Plugins

A long-form Chinese novel workbench inside DeepSeek Harness: read, edit and annotate the manuscript in place; AI reports problems with ready-to-adopt revision drafts, and the text changes only when you accept. A ledger for characters, facts and foreshadowing plus a world map keep a long novel consistent, and AI can continue, expand or rewrite chapters.

WeChat personal assistant that manages Apple Calendar and Reminders with confirmation before writes and sends scheduled WeChat messages, for macOS 14+ on Apple Silicon.

Codex, Grok Build, Cursor, Pi and ZCode presets with native conversations, code review, document conversion and workflow controls.

Counts agent steps and, every N of them, adds a progress-report instruction to the batch that is about to be sent, so the agent states what it is doing, what it finished and what comes next, then carries on without stopping for you. Interval, prompt text and a main-agent-only default are editable in its settings page.

Local Markdown long-term memory for DSH: an Obsidian-compatible vault, a sidebar management page, four memory tools, and budgeted per-turn recall.

Per-turn TPS and TTFT statistics for DeepSeek Harness: model time, tool time, time-to-first-token, and output speed per turn.

Shows download, install, fetch and search progress in a floating overlay or in a compact chip above the composer, and lists background jobs that have no progress to draw.

Embeds chat.deepseek.com directly inside DeepSeek Harness desktop with persistent login sessions and dual-entry chat panels.

Russian for the dsh web GUI: the whole interface plus the menus of 520 third-party plugins — 584 sections, 67,666 strings.

Adds an extensible Functions menu to the DSH top bar, where plugins register one-click actions with staged confirmations and submenus. Since 0.4.0 it bundles a built-in "Restart DSH" action, so no separate restart plugin is needed.

AI short-drama workspace for DeepSeek Harness, with a dedicated drama agent preset, 35 agent tools and 10 bundled skills covering outline, characters, script, storyboard and export.

Continues numbered and bulleted lists in the DSH composer, so Shift+Enter after "1. item" starts a new line with the next marker.

Turn another computer into a remote Agent teammate: the worker runs its own DSH and a full Agent loop locally, so dispatched tasks keep running when the link drops and its model credentials never leave that machine. The hub dispatches through 8 remote_* tools, receives only a handoff summary plus an artifact manifest, and pulls finished products on demand — intermediate artifacts stay on the worker. Pairing-code onboarding, direct or relay transport for NAT, Web panel on both sides.

Read-only Git panel for the current workspace — branch, changed files, diffs and image before/after compare — plus three capsules above the composer (Git status, tool and skill activity, session running time) and per-session change attribution with review progress.

Multi-agent roundtable discussion: each member runs as a subagent on its own model and speaks in turn, a host summarizes each round, and termination writes meeting-minutes Markdown.

Deep-reading workflow for books: import EPUB/Markdown/TXT/PDF into chapters, read one chapter per subagent so the source text never enters the main session, keep \[n]-anchored chapter memories for cross-session continuation, and export notes or an EPUB.

Requirements interview before work starts: judges whether a request is clear, asks focused questions when it is not, in strict mode denies editing tools until the user confirms a four-part summary in chat, and keeps confirmed preferences in two-layer memory.

Finishes 头歌 (EduCoder) homework over the platform JSON API, with no browser automation. educoder_sweep scans every course for homeworks that still have unfinished challenges — complete homeworks are skipped without reading a single challenge, so a full-account scan costs no model tokens — and can finish them; multiple-choice challenges also cost no model tokens, because the platform returns the answer key in any submission response, while code challenges are generated by the model, uploaded with evaluate:0 and graded once per challenge, then retried using the error report the platform returns. Three tools: educoder_sweep, educoder_homework, educoder_account.

Keeps project decisions in plain markdown instead of chat history: one main document plus per-module documents, every entry carrying a file:line citation. The agent interviews you to turn open questions into settled decisions, and a five-dimension score (task complexity, extensibility, maintainability, code quality, reusability) is derived from the documents and shown on a button beside the model picker. A session can bind several projects; projects are sized small/medium/large, which widens the entry limits, and a reusable-pipeline section in the main document is injected when its trigger words appear.

Hierarchical workspace tree plus browser-style session tabs for the DSH sidebar: a "/" in a workspace or session title creates a virtual folder, with sections, pinning, time buckets, drag ordering, unified 6px status dots, per-row appearance customization (colour, glow, weight, icon) and a floating full-text search over session titles and message bodies. Several settings switches restore any behaviour to the host default, and a host-side route deletes sessions permanently (DSH ships archive only). Fork of KannaKuron/dsh-better-workspace.

Heads-up Texas Hold'em training with an independent AI opponent, persistent public-action memory, and decision reviews.

Turn a set of DSH plugins into one pasteable share code, then install it plugin by plugin — with a QR code to carry it to a phone.

Discovers rule files from every convention OMP supports and injects them into the system prompt as an always-apply layer plus a rulebook index, then enforces them with time-traveling stream rules that abort violating output mid-turn, deny a matching tool call, fold a reminder into that call's result, or ask a judge model about the finished reply. The same tool lets the model write new rules, and an audit panel on the Plugins page says why each rule is in force.

Surfaces important facts and trade-offs from the current task, with on-demand explanations and optional discussion in the main session.