Plugins

Syncs the DSH home directory and Mnemon memory data through a git remote: pull, push, sync on an interval, cross-device migration, initialization backups, and conflict detection that stops before pushing and keeps local commits. A panel lists up to 100 recent operations with their backup paths.

Defines multi-agent team rosters as YAML or JSON files, validates them, bridges each member into dsh-agent-teams member arguments, and verifies the result against the running team.

Ships BMAD-Method inside the plugin: the seven modules are laid down on first open, no installer run and no download, the method's 90 agent and workflow skills come in the package, and each module can be turned off by writing the disabled flag into the profile patch.

Multi-agent agent team of employee AI subagents for DeepSeek Harness — dsh-company: employee management, CEO bidding, n8n workflow orchestration, drag and drop canvas. DeepSeek Harness plugin with seven built-in roles (project director, product manager, tech lead, engineer, data analyst, QA, reviewer), each with its own persona, model and tool whitelist. Broadcast a mission and the employees bid for it themselves — the first line of their answer must be 【accept】 or 【decline】 — so accepted work runs in parallel instead of down a pipeline. The workflow canvas wires employee nodes with the start node, references each upstream result with {{input}}, and runs the graph in topological layers: parallel across employees, serial per employee, downstream skipped when upstream fails. Ships a web panel for the employee management tab, the mission board and each employee's transcript, the /dsh-company slash command, a live mirror into the built-in agent-team panel, deletable missions, and a private memory directory per employee.

RunningHub cloud ComfyUI workflows as a background task pool: submit a batch, and a watcher dispatches it within a concurrency budget, polls each job, downloads every output to disk, and wakes the session when the pool drains. A shared ledger records batches, costs, instance tiers, submit and finish times, and per-file save paths, with a live board in the right sidebar.

Bilibili curation floating panel for DSH: learns your interests on a probability schedule and decays them by half-life, picks videos inside a token budget derived from a daily money cap, and mentions you only when a fit score passes a threshold. Comments are off by default; once enabled they run in draft-and-confirm or self-checked auto mode under a daily quota, with a hard no-comment rule for political, government and frontier-science topics, and comment text is scrubbed for account identifiers before it can be posted. It signs in through your own Bilibili account cookies; a separate posting account is recommended.

Manage skill experts in the sidebar and summon them from the composer or @ menu.

Bundled Pixiv skill backed by a Python CLI: search illustrations, manga and novels, browse daily/weekly/monthly/rookie/original/AI/R-18 rankings, look up works, novel series and artists, and download originals, with multi-page works merged into PDF, ugoira converted to GIF and novel series merged into TXT; requires Python 3.9+ with requests, img2pdf and Pillow installed (pip install -r requirements.txt), and R-18 content needs your own Pixiv cookie.

Let DSH speak: every reply is rewritten into natural spoken language before being read aloud, with code, tables, and Markdown handled gracefully. Works out of the box with system TTS; Doubao (third-party API) adds dialects and expressive voices; the local Kokoro engine runs offline to keep content on your device. Supports both DSH desktop and dsh web. Optional session sounds tell you when the agent is working, finished, or waiting for your answer — with swappable built-in tones and support for your own audio.

Qwen media tools: multimodal video and audio understanding, text-to-speech with preset voices, and scene audio generation, through Alibaba Cloud DashScope.

Multi-engine image generation inside the conversation: an agent tool plus a composer-dock panel with an on/off switch, engine presets (Volcengine Ark, SiliconFlow, OpenAI-compatible, custom) and local spend tracking. You supply the API key for the engine you pick.

Connects DeepSeek Harness to a local SolidWorks install through an MCP server, with a read-only default mode and per-call confirmation for high-risk operations. Windows 10/11 x64 only; needs Python 3.10+ with mcp, pydantic and jsonschema (plus pywin32 and comtypes on Windows) and a local SolidWorks 2023-2026 installed and started at least once.

Native discovery and per-agent loading of built-in, plugin and MCP tools, with BM25 and optional Ollama or OpenAI-compatible embeddings.

