Plugins

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.

Registers external ACP (Agent Client Protocol) agents such as Codex, Claude Code and Gemini CLI as DSH model providers: maps each agent's thought_level options to the reasoning-effort selector, forwards images when the agent advertises promptCapabilities.image, and routes permission requests to DSH's approval dialog; ships an ACP Agents settings page that imports Zed agent_servers JSON and discovers models.

Registers an apiCost session projection that estimates DeepSeek spend from reported token usage at published peak / off-peak rates, in CNY and USD, over self, delegation-tree or verified-team scopes: a composer-dock pill and its detail panel, the /cost command, a session_cost tool, and GET routes for the view and the current rate card. A panel switch parks model requests at the llm/stream waterfall while peak rates apply and dispatches them unchanged once the off-peak window opens, so the call is billed at the off-peak rate with no failure reported to the session.

Select text in any Windows app and read the translation in an always-on-top card next to the cursor: streaming text follows the newest line, up to 15000 characters per request. Windows only.

Todo sidecar for DeepSeek Harness: turn selected text into a todo, capture todos from a conversation, review them in a left sidebar panel, get due reminders as an in-app popup or an IM bot message, and export everything to HTML, Markdown, Excel, CSV or JSON.

Desktop full-screen boot splash. It covers DSH's loading card before first paint, plays a 4.5 s five-stage compass sequence, then hands off to the app; clicking anywhere skips ahead, and reopening the window from the tray replays it. The first cold start may hitch once, then runs smoothly.

System-level desktop floating-window plugin for DeepSeek Harness (DSH): a global hotkey opens a floating window; the plugin supports workspace and session switching, approval and follow-up handling, and export of its own diagnostic logs for plugin troubleshooting.

An announcement file shared by several agent sessions on one machine. It is a plain text file the agents only append to; before each step a session receives the entries it has not seen yet, a new session catches up on the ones still in effect, and a session whose context was compacted gets them re-sent.

Sounds and notifications when a task completes or waits for your approval or answer; notification sounds are configurable in the settings page (synth styles or your own audio file), with built-in cat hachimi sounds, just for fun. An optional Windows patch adds a taskbar digit, flashing and native toasts.

Run one deterministic skill-routing pass on every user instruction, so the skill catalog is consulted before the model answers instead of after it remembers. On the host it scores the live catalog from the instruction's keywords and the catalog metadata, with no LLM or embedding call, and appends a single producer-tagged message to the step: a ranked shortlist by default, the winning SKILL.md body once the score clears a threshold, or an instruction to call a named router skill exactly once. Unrelated instructions stay silent.

A Herta theme for DeepSeek Harness: an ASCII boot opening ported from the upstream Herta project, a violet palette of design tokens, swappable wallpapers (four bundled, plus image URL, local import, solid colour or off) and a "Herta Appearance" page in DSH settings. Doubles as the theme that dsh-herta ships with.

Project-level memory built around a ledger whose structure the AI designs per project; the plugin only handles detection, injection, write-back, archiving and git. Memories are split by meaning: the active ledger holds current state, the turn journal is append-only, and decisions, the goal tree and lessons are one-card-per-file (with a multi-dimension index over the card folders); the fundamental principles file can be changed only by the user. There is a single compaction mode: before compacting, the AI sorts the work into three buckets - what must stay in context and is re-injected verbatim, what is written to the project's _stash/ for later lookup, and what is dropped. When a conversation is opened in a container workspace holding several projects, it can be locked to one sub-project (the lock lives in the plugin's own home and survives restarts; it leaves no marker in your project). Also an activity log with five levels, where L4 stores the user's own words verbatim (never injected, queryable on demand). Before each compaction a verbatim capsule (user quotes, raw errors, changed files) is written to disk, and both the capsule and its source material survive a restart; a handoff document lets a new conversation resume the previous one. 21 tools, 12 subcommands, 39 settings (one of them switches whether the settings panel or each project's manifest wins). Runs locally, no runtime dependencies.

Delete one finished turn from a DSH conversation without deleting the session — the trash button next to the last assistant reply removes the question, that reply and the tool records of the turn from the conversation surface and from the model context, while the session id, title, later turns and the append-only log stay; the removal is written as a persistent tombstone, so it survives a reload.

