dsh-longcat
LongCat (LongCat-2.0) model provider for DeepSeek Harness — 1M context, thinking mode, tool calling
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LongCat (LongCat-2.0) model provider for DeepSeek Harness — 1M context, thinking mode, tool calling
Auto-fold context/system messages in DSH conversation — hide the large system prompt / instructions by default, click to expand.
Memo — session memory search for DeepSeek Harness agents: cross-session full-text recall (memo_search), distilled notes (memo_remember), and corpus stats (memo_stats) over the official sessionQuery service. Zero infrastructure, local-first.
MiniMax 文生图插件:把一句话画面描述生成图片并保存到工作区,作为 `image-gen` 工具提供给模型调用。
The see tool for the DeepSeek Harness: offline OCR with positions (macOS Vision) + ASCII layout art (PIL) + vision-model semantic description (mimo-v2.5 via the opencode-go gateway). Works with any model, including text-only ones.
DSH bundle: Qwen multimodal bridge — vision (qwen3-vl), speech-to-text (qwen3-asr), text-to-image (qwen-image), via the deepseek-vision skill scripts
Paper Plane X plugin for DeepSeek Harness Web: context panel + /ppx-paper search
OpenViking memory integration: Web Settings plugin card + automatic memory-context injection
Model-facing ocr_image tool that runs local RapidOCR (via a Python subprocess) and returns image text as plain text, so text-only DeepSeek models can read images
The small deterministic operations an agent needs mid-task. JSON/YAML round-trip, JSON Schema validation, text diff, JWT decode, regex testing, SQL dialect transpiling, QR codes, timezone and unit conversion, page screenshots.
Paste images into DeepSeek Harness chat and have them read by a free local backend (macOS Vision / Tesseract) before the text-only DeepSeek model answers
文本行去重/排序/统计
DeepSeek Harness plugin: drive the local Grok Build CLI for text, image, and video.
文本换行
Lightweight DSH upload bridge: images and common documents become workspace paths so a text-only DeepSeek model can read them with Qwen-MM-Plugins vision tools.
DSH WebUI 语音输入插件(火山引擎流式 ASR / 豆包 Seed ASR):输入框麦克风按钮(Alt+V)→ 浏览器采集 16kHz PCM → 经宿主 WebSocket 中继到豆包流式识别 → 实时回填(跟随光标/Proma 式输出,失败兜底剪贴板)。协议实现源自 Proma 桌面端调研复刻。
模板渲染
DSH plugin: keep text-only models (deepseek-v4-flash / deepseek-v4-pro) as the session default, and automatically route requests that carry image content to a configured vision-capable model (deepseek-v4-flash-vision-exp) 鈥?no manual model switching (periscope).
跨会话持久记忆压缩系统:生命周期 hooks 自动捕获工具观察、生成语义摘要、3 层检索工作流(search→timeline→get_observations,~10x token 节省)、渐进披露、私有标签、混合搜索。受 thedotmack/claude-mem(86k★ Apache-2.0)启发。
SenseVoice 语音输入插件 for DeepSeek Harness:在对话输入框旁添加麦克风按钮,录音后调用本地 SenseVoice 服务转成文本填入输入框。首次使用自动下载模型并显示进度,后端由插件自动启动。
DSH plugin: adapts DashScope/闂傚倸鍟锟犲闯闁垮顩查柟瀵稿У椤忋儵鏌?DeepSeek endpoints that lack native tool calling by converting tool definitions to prompt text and parsing model responses for tool calls.
A canvas the agent draws on and then sees: eyes_render draws text/shapes in the Web GUI, stores the PNG locally, and hands the result back to the model. Windows-only: eyes_ocr reads text via the built-in Windows OCR engine (Windows.Media.Ocr).
In-session full-text search for the DeepSeek Harness web GUI: kind filters, multi-term AND matching, keyboard navigation, copy-to-clipboard, and jump-to-message.
Giving text models eyes to see in DeepSeek Harness.