Plugins

Memory for long-form RP and companion chats: three fading layers, per-turn recall, source labels, character and world boundaries, a relationship graph, image attachments and a WebUI.

Shows per-turn and session costs, provider/model breakdowns, subscription usage and the signed-in DeepSeek account balance in the sidebar.

Lets the model turn an answer into a web page that lives right inside the chat. The show_page tool either fills one of 8 built-in templates (report, compare, checklist, dashboard, timeline, flashcards, quiz, flow), where the model writes only the content and the plugin does layout, interaction and saving, or saves a full page the model wrote itself (calculators, games, custom layouts). Hand-written pages get the same design as base styles inside a CSS @layer, so the page's own CSS still wins. The page appears under the reply at its content height, interactive and following DSH's light/dark theme. The page's localStorage is stored in DSH per page, so what users tick or type survives reopening and new versions, and the same page open in the chat and the right sidebar stays in sync. Each edit is saved as a new version you can flip back to. After saving, the page is checked in a hidden local Chrome or Edge (script errors, horizontal overflow, dark mode not applied) and problems go back to the model. The right sidebar shows the page full-size with versions, a gallery of all pages and export to PNG, PDF or HTML. Pages run in a sandboxed, separate-origin frame with a strict CSP: no access to DSH data and no fetch, XHR or WebSocket. Works on phones with dsh-mobile.

Reach your DeepSeek Harness assistant from Feishu / Lark: a dsh plugin built on the official Lark CLI

Adds a Restart button to the DeepSeek Harness title bar so newly installed plugins can take effect. The first click arms the button and the second one ends the current desktop instance and relaunches the executable. The restart runs in a PowerShell script created through WMI, outside the application job object and without a console window, and every step is written to $DSH_HOME/logs/dsh-restart-plugin.log. Windows desktop app only.

Layered long-term memory for DeepSeek Harness: each turn injects a persona frame plus a relevance-ranked memory index while full transcripts stay on disk and are expanded on demand, so the vault never floods the context window. Facts are slot-keyed, so a changed mind retires the old value instead of recalling two contradictory answers. The persona frame is derived from memory by the model itself, behind three anti-pollution gates (rejecting development chatter, blocking echoes of its own injection, and merging restatements by shared content words). Periodic consolidation merges near-duplicates, rolls long topics into digests, and archives low-value old entries by tagging only — raw text is never deleted. The append-only store is compacted in place (measured 4.7 MB to 355 KB). Zero third-party dependencies, no build step. Note: per-turn injection inevitably lowers prompt-cache hit rates, so this plugin is not recommended for work scenarios.

Browses Bilibili on your behalf and actually takes part: search videos, like, coin, send danmaku, post comments, reply to other people, and keep replying when someone answers back. Reads the video's Bilibili AI subtitle (or transcribes the audio locally when there is none) so its comments talk about what the video really says, and it reads that same content before choosing whom to reply to. Instead of copying other people's sentences, it distills rules about how to sound like a person and gets less AI-ish the more it runs. Skips videos it already browsed, de-duplicates replies per thread, and after too many rounds with the same person in one comment section it reviews whether the exchange has any substance -- if it looks like two bots complimenting each other, it says one last line and stops replying to that person there. Checks whether each comment is genuinely public or only visible to your own account, can scan any time window (last hour / day / 3 days / all) and lists the ones others cannot see, warning you on the overview when replies got folded. Six-tab settings panel where every change takes effect immediately with no restart, a manual run that queues behind the background listener instead of being refused, and an environment self-check that tells you exactly what to install. Requires Python 3 for the Bilibili actions; the built-in check lists any missing packages.

Builds presentations by driving the LibreOffice installed on the machine over UNO instead of writing OOXML in JavaScript: `lo_build` opens a real Impress document from structured slides and lets LibreOffice export editable PPTX/ODP plus PDF and per-slide PNG, `lo_render` re-renders an existing PPTX/PPT/ODP, `lo_convert` converts between Office formats, `lo_probe` reports the detected soffice path, version and UNO bridge, and `lo_config` edits the stored settings. Needs a local LibreOffice (verified on 26.8.1.1); it uses the bundled Python, so no system Python is needed, and it keeps one warm soffice process between calls. Fully verified on Windows only; on Linux it falls back to the system python3, which needs python3-uno.

DSH remote-development local plugin: host registry, SSH tunnels, remote workspace/session discovery, an authenticated Connection RPC surface, agent tools, and a Settings page.

Bridge DeepSeek Harness sessions to Cherry Studio desktops over the LAN - QR pairing, 14 cherry_* tools for agents/sessions/history/interactions, and a settings page for devices and per-workspace tool scope.

Eight academic skills bundled for one-command install — publication-ready academic figures designed from real data with visual review, AI-text humanization, paper polishing and rewriting, PubMed citation verification (DOI/PMID reconciliation), Chinese citation and medical-book reference checks, and WeChat medical article structuring. Three skills run fully local, five call external services only when used (NCBI E-utilities, DOI resolvers, publishing platforms), declared per skill. Zero telemetry, no install-time scripts. Skills run local Python 3.8+; the academic-figures skill also needs its pip dependencies installed.

