Plugins

Shows DeepSeek balance and estimated daily spend, merges external vision-call JSONL records, and adds a per-session dashboard with model statistics, TTFT, cache rate, call logs, cost estimates, filters, and CSV export.

The model switch for DSH: point it at any OpenAI-compatible endpoint, with curated free/cheap DeepSeek provider presets and automatic fallback when a free tier rate-limits you.

Import local Codex, Grok, Claude, OpenCode, and CC Switch logins into DSH; pick sources and enable models in Settings.

Windows computer use for DSH built on a native C# engine: text-only DeepSeek models can read UI Automation controls and fill forms in verified batches without screenshots, stale control refs can be re-found by unique identity, CAPTCHAs and verification codes are refused and handed back to you through the DSH approval prompt, and Esc stops the turn at any time. A derived project based on Yu-tao-Li/dsh-computer-use-win; do not install it together with that upstream plugin, since both drive the same desktop.

Select a model and its reasoning effort in one drag with a two-dimensional grid.

Per-model jailbreak armor: a Settings table arms model patterns with spells (25 spells: 20 across 11 model families, plus a model-agnostic armor, an upstream preferences file and three stackable persona layers), previews the exact injected block, and writes it into the armed model's system prompt only, leaving every other model untouched.

dsh-TokenLens (词元透镜) — provider-aware plan capacity plus local token statistics (today/7d/30d/all-time, by harness/provider/model/workspace) for DSH.

Use SSH hosts and WSL distributions as remote DSH workspaces, with environment setup, model configuration porting, and the remote interface embedded locally.

QQ hub for DeepSeek Harness: private and group chats over OneBot v11 (SnowLuma/NapCat), with per-chat scoped memory, per-chat persona routing, a vision helper that narrates images for text-only models, multi-platform link parsing (large videos sent as group files, image galleries sent inline or as merged-forward messages) and a web panel for models, personas and settings. Derived from aureth-labs/dsh-aurabot-qq; do not install both.

Blocks a tool call whose arguments lost their boundary, keeps a closing turn open when a step silently never ran so the model can re-issue it, and replaces damaged output on the model-visible surface after a fork. Zero dependencies.

Multi-model layered pipeline for DeepSeek Harness: a strong model plans, a fast one drafts, a third reviews, and a failed review is reworked automatically — endpoints, protocols, models and gates all configured from the DSH settings panel.

A DeepSeek Harness bundle adapted from whatcccup/cece_legal_skills (MIT) for local DOCX, PDF, TXT and Markdown redaction with human review, PDF masking, local history and optional Agent rule edits. Requires Python 3.9+ and first-run dependency downloads. Model access is needed only for rule generation; 1.3.0 is a development prerelease and Windows acceptance is pending. The upstream is a standalone offline tool/SkillHub skill, not a DSH plugin, so it cannot be installed alongside this bundle.

Manage which models appear in the composer model menu. A settings page lists every provider's models with a per-model switch, provider-level show/hide, collapsible groups and filters by kind; switched-off models leave the menu while the model in use in the current session stays. The list can be searched by name or ID and filtered by capability (multimodal, reasoning) and by minimum context window (128K / 256K / 512K / 1M). Rows show the model kind, and chat models also show reasoning support with the adapter's effort levels, their context window and input modalities. Labels merge manual overrides, the settings document, the adapter, the relay's GET /models response, a built-in pi-ai / models.dev catalog, name inference and vendor corrections in that priority order, with the source named on hover and dashed borders marking inferred values. Any model can be edited by hand (kind, context window, modalities, effort levels); those edits are stored outside the profile and can be written back into the model provider's settings. A hard re-fetch drops every cache and asks the adapter and all relays again. A switch, on by default, lets relay routes whose models declare text-only input accept images in memory, without editing configuration files. The hidden list is stored in the profile patch and mirrored to $DSH_HOME.

将 AStudio 应用中已登录的模型复用到 DeepSeek Harness — bring AStudio app models into DeepSeek Harness with zero configuration.

DogCatTrace attribution parody as a right-sidebar tab: draw three integer challenges, paste each model reply into one box at a time, read the 24-breed dog and cat ranking.

Bilibili live-room danmaku for DSH: read live chat, send messages only after explicit confirm, hang out in a room in the background, optionally let the model decide whether to reply, and optionally transcribe stream audio to text; no video understanding. Setup needs a Python 3 venv with the bundled requirements plus Bilibili cookies; transcription additionally needs ffmpeg and a Whisper engine.

Plain-text memory for DSH where every event is one Markdown section with six fields, recall works by element association rather than vector search, at most two old titles surface per turn, and nothing calls a model or the network. Writing the library stays manual: you can open it, grep it, correct it and carry it to another machine.

Manages a Magpie model list and writes the checked models into a Magpie provider group: full sync from /v1/models, manual entries, per-model context, output limit, image input and reasoning efforts, sorting and drag reordering. On Windows x64/ARM64 it also downloads and upgrades Magpie from yetone/magpie-releases with SHA-256 verification and rollback, auto-upgrades once a release is 30 minutes old and all conversations are idle, starts Magpie in the tray with DSH, and toggles its lite mode and sleep prevention.

Picks each subagent model from the local catalog by task category, unless the caller already named one. Free and quota tiers are ranked together by per-model capability tags; a pricier model is used only when no cheap model is tagged as good at that kind of work. Only providers whose remaining balance is readable, or that are account-backed, enter the candidate pool; the rest stay listed but are not routed. A panel adds per-model manual priority above the automatic order plus allow/revoke controls.

Connect a self-hosted Magpie gateway with one provider route per upstream owner, so the stock model picker shows Magpie (Claude), Magpie (Codex) and other groups, plus automatic multi-line failover, read-only usage cards, per-model default effort, a per-session fast switch for priority-tier models, and optional Cloudflare Access.

Automatically retries model failures (drops, timeouts, rate limits, 5xx) in-turn. Retry count, delays, backoff, fallback and more are configurable. Rate-limit backoff is capped and jittered, server retry hints are honored, and optional retry-until-success keeps going within a time ceiling. Every option is editable from the plugin config or the sidebar panel: retry count, initial and max delay, backoff factor and jitter, rate-limit delay window, retry-until-success ceiling, fallback trigger and switch cap, dead-route blacklist TTL, log file. When a route keeps failing it can switch to a pinned, allowlist-filtered, or catalog-picked alternate model route, with a 30-minute dead-route blacklist (auth failure, subscription expired, billing) and a stale-assembly guard that keeps retried requests on the switched route. Ships a right-sidebar panel with live retry state, route labels and incident counters, Chinese and English locale, and memory-only logging by default.

Save reusable prompt snippets with categories and {{placeholders}}; manage them with /snippet commands (add, list, rm, optimize) and insert into the composer from a draggable dock panel, with one-click AI refinement through the default model.

Slash commands for the DeepSeek Harness Weixin channel: persona toggle & multi-persona library, session history (soft load or real session switch), model selection, token usage and balance, scheduled daily greetings. Host-side execution, zero model tokens