Plugins

One-click prompt refinement for the DSH composer: the ✦ button next to Send rewrites the draft into an agent-ready prompt in Standard or Light mode, with Revert and Retry; asks follow-up questions through the question card when the draft is missing information; checks that paths, identifiers, numbers, URLs, inline code and @mentions survive the rewrite and adds back any that were dropped; runs on the conversation model or a dedicated model chosen on the Plugins page.

Replaces the sidebar account label — the phone number, email, username, or account name — and avatar with your own display name and image. A new "Edit display name" row in the account menu opens one dialog for both, so screenshots do not expose the original.

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.

A no-limit Texas holdem table where you are the only human. Eight seats: you plus seven LLM agents written on AI-founder personas (Altman, Dario Amodei, Musk, Liang Wenfeng, Jensen Huang, Lei Jun, Zhang Yiming). Each agent bets through the currently selected host model and falls back to its own heuristic style when no model is available; every agent only ever sees its own hole cards, because the prompt is built from that seat alone. A hand ends by revealing the winner hole cards, with a flip animation replayed on every community card, and a rail keeps a live action-and-table-talk timeline. LAN mode (off by default, single-machine play is unaffected) lets another install on the same network join this very table: the host shows a six-digit room code, UDP broadcast discovers nearby tables, and a joiner randomly takes over one bot seat, with the seat handed back to its character on leave or after twenty seconds of silence. The room code is never put on the wire and every snapshot is generated for one viewer seat, so nobody, not even the host, can read another player hole cards. Fork of BubblePtr/dsh-holdem (MIT), credited in the README and in the UI.

One-click restart for the DSH desktop client, from five entries you can toggle individually: a sidebar panel, the sidebar footer, the conversation header, a Settings → General row, or the /restart slash command. The app is closed gracefully (only the main process is signalled, so the host can flush session logs) and relaunched by a detached helper that survives the shutdown; optional guards wait for running tasks or an in-progress plugin install, results land in a receipt, requests are fenced to loopback same-origin, and every switch is persisted in the profile config file. Only the four UI entries can be switched off; the slash command is always available.

Diagnoses third-party LLM API endpoints from the sidebar: a four-layer check (DNS/TCP/TLS, key and model, a real streaming conversation, then timed repeat runs) reporting time-to-first-token, output speed and success rate, with every failure attributed to the step it stalled at and the byte count that actually left the machine. 37 provider presets, no model names hardcoded.

Preset doctor for agent presets: scans home/profile `cordis.patch.yml` declarations and legacy `.agent-presets/` dirs across host lines 0.1.0→0.2.0, shows every detected preset as an official-style card (source/era badges, broken red border, per-finding details, current DSH version + era catalog), and syncs a preset to the current syntax per-card behind an explicit confirm dialog (.bak backup + atomic write; report-level findings are never written — startup scans are report-only). npm: `dsh-preset-doctor`.

Desktop notification and sound when a turn completes, the agent asks a question, or an approval is requested, each independently toggleable, plus an activity ball that lists live sessions and jumps back to one.

Runs one-time and recurring Agent tasks in fresh DSH sessions, with natural-language schedules, visual schedule editing and session-linked history. Supports custom models, skills, pause on failure, and permission settings.

Builds a Markdown knowledge base from your course materials, then drills you until every knowledge point reaches your target accuracy; each answer must include your reasoning.

Lighten the load on your agent: one-click toggles for skills and MCP, so the context holds only what this turn actually needs. Skills and MCP servers you switch off are no longer injected into the model context — cleaner, and lighter on tokens.

Tavily backend for the built-in web search: an in-process endpoint at 127.0.0.1:8787/v1 turns its requests into Tavily searches; up to 10 API keys stay in the DSH credential store, rotate per request and switch on auth, rate-limit or quota errors; a settings card manages keys, runs a test search and points web search at the endpoint.

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.

Automated data masking tool based on national standards, automatically identifying sensitive fields like ID cards and phone numbers.

Data lineage tracking tool, mapping the full lifecycle of data to meet source legality requirements.

Quantitative data quality scoring based on GB/T 36344-2018, providing auditable quality reports for data asset capitalization.

Bridges the CursorTouch/Windows-MCP desktop automation server into DSH through the official MCP client bridge, registering its screenshot, UI tree snapshot, mouse and keyboard input, clipboard, process and registry tools as mcp__windows__* tools for the model, and adds a windows_mcp_doctor diagnostic tool. Computer Use is off by default and is switched on from the plugin panel.

A memory subsystem for DeepSeek Harness: full ontological memory (event/fact/rule three-ontology) plus a goal system (goal.js) — belief values written via Bridge trigger words, goal activation scored at read time (0.25 domain + 0.35 bridge + 0.40 sim) to recover goal/value long-range associations, journal ledger, episodic archive, low-confidence decay, memory_append/read/consolidate plus linghun_goal_update/get/state closed loop. Independent of the original dsh-linghun line. 16/16 on LoCoMo-Plus.

Pulls GitHub repo content into DSH conversations at start and writes extracted long-term memory back with SHA-locked multi-file commits at conversation end; per-file read/write permissions, native settings page, turn indicators and global change notices.

A daily journal the agent keeps: the journal_record tool appends dated todos and memories, with optional detail and tags, to one JSON file per day under the DSH home; the right sidebar draws those files as a month calendar marked per day by open todos, finished todos or memories, with checkable todos and the selected day's entries below; and the duty sentence it adds to the system prompt is editable from the calendar itself.

Long-term memory scoped to the DSH workspace. A session opens with a one-line-per-memory index - bodies are fetched on demand by the `memory` tool, so the same character budget holds about three times as many entries - and when a turn ends a purely local search over the last user message pulls the matching memories into the next briefing at no model cost. Optional auto-capture distils a stretch of conversation into structured memories; in draft mode they wait in an approval queue instead of landing directly. A portrait layer keeps a workspace and a global picture, rewritten from the journal and the records, with the same draft-or-direct choice. Settings → Memory holds the archive, list and timeline views, a health check (duplicate clusters, stale entries, broken links), the supersede version chain with rollback, and a read-only scan that imports existing WorkBuddy / Qoder memory files.

Tidy the model list of DeepSeek Harness: hide any model from the selector, rename models and channels while keeping the provider billing rate, and arrange both with arrows. Search, per-provider batch toggles, back to top, one-click restore. Display only — no provider configuration is rewritten.

Model routing groups for the session model picker: bind a group to a session, and once a model exhausts every retry policy the plugin records the failure with its error code, switches to the next usable model in the group and continues the same turn. Groups carry per-model priority, Beijing-time active windows and expiry dates; subagents inherit their parent session's group and can also pick models or bind their own; models served by dsh-our-free-model retry another account before failing over.

A Neon Genesis Evangelion-inspired skin for DeepSeek Harness: Title Card (light) and Central Dogma (dark) modes over one removable override layer, 55 measured color tokens, a wallpaper paint layer whose two surface alphas are solved against a WCAG contrast budget, a boot mask and a brand mark. It also rewrites the sidebar identity line, replaces the new-session heading, hides six official entries, and reshapes the composer control row. Unofficial fan work, non-commercial: the background pictures are fetched from third-party URLs at paint time, not shipped in the repository.