Control pnpm build-script approval across DSH profiles from Add Plugin, and repair same-Git-URL updates on DSH 0.2.0-rc.2.

Persists unsent composer attachments per session, so images and files pasted into the input box survive page switches, conversation switches and app restarts: the bytes are mirrored in IndexedDB and handed back through the official createDrafts/addAttachments channel, and are dropped as soon as the message is sent or you remove them.

Unofficial Zhipu Qingyan (chatglm.cn) web provider — run DeepSeek Harness on web-app credits with a login token, streaming and prompt-bridged tool calls included.

Bidi-safe RTL rendering for mixed Arabic and English text, a full Arabic interface pack (3,307 strings) registered through the DSH locale service, and three thmanyah cuts — Sans for the interface, Serif Text for reading, Serif Display for headings — placed behind whatever font the app itself is set to.

One-tap connect Agnes language, image and video models into DSH (off by default), with image recognition and thinking level filled in automatically. After signing in to the Agnes console (re-login uses AGNES_PASSWORD), you can see the quota windows (5-hour requests, weekly requests, daily images, video), account-wide cumulative usage, and a usage pill beside the input box. AgnesCode desktop models can be connected too (Windows only), re-reading the desktop app login when the token expires, with rate-limit hits backing off on their own.

Read-only Skill inspector for DeepSeek Harness. It shows which Skills a conversation actually loaded — from observed loading evidence only — and which Skills the current environment can discover, and gives each Skill a detail page whose left column switches between the framework, the evidence, validation, evaluation, step evidence, run logic, the before/after of a modification when there is one, the definition facts and the raw SKILL.md. Declaration and run stay apart: the body is split into structure, declared workflow and progressive disclosure, the run logic is read from local receipts, and every declared step carries its own evidence — no observed evidence is reported as exactly that, not as executed or not executed, and the run's instruction hash sits beside the current definition as match, mismatch or unavailable. The evidence view gives each subject one of three states (declared, observed, unavailable), walks definition → load → use → outcome, names what each stage cannot reach, declines to attribute an outcome to the Skill, and can copy the snapshot out as byte-stable JSON. Validation checks the Agent Skills open standard and the platform profiles separately, each finding on a rule id and labelled standard compliance or platform compatibility. Evaluation keeps repeatable cases with the conditions each run was recorded under and the four stages of runtime evidence, comparing two runs of one case item by item without scoring them. The trigger boundary keeps should-trigger and should-not-trigger prompts, has you run them in a real new session, then records what was observed — whether a load record appeared and whether it was model-invoked or user-explicit; a typed /name is not a natural trigger, and an absent load record is reported as not observed, not as not triggered. The same projections are exposed to the agent as a read-only skill_review tool, which returns a structured review packet and says what it omitted when the packet will not fit whole. The inspector, the evidence chain and these reviews are computed on this machine from the definition and the session receipts; the Chinese reading version sends the body through the host's model when you ask for it, keeping code fences, paths and frontmatter verbatim and storing the result on this machine. A Skill can also be cloned into a local copy. It does not write back to the source Skill and does not run its scripts.

Windows toast and a chime whenever a conversation task stops: completed, waiting on your approval or answer, or failed. Clicking the toast reopens that session at your last question, and in DSH Desktop it also raises the window.

Replaces the reasoning-effort dropdown with a draggable snapping slider, and draws 13 canvas effects across five style families (thrust, fluid, fantasy, tech, texture) with three tiers that unlock as the effort level rises. Custom mode combines 10 particle forms with 8 flow trajectories; 9 accent presets and a hex picker recolor every effect, and optional key sounds cover two voice clips, a synthesized duck squeak, a mechanical click and a local audio file.

Element annotation and review panel for the DeepSeek Harness web client: pick UI elements in a live app preview, write notes, and send them to the conversation as one structured block

Stages exact DSH tool calls without dispatch, presents complete canonical arguments in a durable Batch Review Inbox, invalidates approvals after edits or restarts, and commits only a matching SHA-256 digest plus single-use nonce through the normal DSH tool pipeline.

Builds, tests, archives and runs an Xcode project or workspace on iOS or macOS through xcodebuild — a live filterable build log, scheme and destination pickers read from the project, and log streaming for simulators and USB devices, including iOS 16 and earlier over libimobiledevice, whose app logs stay readable while the device is locked. macOS only.