Plugins

A black hole theme plugin for the DeepSeek Harness (dsh) Web UI.

Volcengine Mobile Use Agent plugin for DeepSeek Harness

Codex-style message timeline navigation for DSH web clients: hover to preview a message, click to jump to it, and the currently-read turn stays highlighted.

DSH 更新检查插件（静态 bundle）：首次打开自动检查 DSH 新版本（取 npm 实际最高版本），左下方弹窗提示并提供【更新到】按钮自动执行已校验升级；UI 使用 DSH 语义化主题 token；DSH 安装根/版本、node/npm 均在运行时探测，无硬编码路径。

Emotion Ball pet dock for the DeepSeek Harness Web composer

DeepSeek Harness (dsh) plugin for @tabilo/ai-code-review: registers the ai_code_review tool (review | context) that reuses the Node.js engine to collect diffs, run tests/coverage, do impact analysis, and feed pack skills to the model.

The task-center plugin set for DeepSeek Harness in one package: task ledger, model tools, human commands, wake patrol, quota-resume guard, idle-session mining, the reaper, scheduled sends, and the web kanban.

Reasoning-effort tier manager for custom API models plus cross-session preference memory: inline low/medium/high/max toggles inside Settings → Models, with atomic settings writes.

Adds an x_search tool for X/Twitter search to DeepSeek Harness, authenticated through a separate SuperGrok OAuth account so it does not consume the session provider quota.

Telegram channel for DeepSeek Harness — real Telegram markdown, and answer the agent's questions and approvals straight from chat

Unified Editor runtime for DeepSeek Harness, powered by MCP Apps

Enforce configurable git commit / push / PR conventions for DeepSeek Harness agents

DSH 静态 bundle 插件：在设置页管理第三方插件——列表、启用/停用（持久化到 ~/.dsh/cordis.patch.yml）、卸载、检查更新、一键升级。A static-bundle DSH plugin that manages third-party plugins on the settings page: list, enable/disable (persisted), uninstall, check for updates, and one-click upgrade.

Youdao-style word-selection translation for the DSH web GUI. Select text anywhere, click the floating button, get a translation from the harness's own LLM — no API keys, no third-party endpoints. 有道风格划词翻译：DSH Web GUI 里划选文本即现悬浮「译」按钮，点击由 harness 自带大模型翻译，无需

Adds custom icons to the DSH Settings sidebar and exposes a keyed `settings.section.icon` slot for third-party settings sections.

Free-tier model routing for the DeepSeek Harness (dsh) - discover OpenRouter free models, apply the best free model as agent default, auto-rotate on rate limits; paid config untouched unless asked.

LLM debug console for DeepSeek Harness: captures the full content of every model call at the llm/stream waterfall (options, stream chunks, usage, wire protocol) and shows it in a DevTools-style docked panel, with shared-prefix context diffing, wire-JSON reconstruction, JSON export and a runtime-tunable capture policy.

macOS desk pet forked from anneheartrecord/dsh-desk-pet, in a real always-on-top window that covers fullscreen Spaces: the pose follows whatever the local DSH is doing across six states, the right-click menu is a real AppKit one carrying quiet mode and a session list, and seven skins ship with it. Two of the seven are drawn from geometry by a script in the repo rather than generated, so making a skin needs no image model and no API credits; the bundled skill still expands one photo into all eighteen poses on your own image tool and credentials. There is no Electron and nothing to install alongside it: the window is driven through ctypes on the Python that ships with macOS.

Screenshot-to-input for DeepSeek Harness: composer camera button + global hotkey (Alt+A) + listener bound to the app lifecycle, configurable in settings. 截图自动粘贴到 DSH 输入框：相机按钮 + 全局快捷键 + 生命周期绑定 + 设置页配置。

聊天输入框图片工具(自研):上传图片(≤10MB 防烧 token) + 自定义区域截图(Electron desktopCapturer),注入 DSH 草稿图片轨。零外部依赖。

A microphone button for DeepSeek Harness that writes browser speech recognition into the composer draft.

Browses the petdex.dev catalog in Settings and runs a floating macOS desktop pet that reacts to agent activity.

SSH remote execution set: give a session an ssh:// workspace and every file, command, and shell operation runs on the remote host.

Load testing with Grafana k6: smoke-test first, then run scripts and read percentiles from the JSON summary.