One MCP tool surface over two Windows desktop-automation backends (dsh-computer-use-win and windows-mcp). Both must be installed first: dsh-computer-use-win as a DSH plugin, windows-mcp via uv tool install windows-mcp --python 3.14, then run the bundled scripts/patch-backend.mjs once (it rewrites the local windows-mcp source; without it screenshot/read_webpage/launch_app fail) and disable the standalone wincu mount. It merges their 44 tools into 29 by dropping 10 duplicates, 4 dangerous ones (PowerShell, Registry, FileSystem, Process) and 1 tool-management entry, and blocks dangerous calls before forwarding through a reversibility-graded guard.

Mounts the REA (Reverse Engineer Anything) MCP server through two gateway tools instead of registering its 200+ tool schemas on every request: rea_list searches the catalog or returns one tool's input schema, and rea_call invokes that tool, each call spawning a short-lived rea mcp process, so the tool definitions sent per step drop from about 780 KB (207 tools as measured in REA 6.5.0) to about 1-2 KB. When no rea entry point is found it installs rea-agents into a plugin-private directory with the pnpm bundled in DSH (dependency build scripts skipped) and links the reverse-engineer-anything and rea-gateway skills into the skills directory without overwriting existing ones.

QQ Zone poster for DeepSeek Harness: scan a QQ login once, then let the model publish posts with mood-matched sticker images, with a daily cap and a silent headless Chrome session.

Scientific figures as code: agents create a versioned figure IR, patch it incrementally (one node at a time — untouched objects never move), and export publication-ready, true-vector SVG / PDF / PPTX. Seven papercanvas_* tools (create, get, patch, layout, lint, render, inspect) over a deterministic engine with FC001-FC008 quality lint and CJK-aware font resolution. Verified on DSH 0.1.5-rc.3 through 0.2.2-alpha.2. By the author of dsh-sessionflow. Requires the PaperCanvas engine installed separately: Python 3.11+ and the papercanvas CLI (not yet on PyPI — install it from this repository, put it on PATH or set papercanvasBin); the plugin is a thin adapter and ships no engine.

Chinese writing-register plugin for the Gcores/机核 editor 白广大: registers a 40 KB style skill plus style_lint / style_audit / style_rules tools — 4 hard-error and 27 warning checks (banned connectives, slogan inflation, empty quantifiers, uniform paragraphs, unsourced statistics, AI clichés), with thresholds calibrated on 444 articles / 802k characters; a settings page adds an on/off switch, single and batch trial runs, and JSON threshold overrides.

Local-first experience library for DeepSeek Harness: indexed markdown knowledge with error-signature retrieval, failure capture, gap-to-entry closure, and a sidebar panel. Ships a sanitized starter library. No network access.

Project-scoped SQLite memory with progressive retrieval, bounded context, a composer button and visual review. Install the repository-root DSH bundle, then run /learning-init with an absolute project path using local Python 3.10+.

Local-first knowledge base for DeepSeek Harness: each base is a folder of UTF-8 text, text PDFs and OCR-read images; the plugin retrieves from the session's selected bases before every turn with Chinese n-gram and BM25, answers carry citations that open in a right-sidebar reader, and the agent gets five kb_* tools (list, search, read, stats, propose text) plus /kb commands for create, use, files, search and rescan.

Long-conversation memory for DeepSeek Harness (DSH): keeps settled decisions, user corrections and exact numbers, with their conditions and sources, available across compaction and restarts in 100-1000+ turn sessions; one small budgeted evidence block per model step, with enabled skill rules re-applied in the same block.

Session replay view for DSH conversations: replays a session turn by turn at reading pace for prompts and answers while reasoning and tool output stream past, and every prompt is followed by a one-second pause. It can also re-run the original prompts in a new session, and record the replay — screen recording the conversation itself — to a WebM video through a 1280x720 canvas, with no OS screen-recording permission.

Export the current session as a readable Markdown file with dialogue only: user messages, assistant text, and ask_user_question answers, from a session-header button or the /md command.