Plugins

Explains computer memory as a workbench: current usage, memory pressure and top programs.

Kids' coding classroom: run Python and LOGO-turtle code, explain, practice, review and step through programs.

Publishes whichever model the profile has selected in chat or on the Models page as an OpenAI-compatible HTTP endpoint: /v1/models, /v1/chat/completions (streaming SSE and non-streaming), /health. A request may name any routable model; one that omits `model` gets the current chat selection, so switching model in chat switches the endpoint. Switched on, it can also lend the model the Harness tools switched on individually, run those calls through the Harness tool runtime and feed results back up to a round limit. The listener is the plugin's own, so 127.0.0.1, 0.0.0.0 and a NAT-forwarded port all behave the same with no change to the request path. Settings cover bind address, port, API key with a generator, fallback provider and model, reasoning effort, CORS origin and timeout.

DeepSeek Harness native SSH terminal and SFTP workspace with direct agent tools and security sandboxing.

Registers a janitor_scan tool that returns a deterministic, reviewable cleanup plan for session debris (plan docs, scratch scripts) with per-file verdicts and evidence; scan-only, never deletes.

Scheduled update checks for DeepSeek Harness itself and installed plugins, applied only in idle windows — four schedule modes (daily, every N hours/days/minutes), bidirectional plugin whitelist, disk-verified installs, and a web settings panel with hot-reload config.

All-in-One MCP and skill manager with feature-based recognition and native OAuth 2.1 authentication.

A governed registry for MCP connectors (stdio and streamable-http): four-level fail-closed authorization, mcpServers JSON import that rejects malformed args, credential redaction at every output boundary, an agent bridge that returns an honest review verdict instead of a fake approval, and a /mcp-reg operator console plus a settings page.

Runs the local Cursor CLI as a child coding agent from DSH - a `cursor_code` tool plus the native one-shot `cursor_subagent` tool, one mirrored DSH sub-session per run, per-task model choice, resumable conversations, a step timeline with real file diffs, and a usage, model and session panel.

Enforces a check-fix-recheck loop when a task is wrapping up: it auto-runs your tests/lint/audit, blocks any round that still has unfixed problems, and only calls the task done after three consecutive clean full checks, then notifies the user.

Linux desktop notifications and a chime for DeepSeek Harness, sent by the host process so they still arrive with the browser page closed: turn completion, pending questions, tool approvals and turn errors, delivered through notify-send and paplay with a canberra-gtk-play fallback. A browser half adds an in-page toast, a Web Notification and a synthesized Web Audio chime as fallback; every switch lives in a Settings page or a JSON file.

Turn-handoff reminders in the DSH Web UI with a modal or top banner, six browser-synthesized chimes, configurable auto-dismiss, and a sidebar speaker toggle with an unread session list.

LittleStar Meeting Room: bring AI sessions that cannot see each other into one room — for a meeting, or a game of werewolf. By 插件·2代小星辰 (LittleStar) and 辰启星明; the four-piece game layer (targeted visibility, private chat, concurrent asks, elimination) was contributed by 插件·3代小星辰. Any session can host — set the topic and goal, invite members in order, they speak one at a time, and the meeting closes with written minutes; it also runs games like werewolf. Includes 小白使用指南.md, a beginner guide written by 插件·3代小星辰.

Adds an Assist button beside the composer send key. It scans the skills and MCP tools available in the session, ranks them against the text currently in the input box, and lets you inject the selected ones into the running conversation as a steering message.

C++ audit skill: detects the target project's actual C++ standard before making any recommendation, then reports findings across RAII and ownership, move semantics, exception safety, performance and memory, containers and algorithms, concurrency, templates and compile-time, standard boundary, and type safety, ABI and interface design, each with a checkpoint, a reason, a bad-to-good example and the minimum standard.

Converts Office and PDF files in the DSH workspace to Markdown locally — no network, no API quota — before the model reads them. Writes each .md next to its source so only a file path enters the context, and returns byte size, line count, token estimate and a heading outline, for a single file or a whole directory.

Distills daily AI conversations (Doubao, ChatGPT, DeepSeek, Kimi) into structured Markdown notes in your Obsidian vault.

Give the agent a talking face: seven tools that drive a self-hosted Mynah realtime digital human — speak (queued or interrupting), play a gesture clip, interrupt, list live sessions and channels, check health, add text to its knowledge base.

Registers one `ocr` tool for text-only models: it runs the offline script bundled in the repo over a single image or PDF and returns the recognized text — Windows native OCR first, then RapidOCR, then Tesseract. Recognition runs locally with no network request and no API key. Needs Python 3.11 on PATH plus `pip install winocr pillow` (winocr is the preferred engine; RapidOCR and Tesseract are fallbacks). Per-line results for images, per-page results for PDFs.

ZCode-style enhancement of dsh plan mode: a model-initiated enter_plan_mode tool, hard interception of write-class tools at dispatch time (ctx.tools.guard) while planning, and a four-phase planning prompt section (explore, design, review, exit), reusing the official plan/mode state machine and exit review flow.

Complementary web-search provider: every web_search merges the DeepSeek Search API (primary) with serper.dev Google results (secondary) at a fixed 2:1 ratio. Both sides are direct HTTP retrievals with no auxiliary model turn: URL-deduped sources, long DeepSeek summaries clipped into snippets, per-backend timeouts and graceful degradation to whichever backend answers.

Model failover router that queues all connected DSH models, retries with continue text on failure, switches to next model, and quarantines failing models for the day.

White-box memory in two parts: four user-authored long-term tiers (must-follow rules, behavior settings, user profile, optional rules) injected as one system-prompt section, plus a separate temporary working set (cross-session handoffs, stage goals, waiting items) with its own 1200-character budget that stops being injected once an entry is done. The long-term tiers are plain Markdown files the user can read and edit; the agent can only propose them, and nothing is written until the user accepts in the Chinese panel. Temporary entries are written only when the user explicitly asks, and live in <DSH_HOME>/temp-memory.json (created on first write), not in the memory directory. No auto-capture, no background writes, no network.

Routes DSH web search through the current session's model and a self-hosted retrieval backend.