Plugins

Bioinformatik-„Wunsch-Analyse“-Plugin: vollständige BioPython-Abdeckung (beliebiger Code-Executor bio_python + 14 Domänen-Skills) + hochfrequente semantische Tools + Null-Abhängigkeit-Selbst-Bootstrap-Python-Umgebung (uv + venv + biopython automatischer Download in Plugin-Privatverzeichnis). Benutzer können nach Download und Installation direkt Wunsch-Analysen durchführen.

Registriert die Skill remove-pdf-background-gray für DSH: bleicht graue oder cremefarbene Scan-Hintergründe bildbasierter PDFs weiß, während Auflösung, Seitengeometrie und kantengeglättete Textkanten erhalten bleiben (verlustfreies Flate-Zurückschreiben), über ein einziges Python-Skript (pypdf + Pillow + numpy).

Führt OmicVerse/OmicOS-Bioinformatik-Analysen in einem persistenten Python-Kernel aus, mit Suche im Fähigkeitskatalog, Kernel-Inspektion und einem Konto-Tab.

Coding-learn floating panel: edits and runs Python/JavaScript/TypeScript/PowerShell with exit code and timing, grades JSON course lessons with progress export, keeps Markdown notes and snippet library, and sends code plus a question into the current conversation for the main model to answer.

Node.js fork of andyfan1094/dsh-winrm: the same WinRM host panel and seven agent tools, with the backend ported from Python/pywinrm to a native Node WinRM client so the machine running dsh needs no Python.

Drives the GME Test Agent from inside Harness: three tools list interface catalogs, start and repair autonomous test generation in a local Python backend, poll the task until review, and keep PR, skip and cleanup actions behind explicit user consent.

浏览器控制插件：把 browser-use / browser-harness 的 Browser Harness 注册为 DSH 会话工具，让 agent 直接写 Python 驱动真实 Chrome（导航/点击/输入/取无障碍树/截图），支持独立专用浏览器与附着系统浏览器两种模式；配置 TypeSafe key 后条件启用 browser_autopilot（Jev 目标式自动化）。

端到端内容智能采集与调研工作流：多源嗅探→四维评分门控→4级深度抓取→键控事实槽矛盾检测（GA11）→网络边界门控（GA12）→跨源融合→结构化 Markdown 报告。DeepSeek Harness (dsh) skill bundle.

Drives the GME Test Agent from inside Harness: three tools list interface catalogs, start and repair autonomous test generation in a local Python backend, poll the task until review, and keep PR, skip and cleanup actions behind explicit user consent.

Image and video production toolkit for DeepSeek Harness - background removal with quality tiers, local inpainting retouch, headline and logo compositing, batch platform-size exports and image QA reports; for video, AI subject matting with background replacement (RVM ONNX, plus a chroma-key path), animated titles with fade/slide/typewriter/karaoke presets, 53 transitions with stylized variants, and light-effect and grading presets with LUT support, alongside footage inventory, accurate cutting, subtitle burn-in, music mixing and platform-spec exports; it provisions its own local Python runtime through a bundled bootstrap script.

Voice input plugin (dual-face): mic button beside the composer send action, live recording bubble streaming PCM to a local Qwen3-ASR python service managed by the host, plus an ASR environment settings page (clone runtime/model repos, create venv, run ser

Decides whether Python code is original or a derivative work: reports the longest identical expression span against an upstream tree, an exemption rationale per finding, and a risk list. Pure stdlib `ast` analysis — offline, no model calls.

`dsh-workflow-designer` 是面向 DeepSeek Harness 的提示词优先工作流设计插件。它收集当前 Agent 可见的本地 Skill、原生 Tool 与 MCP Tool，提供必要的可视化编排和约束编辑能力，并导出稳定的 YAML 中间表示。后续可由大模型及对应框架把 YAML 生成 Python、`workflow.mjs` 或其他代码工作流。

DeepSeek Harness 插件：语音 + 通知出口——agent 通过云端 TTS（火山 seed-tts / 小米 MiMo V2.5，失败自动回退 SAPI）/ 桌面通知 / 提示音主动联系用户。融合 dsh-plugin-notify 的 DSH 原生深度集成与 agent-voice-mcp-minus 的云端 TTS 调优，零 Python 依赖，Windows 原生。

Persistent C/C++, Python, and TypeScript definition navigation with a standalone workspace UI for DeepSeek Harness.

Bridges the agent to a persistent OKF memory vault over MCP stdio: a Python server (SQLite FTS5 + Markdown) with 10 tools for searching and storing decisions, facts, learnings, conventions, profiles and sources.

Legacy code modernization workflow (preflight -> assess -> map -> transform) with a Cordis mount plugin, offline Python scripts and smoke tests.

Linux X11 computer use for DeepSeek Harness: 9 tools (screenshot, move/click/drag/scroll mouse, type, key combos) driven by a zero-dependency Python XTest helper — native-pixel coordinates, per-action verification, optional auto-observation screenshot.

OI 出题工作台：知识点锁定、数据构造、本地评测，打包 Hydro/HOJ 原生题目包，发布/管理 Hydro 与 HOJ OJ，提供 Python 外部管理 UI 与系统斜杠命令。Opt-in skill-first plugin for DeepSeek Harness.

A stateful, Agent-controllable Jupyter workspace for DeepSeek Harness: a persistent ipykernel runtime the DSH agent can read, edit, execute, and inspect, with VS Code-aligned cell UI and .ipynb save/load.

Native dsh (DeepSeek Harness) Cordis plugin adapter for vision-translation: grounds images into <vision-context> via the Python CLI (PROTOCOL v1). Spawns cli.py, never re-implements core logic.

RLM mode for dsh: recursive sub-agents via a unified rlm() function — spawn, await, and chain child agents as first-class Python calls. Adds a persistent IPython kernel with tools.* bindings and context-as-variable.

DSH project scaffold preset: strict 5-phase init runbook, engineering standards and six runnable starter templates (node-ts / react-vite / python / go / spring-boot / monorepo).

Desktop (native) control for dsh as a stdio JSON-RPC bridge over a Python core: capture the screen, inject pointer/keyboard input, drive accessibility-tree actions, and gate high-risk operations behind required confirmations.