Plugins

Entrada de voz offline para la Web UI de DSH: micrófono a FunASR local (SenseVoiceSmall), instalación en un clic y sin nube.

Escanea un QR en Settings para abrir en el teléfono la Web UI de DeepSeek Harness dentro de la sesión activa mediante un quick tunnel de cloudflared.

Conecta una instalación estándar de DeepSeek Harness con DSH Cloud: inicio de sesión por dispositivo más un proveedor gateway con 20 modelos. Servicio comercial alojado por AgentsDance AI; las cuentas nuevas incluyen 500 créditos gratuitos.

Reenvía una interfaz web local de dsh a internet pública bajo un subdominio *.ds.hn (o tu propio relé autoalojado), protegida por un inicio de sesión, con cifrado de extremo a extremo opcional (PBKDF2 a AES-256-GCM).

Gestión de recursos de Azure: consulta y muestra recursos, despliega plantillas Bicep/ARM y revisa el registro de actividad.

Mando remoto móvil para DeepSeek Harness: compañero con cifrado extremo a extremo sobre RPC con lista de permitidos de doble plano (LAN, Tailscale, túnel rápido de Cloudflare opcional o punto de encuentro autoalojado).

Plugin nativo de DeepSeek Harness (dsh): memoria híbrida local y en la nube de XMemo para agentes basados en Cordis.

Convierte una necesidad declarada en una elección de plugin: una skill de agente que consulta un catálogo de todo el topic `dsh-plugin` (más de 2600 repos, recrawleado cada 6 horas, descripciones en chino desde Workers AI) mediante una API de búsqueda rankeada, y devuelve el comando `dsh plugin add` correspondiente.

Visión estructurada local-first para agentes solo de texto: las imágenes van a un VLM local compatible con OpenAI y vuelven como evidencia JSON (resumen, OCR literal, regiones de diseño, entidades/relaciones, colores, incertidumbre explícita), con respaldo anti-alucinación y un puente opcional de pegado/subida; coste en la nube cero, las imágenes nunca salen de la máquina.

Integración con el gestor de incidencias Jira Cloud que expone ocho herramientas de modelo para búsqueda de incidencias, consulta de proyectos y transiciones, creación, actualizaciones, comentarios y transiciones; las escrituras son opcionales y admiten dry-run.

Plugin de DeepSeek Harness: herramientas de navegación web (markdown / captura de pantalla / pdf) impulsadas por Cloudflare Browser Run, un Chrome headless real con renderizado de JS, sesiones de inicio de sesión y soporte de WebMCP.

DSH 插件：把电脑上的 DeepSeek Harness 变成手机可访问的服务——起桥接、自动开隧道（ngrok / cloudflared）、签发 30 天 Cookie，并输出地址与凭据，配合 DSH Remote Android App 使用。

Serves this harness instance's Web GUI on a fixed port with device pairing and Cloudflare Access verification.

Cross-session long-term memory backed by Supermemory: pre-step semantic and chunk recall with per-session deduplication, debounced transcript upload, background LLM fact extraction (add/update/delete) with bigram and semantic deduplication, memory_search / memory_save / memory_forget tools, global or per-workspace container scoping, and a Plugins page UI for API key storage, memory CRUD, recall preview and activity log. Requires a Supermemory API key (a free monthly quota is available), and conversation excerpts are uploaded to Supermemory's cloud.

Whale-themed desktop pet for DeepSeek Harness with a local todo list: click the pet to add or edit tasks inline, get reminders that get more frequent as a due date approaches, and optionally append one short AI-written next step to a reminder. Based on PC2005-cloud/dsh-pet (MIT); artwork keeps the upstream non-commercial terms. Install it instead of PC2005-cloud/dsh-pet, not alongside it.

