Plugins

Long-term memory ported from the OpenAI Codex memory pipeline: sessions idle for 6 hours are extracted in the background by a chosen model, then merged by a hidden agent into a Markdown memory folder at most every 6 hours; new agents get the memory summary in the system prompt plus four tools to list, read and search memory and save corrections. Models are chosen on a settings page or with /memories; an optional filter keeps a notes folder out of memory.

Shares one set of Markdown memory files between DSH and WorkBuddy: project memory, user memory and the user profile are injected into the session context, while daily notes stay out of the prompt and are read, appended and archived through the tools. Six tools (status, read, search, append, edit, prune) write back through a cross-process lock, revision check, backup and atomic write, and a panel in the left rail and the right sidebar edits entries, sections and whole files.

A long-form fiction writing desk inside DeepSeek Harness: a shelf of books, a three-column editor (chapter tree, prose, AI assistant), outlines, character/world/foreshadowing notes, story archives, and an AI assistant whose prose and note edits are reviewed before they are written. Interface and AI prompts are bilingual (Chinese and English), manuscripts stay local in SQLite, and the model uses the DeepSeek service already configured in Harness.

UI design knowledge base: 99 filterable design cards with live demos, implementation prompts and per-design usage notes, plus an agent tool that finds and applies them.

Writes engineering decisions down the way DSH documents its own — every non-trivial change leaves a Markdown note under .agents/notes/<lifecycle>/<class>/ holding the problem, the decision, the rejected alternatives and the consequences, committed to git and reviewable like code. It supplies the workflow for that: five tools (note_search, note_read, note_write, note_verify, note_skip), an end-of-turn hook that asks a code-changing turn to either write a note or record an explicit skip, the tree/format/archived checks, and the write-notes-like-deepseek skill bundled in the package and mounted by the plugin. A sidebar board that renders the notes into four views (architecture baseline, timeline, rejected proposals, decision list) comes with it; the notes themselves stay plain Markdown.

Port of MiMo Code's infinite-context memory: as the window fills it asks the model in the background (the current provider route unless writerProvider/writerModel is set) to write a structured checkpoint and a project MEMORY.md, then, ahead of DSH's native compaction, replaces the visible window with a rebuilt dump of tasks, checkpoint, recent user messages, memory and notes, falling back to a MiMo-style compaction. Memory files live under $DSH_HOME/mimo-context (per session, per project, plus a read-only global one); it adds mimo_* memory, history, checkpoint and rebuild tools and /mimo-* commands, with dream/distill off by default. Needs DSH 0.2.2-rc.1 or a later 0.2.x.

Local-first knowledge base for DeepSeek Harness: workspace Markdown notes with provenance, review status, BM25 search, and four km_* tools.

Rich-text notes with images, links, symbols, categories, backgrounds, heading folding, and export.

Adds a notepad to the DSH Web UI that floats as an edge-snapping, resizable card or docks into the real bottom layout row, switchable at runtime with shared text and images; notes auto-save (text in localStorage, images in IndexedDB) and follow the active theme.

Quest tree for DeepSeek Harness: track a long-running task as a main quest plus a branch tree, with a sidebar panel showing status, progress, markdown notes and a 'you are here' marker to prevent drift.

Adds 17 barotrauma_* tools to DeepSeek Harness for making Barotrauma mods: environment probing, mod skeleton, item and weapon XML, afflictions, creatures, random events, Lua and C# script scaffolding, localization, validation, icon generation, zip packaging and Steam Workshop publish notes. barotrauma_gen_icon sends your prompt to image.pollinations.ai and writes the returned image into the mod folder; on Windows the Steam install path is read with reg query.

Zotero bridge: search, read, add, update and trash items in a local Zotero library, read PDF annotations and notes, pull indexed full text, and find related papers by DOI. Needs Zotero 7 or later with its local API enabled; the first write triggers a Zotero authorization prompt (the key is stored in plugin-data/dsh-zotero/auth.json) and permanent deletion asks for confirmation. Finding and adding items query OpenAlex, Crossref, arXiv, PubMed and Open Library. Uses the same package name (dsh-zotero) as Vncntvx/dsh-zotero, so only one of the two can be installed per profile.

Local jot-down notes for DSH: one Markdown file per note, stored under ~/dsh随手笔记 by default (portable home path, no drive-letter assumption). Redesigned card UI, trash bin (deletions restorable for 30 days), custom tag management with stable per-tag colors (create tags ahead of use, delete one everywhere), compact/comfortable density, adjustable font size and line width, flat quick-scan list, full-text search, Markdown preview, autosave, batch deletion, zero-dependency Office text extraction, and AI write-back from conversations. Heavily reworked from dsh-knowledge-lite; pick the old library via the in-app root picker to migrate.

Local long-term memory core for DeepSeek Harness with plain-text notes, a bounded index, five tools, optional host-model consolidation and auditable evidence. It appends every user and assistant message under 32,000 characters to ~/.companion/cold/<date>.jsonl in plain text, with no built-in cleanup, and adds a short index of its notes to every system prompt; the optional model-based consolidation is off by default and, once on, sends those messages to the session's model.

Session archive with tags, notes and per-session metrics, plus a Plan-Do-Check-Act loop over the archived sessions that produces prompt and AGENTS.md rules and re-measures them on later sessions. Runs locally without calling a model.

Sticky notes wall in the sidebar: rich text, images, ink, drag-to-reorder, per-note alarms, and scheduled delivery of a note into a session. Its read-only /state and /media routes have no authentication.

A reading room kept for the agent: through reading_* tools it reads a book from ~/.dsh/reading one chapter at a time, writes notes beside chapters and keeps its own writing there, and a panel shows its reading progress. It adds lines to the system prompt telling the agent the room is its own and optional. Not a to-do list.

Toggleable Side Memo notes panel for DeepSeek Harness: labels/tags, resizable inline images, favorites pinned to the top, quick copy, drag a memo into the chat box to paste it, save a conversation or a single prompt as a memo, right-click memo actions, an

DSH 会议助手：本机麦克风采集与 DashScope 流式转写、会前准备、会中发言建议与会后记录。独立插件，不依赖 Agent Mesh 或任何平台连接。

Learning-plan tracker with local progress, notes and JSON progress backup. Ships one original six-node example roadmap; adding your own roadmaps means editing roadmaps.ts and rebuilding the plugin. Needs the dsh-studio-projects runtime and the dsh-worktable container from 1997liuyh-boop/dsh-studio-library installed first.

Matches each new task against installed skills with deterministic keyword scoring and injects the best one without being asked; records effective operations as per-skill notes for later runs.

AI-assisted reading workspace for DSH with a local TXT/Markdown/EPUB/PDF library, annotations, prompt presets, long notes, memory, export and companion tools.

Local Markdown notes panel: import any local folder, browse its Markdown tree in the sidebar, edit files with auto-save, and search across every imported folder. Shows read-only Git context for each file — status, diff against HEAD with per-line change bars, and line blame — and exposes six notes_* tools so the agent works on the same folders. Files stay where they are on disk; the plugin never commits, stages or checks out.

Mendeley-style paper library for DeepSeek Harness: import PDFs, read them in the panel with highlights, notes and questions, turn AI answers into note cards, compare 2–6 papers, build a typed relation graph (manual, AI-discovered, or citations parsed from PDF references), and keep everything as plain files in a folder any cloud drive can sync.