrying both slots' models with four thinking-effort levels.