A model-driven desktop pet, derived from PC2005-cloud/dsh-pet (MIT): an always-on-top companion that follows your agent sessions, races the Web UI to answer approvals and questions, keeps a per-pet affection ledger, speaks through pet_say/pet_do tools or an autonomous inner voice, and can read those lines aloud through BreezeBlue TTS with your own BreezeBlue API key. It uses its own /whale598 route and ~/.dsh/whale598 data directory, so it is a separate pet from dsh-pet rather than a replacement for it.

A talking whale desktop pet for the DSH web UI — persona-driven chat with persistent memory, plus scheduled autonomous web browsing that brings back a summarized message telling you what she found. Based on PC2005-cloud/dsh-pet (its art and animations are reused under the upstream non-commercial terms); install it instead of dsh-pet, not alongside it.

One-step local model onboarding plus a light harness for DSH. On load it probes loopback and private-network inference endpoints (llama.cpp, Ollama, vLLM, LM Studio), reads each model's context window and output budget from the backend, and registers the provider through settings, so a local model becomes selectable without hand-writing config. It then trims only that provider: masks the tool list through tools.restrict, drops named system-prompt sections and memory contexts, swaps the injected instruction block for a short one while preserving its first 80 and last 96 characters, and truncates tool-schema descriptions without touching structure or required fields. Cloud providers are returned as the same assembly object, untouched. The injected block is re-read from disk on every assembly, so editing the file takes effect on the next request with no reload. The panel in the plugin detail page is a live operations view: it shows whether the takeover is active and by which hook, how many characters the last assembly was trimmed from and to, and which providers are online (probed concurrently, with a real one-shot completion reporting latency and reply text per provider). If a local backend is not running, the panel offers a one-click launch: it probes the port first (never starting a duplicate), then starts the matching server (ollama serve, or a custom command you supply) detached and polls until the endpoint answers, clamping the wait to 3-600 seconds, reaping the child it just spawned if it never answers, and naming the paths it searched when it cannot find the binary. A stop button kills backends this plugin started (SIGTERM, then SIGKILL) and refuses to touch a server somebody else launched. When a model has just been downloaded and no backend exists yet, the panel can also scan the machine for .gguf files with hard caps (300 models, 20000 directory entries, Downloads only one level deep), start llama-server on the chosen one (skipping the .mmproj. vision projection and reusing a server that already answers), register the provider under the model id the backend reports, and run one real completion to prove it works. Before spawning anything it does a fit check that counts only the part of the file the GPU cannot hold: hostBytes = fileSize - VRAM, compared against max(total RAM - 2GB, free RAM). A file far larger than RAM is refused outright; a heavy host-side remainder is refused unless you confirm; the same remainder under mmap is only a warning, because the weights land in reclaimable page cache. A load-mode selector (auto / mmap / none / mlock / mmap+mlock) maps to llama.cpp --load-mode, and the default is whatever the engine picks. From 0.4.2 everything above is derived from the platform instead of assumed: home and data directories come from os.homedir() and the OS conventions (LOCALAPPDATA on Windows, ~/Library/Application Support on macOS, XDG_DATA_HOME elsewhere); backend binaries are looked up in the official install locations of each OS and can be pinned with OLLAMA_BIN, LLAMA_SERVER_BIN, VLLM_BIN or LMSTUDIO_BIN; model discovery walks DSH_LOCAL_MODELS_DIR, ~/models, ~/llama.cpp/models, ~/.cache/llama.cpp and the LM Studio data directory, with the WorkBuddy layout demoted to one candidate among them. VRAM is asked from nvidia-smi, then rocm-smi, then sysctl hw.memsize on Apple Silicon (flagged unified, so the same bytes are not charged twice), and any flip to a unified-memory budget is handled explicitly. Before building a command line the plugin runs llama-server --help once and keeps only the flags that build actually has, so older engines get --n-gpu-layers 99 and no KV quantization while newer ones get auto plus q8_0; anything it cannot probe is passed through rather than dropped. LM Studio is started with lms server start --port N rather than being lumped in with the engines that need a model argument, and vLLM gets its own --model example instead of llama.cpp syntax. Windows, macOS and Linux are all exercised in CI, including a windows-latest and a macos-latest job, because a cross-platform claim that has only ever run on one OS is not a claim. Two of those suites exist specifically to catch this machine's blind spots: one replays the whole suite with process.platform rewritten to each OS, and one deletes every advantage this development box has — no nvidia-smi, no rocm-smi, no sysctl, an empty PATH, a home directory with no models — and requires graceful degradation rather than a crash. That last one caught a real class of bug: several assertions hardcoded 'lms.exe', which cannot pass where the executable has no suffix. Measured on the reference machine: a 16.9GB 27.2B MoE with 14.5GB of VRAM holds a 285-691MB working set and sustains 76 tok/s after a 1000-2000 token warm-up, so file size alone is not a fit signal. From 0.4.3 the scanned models are shown as a table rather than a dropdown (one row per gguf, with its size and any fit warning), so you can see what is on disk before choosing. From 0.4.4 the delete button on the provider table removes the provider entry, not the weights file on disk. That distinction matters: what people actually want to