Plugins

DSH 模型 Pro:为 DSH WebUI 提供将 pi-ai 预设或任意已配置提供商的模型上下文、输出上限、推理档位与兼容开关一键应用到目标提供商,并集中查看、新建、编辑、复制与删除各提供商模型条目的能力。

HUD status panel: Git status, MCP servers, skills, model and token usage in a floating side panel.

Extensible animated response pet with blue-whale, orange-cat, and silver-shaded-cat themes, per-session progress, model and tool-call traces, token counts, output speed, elapsed time, and collapsible multi-session status cards.

Views and builds CAD models (STL, OBJ, STEP, IGES, DXF, SVG) via cad_view/cad_info and OCCT parametric modeling tools, with an interactive viewer card and a resident CAD panel in the Web UI.

Compliantly use your own Antigravity quota in dsh: bridges your local logged-in official Antigravity CLI (agy) session as the chat and subagent model provider.

Seven agent-preset modes for DeepSeek Harness, each with a fixed tool boundary, per-mode model routing and fast/deep omd_task delegation tiers, plus a plan-to-execute loop (/mode, /start-work, goal-backed /ulw) and a settings page that remaps models per mode and per tier.

Forces context compression when running low-context llama.cpp models such as Qwen3.8-27B, improving model efficiency and usage smoothness, together with well-refined compaction prompts.

DeepSeek usage dashboard for DSH Web: days-left estimate from recent average spend, daily/monthly/cumulative costs with period-over-period comparison, per-model cost ranking, cache savings, peak/off-peak pricing impact, and a draggable floating balance widget.

FormatForge: forge 30+ file formats (PDF, DOCX, PPTX, XLSX, EML, EPUB, TOML, archives) into AI-readable text via a drag-and-drop inbox, ff_translate/ff_formats/ff_result tools and a CLI; scanned PDFs fall back to local OCR or an enhance hint for the session model.

AI-for-Science research workflow skill for DeepSeek Harness, aimed at researchers and grad students using agentic AI: multi-angle literature review with per-angle files, experiment design with a caveat list, figures and paper reading; each job returns files plus a cross-verification brief for a second model. Zero deps, MIT.

Side-panel Xiangqi board for DSH: play as red while the current DSH model chooses black moves, with Host-side rule and revision checks.

Continual self-refinement loop: persistent memory, periodic review-and-refine, cross-session shared knowledge, and automatic rollback through a model-callable harness_refine tool.

A vision-language gateway provider route: pasted images are described by a configurable VL model (Qwen-VL by default) before the DeepSeek wire.

DeepSeek Harness plugin that shows API balances and available models in Settings

On-demand second opinions through ask_advisor, with editable requests, per-call human approval, and text advice from a model configured in DSH.

Task reliability: auto-retry on model timeouts/failures, auto-continue unfinished tasks, completion verification, thinking-repeat interruption, resume after sleep or restart, autopilot mode and a remote trigger endpoint (npm: `dsh-task-reliability`).

Visual round-table meetings for DeepSeek Harness — expert nodes on a ring with directed edges, an aggregation gateway, proxy thinking for black-box worker models, and adversarial review (red-team) of settled plans with per-viewpoint endorse/reject.

For the DeepSeek Harness native vision model deepseek-v4-flash-vision-exp, raises image admission limits to 32 MiB / 8192 px / 600 images and adds a highres_read tool that tiles large images, then returns the whole image plus 800x800 tiles through the host read_image tool.

Smart subagent routing and evolution: bind roles to any provider/model registered in DSH, including models supplied by Codex and Claude Code provider plugins, with workspace learning.

A gated math-modeling skill suite for DeepSeek Harness: a five-stage pipeline with stage gates, award-paper writing patterns, sensitivity analysis, statistical diagnosis, and a contest preset.

Connect a ChatGPT account to Codex models in DeepSeek Harness through browser PKCE or device-code OAuth, with automatic token refresh and Web or headless login.

Automatically and intelligently routes each delegated task to the best configured model using aliases, capability tags, and usage guidance—keeping routine work fast and affordable while sending complex work to stronger specialists. Configure your model team in the Web UI or guided setup, run specialists in parallel with reliable result joining, and see the chosen model at a glance in subagent views and inline in Better Sidebar's Tasks tree.

Analyze usage across models, providers, workspaces, and time ranges — including custom start/end dates over the full retained history — with live cache-hit visibility and separate model and provider breakdowns. Includes hourly single-day trends, daily cross-day trends, a 53-week GitHub-style heatmap, usage streaks, model and workspace Token-share donut charts with Token, cost, and percentage details, independent combinable filters, cache-efficiency trends, DeepSeek account balance, workspace aliases, CSV export, and a durable ledger that preserves successfully flushed usage after session deletion — and after a workspace is deleted, where its recorded usage is summed into one Deleted row — and rebuilds incrementally — on restart, unchanged sessions are applied from the ledger without re-reading their logs (a per-session log revision is the change signal), and only changed/new events are folded. The workspace registry is kept fresh automatically through the DSH domain/changed probe: only added/removed workspaces are reprocessed incrementally, unchanged workspaces reuse their computed aggregates and ledger with zero rescan, and usage is strictly limited to DSH-registered workspaces (unregistered cwds are ignored). After a scan, lightweight revision status fetches full history only when the Host/data state changes and exposes non-sensitive sync health (revision reuse, rereads, ledger recovery, and failures). Token accounting is explicit: input is fresh (cache read/write and reasoning are separate buckets) and zero-usage replays never overwrite recorded usage. Chinese uses local time, English uses UTC; API access is loopback-only.