All-media (video/audio/image) sibling of liustack/modlens: turns local media into structured evidence (timeline / transcript / layout regions) injected into the conversation so text-only models can cite concrete frames, seconds and speakers — Evidence, not imagination. One failover chain per media, recorded hop-by-hop in meta.attempts (a fallback is never silent): host-registered multimodal models first, then a configured OpenAI-compatible endpoint (comma-separated multi-key rotation), then local ffmpeg frame-sampling / a local ASR CLI, degrading to metadata-only as a last resort. Absorbs modlens battle-tested rules — cooldown keyed only on auth/quota failures and softened to chain-tail (never dropped), redact-before-clip error text, magic-byte sniff that never trusts file extensions. Optional vision-bridge registers a media-lens wrapper into the host model selector so a pure-text model gains image/audio/video sight. Registering / authenticating multimodal models and true inline audio-video upload lean on two companion plugins, declared as optional peers (absence auto-degrades, never errors): dsh-thinking-levels fronts the model config / registration surface, dsh-llm-openai-completions takes over the OpenAI-compatible transport that serializes file blocks into video_url / input_audio. Privacy: media goes to remote providers by default; strictLocalOnly keeps content on-machine. Prerequisites not shipped with the package: ffmpeg/ffprobe on PATH (frame sampling / reencode / duration probe; degrades to metadata when missing) and a user-configured local ASR CLI for the audio fallback. Hosts 0.1.7 / 0.2.0 / 0.2.1 (6 rc, exact engines.dsh enum). npm: dsh-media-lens.

A searchable, workspace-grouped multi-select panel over DSH conversations for batch delete, archive and pin, with Shift-range and Ctrl-add selection.

Brings WorkBuddy free/paid models (domestic + international) into DSH without the desktop app, published as @bushizhanghan/dsh-workmate. This package is a maintained fork of Faide-cyber/dsh-workbuddy (whose upstream entry is already listed in this catalogue); do NOT co-install both, as they register the same provider id "workbuddy-ai" and will collide. v1.2.7 relocates over-long system prompts past the domestic gateway channel-risk classifier, rewrites the DSH brand string, and adds a debug dump; supports dsh 0.2.0-rc.2 through 0.2.1 (incl. prereleases).

Render interactive diagrams, charts and staged learning visuals alongside native prose in DeepSeek Harness.

Makes the agent narrate its process: every turn opens with how it reads the message and what it will do first, then short interim notes between tool-call batches, with in-app settings for style, sentence cap and toggles.

Long-term memory for DSH stored as plain Markdown. Real user/assistant exchanges are captured into daily session files without a model call; an LLM pass then distils them into memories that are injected into the system prompt each turn. Retrieval is local (CJK bigram + BM25) in three levels — index, summary, full text — so the model only pays for the detail it asks for. Memories carry priorities; P0 and user-written entries are never changed automatically, and forgetting marks an entry stale instead of deleting it, so the whole chain stays auditable in status.md. Six tools cover recall, write, status, extract, feedback and the skill library; when a cluster of memories is ready to become a skill, a card appears at the end of the turn, writes the SKILL.md on click, and installs it only after you approve. Learning is on by default and follows the conversation model; turning the master switch off stops the LLM entirely while capture keeps working. Failures are surfaced in the prompt and in status.md instead of being swallowed. Pure JavaScript, no third-party runtime dependencies beyond the harness packages.

Provides three learning skills for course notes, conceptual explanations, and self-contained lecture chapters, with bundled references and course helper scripts.

GitHub project search and analysis: describe your idea in one sentence and the agent searches GitHub for related projects, ranks them by relevance weighted with stars, and returns a structured card — last updated, relevance, stars, weighted score, language and a one-line description for each project. Pick a few repositories and it reads their README, dependency manifest, directory tree and key files to lay out their architecture — so you stand on the shoulders of giants before you start building.

An OpenCode Go model source for DSH: it reads the opencode-go catalog from Models.dev and serves it on a separate opencode-go-live route, leaving the built-in opencode-go route in place. The catalog snapshot is cached under the DSH home and a failed refresh keeps the last successful one; its own Models settings card edits the route key and can render OpenCode Go subscription usage.

Recall a message you already sent: the session log stays append-only and nothing is deleted, while the recalled turns leave the model context and the transcript, the text returns to the composer, and the files those turns changed wait in a quarantine you can review and clear from its own settings page.

One authenticated OpenAI-compatible endpoint (built on CLIProxyAPI) for dsh and other tools: gathers WorkBuddy, OpenCode Zen, local Ollama / LM Studio and CLI subscriptions such as Codex and Kimi, signs in from the dsh page without installing their CLIs, and syncs the picked models into dsh grouped by source. The settings page has a switch per source with free / paid tags, plan usage, API keys with 24-hour stats, per-source model picking, and update / uninstall. Runs on macOS, Linux (including next to dsh-vps) and Windows.

Registers a real-browser URL-to-text MCP server in a DSH profile: fetch_url and fetch_urls render JavaScript-heavy and anti-bot Chinese pages (Zhihu, CSDN, Juejin, cnblogs) and return clean article text as JSON with per-tier diagnostics, and crawler_status reports whether the browser is available. Needs a one-time Python dependency setup after install.

Command Code subscription tiers (Go / GOAT / Pro / Max) published as `llm-pi-ai` route profiles rather than an adapter: the live /provider/v1/models list and the plan page capability catalog are joined, each model lands on the OpenAI Chat, OpenAI Responses or Anthropic Messages route the gateway's own supported endpoints call for, switching tier deletes the routes the previous one generated, and the same credential drives five-hour and weekly usage windows, credit balances and a web-search provider.