clear out is the dead entry - one pointing at a port that never answered, one registered twice for the same port, one whose model file was deleted long ago - not a 15.8GB gguf. Deleting a config is undone by importing again; deleting a gguf is not. The removal goes through settings.mutate with an unset op on \['providers', id], because update merges layers: writing null there leaves a null provider key that the loader reads as a broken config rather than an absent entry, and only mutate/unset is implemented as Reflect.deleteProperty. The result is read back afterwards, because unset on a key that comes from a lower layer (the profile cordis.patch.yml or a home patch) does not delete it - it restores the inherited value, so the call succeeds while the entry stays. When the entry is still there the panel says so and points at the file to edit instead of reporting success, and a DSH whose settings service predates mutate() is told plainly rather than silently ignored. Deleting takes two clicks and clearing the confirmation is as easy as clicking another row; on success the online and baseURL caches are cleared and the tool surface is recomputed, since the provider list changed. The gguf itself is never touched by this plugin - the scanned-model table is display and selection only.VRAM reporting is driver-dependent. From 0.4.5 model discovery also reads Ollama's own storage: its blobs are named sha256-… with no extension, so extension matching could never list them. Weights are now accepted by extension OR by GGUF magic header (only dot-free files of at least 8MB, capped at 600 header reads per scan; obviously-not-weights extensions like .json/.md/.safetensors/.part are never opened). Ollama models are resolved from their manifests: the model layer is picked by mediaType application/vnd.ollama.image.model (never adapter/mmproj layers), the human-readable name is restored from the manifest path (…/qwen3/8b becomes qwen3:8b, …/latest stays bare), tags sharing one blob merge into a single row, and the metadata kv supplies name, quantization and context length. Adopting an Ollama model does not start a duplicate llama-server over the same weights: it confirms ollama serve answers on 11434 (launching it if not) and registers the provider under the tag the endpoint itself reports — the blob's sha256 name would never match anything, since 11434 lists qwen3:14b, not sha256-a8cc…. Two real-machine-only traps fixed on the way: existsSync('<file>/') returns true on Windows, so manifest-vs-directory branching now goes by parseable content instead of trailing-slash semantics; and the quantization label table had been copied from a stale ggml enum generation, labelling ggml_type 15 as Q5_K_S while ollama show reports Q4_K_M. Same-blob dedup is by (dev,ino), keeping whichever entry has a name. Verified on the reference machine: the scan goes from 1 row to 4 — qwen3:14b (8.64G) and qwen3:8b (4.87G) now appear with Q4_K_M badges — in 4ms; npm run verify exits 0 (launcher 57, portable 35, panel-host green including the linux/darwin simulated-platform runs); the new panel-host suite builds a real miniature Ollama store in tmpdir and points OLLAMA_MODELS at it, so results no longer depend on whether the test machine happens to have Ollama installed. From 0.4.6 the panel shows each model its real name instead of its file name, because some download tooling stores every weight under one generic file name, leaving the real name only inside the GGUF header. The scan now reads each file GGUF KV section - not a fixed-size head buffer, because tokenizer.ggml.tokens (hundreds of thousands of strings on a large MoE model) and the chat template push general.file_type past any window; and GGUF string arrays are interleaved (u64 length plus payload per element), so treating them as a length table walks the cursor off the rails and produces astronomically large reads. Reading and skipping are accounted separately: key names and types are always read, string values over 256KB and string arrays are only walked past without copying, non-string arrays over 64 elements are skipped outright, and a cursor past the file size stops the read, because an out-of-range position means the cursor is wrong rather than that a limit should be raised. The displayed name goes Ollama tag, then general.basename plus size_label, then general.name, then the file name, with the full string kept for the hover text since it usually carries internal build numbers; mmproj files are now identified by general.type as well as by name. A short read (truncated file) no longer feeds half a value into the parser. On the reference machine, reading one file header parses 44 keys in about 16ms, and a whole scan takes about 31ms. From 0.4.7 the smoke test no longer fails the first time and passes the second. The old code gave both request attempts one shared AbortController and one shared 30-second timer, so the total budget covered both rounds: a cold load of a large model burned it in round one, the abort left the signal permanently aborted, and the second round (the Ollama-style reasoning_effort: none retry) could not even be sent - which is exactly why the first click returned This operation was aborted and the second succeeded. Now each attempt gets its own controller and timer, a timeout retries once automatically (retryOnTimeout, on by default) because a first-request timeout almost always means the weights are still being read from disk, and non-timeout errors (refused connection, HTTP error) are not retried at all. Timeouts report themselves: the message says the wait exceeded smokeTimeout, whether a retry already happened, and points at the new smokeTimeout setting (seconds, default 60, clamped to 5-600) - port responding is not the same as tokens coming out, so it is a separate knob from launchTimeout. Unrelated: an example naming one specific model was dropped from the panel copy, since not every machine has that model downloaded, and the explanation now describes the mechanism instead.

Huawei Cloud CodeArts provider for DeepSeek Harness with OAuth2 PKCE/DPoP sign-in, multi-account rotation, model discovery, and credits and usage views. A derivative of elegant01/dsh-codearts (MIT) with added account and image features; do not install it alongside the upstream - both register the same `codearts` provider.

Session cost meter forked from dsh-cost-meter, retargeted at OpenDesign Cloud: per-session and per-day cost, a 39-model OpenDesign price catalog, OpenDesign Cloud quotas as rolling 5-hour and 7-day used/total bars read from the official funding-check endpoint, Coding Plan quotas, provider balances, peak/off-peak notices, bilingual UI. The quota card needs an OpenDesign Cloud account (the plugin has a built-in login), and it provides service:cost-meter like Han-1413141/dsh-cost-meter and the other dsh-subscribe-meter variants, so install only one cost meter: it replaces dsh-cost-meter rather than running alongside it.

Permite acceder desde un teléfono al escritorio DeepSeek Harness mediante una dirección LAN o un Cloudflare quick tunnel, con emparejamiento QR, feed de sesiones en vivo, solicitudes de aprobación y un widget flotante de escritorio. Ambos accesos (LAN y Cloudflare quick tunnel) están activos por defecto; cada teléfono tiene su propio token.

Muestra en la barra lateral de DSH el saldo de cupones de efectivo de Alibaba Cloud, el crédito que paga las llamadas a Qwen y DashScope. El formulario de AccessKey mantiene la clave en tu equipo.

El comando `/ocusage` muestra el uso y el saldo de Ollama Cloud.

Bridge MCP de WeChat DevTools (wechatide) para DeepSeek Harness: controla el servidor MCP interno del IDE (`wechatide mcp` por stdio) para abrir/importar proyectos, compilar, previsualizar/subir, capturar el simulador, ver logs de consola/red, automatizar páginas y usar CloudBase.