Plugins

Registra un proveedor de fetch `wsl-proxy ctx.web` para que `web_fetch` oficial use undici `ProxyAgent` mediante `HTTP(S)_PROXY`, en vez de conectar desde WSL a una IP pública fijada por DNS (la ruta oficial que produce `TypeError: fetch failed` tras un proxy de Windows).

Lienzo infinito para sesiones, con regiones vinculadas a workspaces y presets de agente, tarjetas de sesión fijadas, notas adhesivas y guías de alineación.

Herramientas de programación al estilo Codex y presets de agente para modelos GPT en DeepSeek Harness: exec_command, write_stdin, apply_patch y view_image; rutas OpenAI Chat Completions y Responses API integradas con plan, ask, approval y compaction nativos de DSH. Los comandos largos pasan a jobs en segundo plano de DSH, con salida head/tail limitada consultable mediante job_output. Cada tipo de subagente puede fijar su preset, proveedor, modelo y reasoning effort.

Curación de contexto para DeepSeek Harness: reduce salidas de herramientas según su contenido, limpia registros obsoletos, fija requisitos del usuario y trabajo en curso, y archiva antes de compactar con rastro de auditoría.

Fija y desfija sesiones en la parte superior de la barra lateral web de DSH, con panel de fijadas, sección de archivo e integración con el menú nativo de tres puntos.

Añade un selector Workspaces/Recent-sessions sobre la lista lateral, con grupo de sesiones fijadas, menú por fila (pin/unpin, copiar session id), filtro de título y almacenamiento de pines local al navegador; migra una sola vez desde dsh-plugin-pin-session.

Biblioteca de referencia de Clean Architecture para DSH: indexa ocho repositorios fijados de Go, Java, C# y Python en una base local FTS5, con reglas de aserción, registro de revisiones y panel de observación de solo lectura.

Vista dividida multis sesión para la GUI web de DeepSeek Harness: la ventana superior sustituye el slot raíz por un contenedor redimensionable; cada panel es un iframe con un cliente DSH completo fijado a su sesión. Incluye barras de título por panel e hibernación LRU.

Gestor de menús de terceros para DSH Web UI: reúne las nuevas entradas añadidas por otros plugins en un lugar, con opciones para plegar y fijar elementos.

Divide la barra lateral de DeepSeek Harness en paneles de Fijados y Recientes con desplazamiento independiente, y oculta las sesiones fijadas de Recientes.

Navegador de plugins de DSH consciente de la tarea: crea un índice local a partir de temas de GitHub y listas de la comunidad, recupera información entre 28 clases de capacidades bilingües y ofrece auditoría estática de riesgos a nivel de commit, instalación fijada con consentimiento, comprobaciones de composición y recuperación.

Plugin de DSH: puerta con contraseña para el acceso remoto (no loopback) a /api (sin contraseña por defecto), desbloquea los endpoints privilegiados fijados a loopback, restaura todo al desinstalar y ofrece al usuario local de 127.0.0.1 el selector de carpetas nativo del sistema para seleccionar el espacio de trabajo. selec

Adds a cross-workspace activity sidebar for running, waiting and unread sessions, with search, optional pinned and scheduled-delivery groups, local read tracking and confirmed archiving.

A sticky-notes wall pinned to the sidebar: one click opens a full-page wall, notes save as you type, and writing #tags in the text turns them into one-click filter chips. Six paper colors, pinning, search, JSON export and import.

Domain-routed proxy for DSH fetch traffic: hosts on a force-proxy list always go through the proxy, every other host tries direct first and fails over to the proxy within a second, the first channel to answer is pinned for the session, connections are reused for 120 seconds, and each attempt is logged. Can also route pnpm/git subprocesses used by plugin installs.

Archives sessions that have gone unused for longer than a set number of days (5 by default), skipping archived, pinned, and protected ones — a session context-menu item marks a single session as protected, a small shield shows it on the row, and a General settings field changes the threshold.

A right-sidebar tab listing the files DSH Sessions produced, newest first, with the Session in view expanded on top: a click copies a row's full path, a double click previews it in the sidebar, and a file can be pinned in one Session or in all of them.

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.

Adds a Speed setting (Standard, Fast, Ultrafast) for OpenAI and Codex models on a patched dsh build: the pinned dsh 0.2.1-alpha.2 web client needs the Model / Effort child-slot patch, the host pi-ai must be 0.87.1 or 1.1.0 (other versions refuse to load), and the terminal controls need a patched dsh-TUI. Then it appears in the Model / Effort menu; in dst /speed opens a picker or sets the speed directly, and the current speed shows in the bottom bar. The choice is kept per session, provider and model and is sent as the request service tier.

Replaces the DSH sidebar list with a workspace rail: pinned chats and folders, projects that are folders with their own AGENTS.md and files, a virtualized Recents list ordered by the last finished turn, a project_search tool, unread markers, a collapsed icon column with an unread count and a live browser tab title.

Base de conocimiento compartida para equipos: un miembro ejecuta un Hub sin dependencias y los demás instalan el plugin y se unen con un código de invitación. Las cargas se clasifican en diez categorías con resumen, confianza y puntos; 15 herramientas de agente permiten subir, buscar, recordar, comentar y leer. Los elementos importantes se fijan con una recomendación escrita a mano hasta que el miembro los lee. Cada cuenta también tiene biblioteca local privada, colecciones, resumen semanal y diferencias entre versiones.

Memoria del agente con curva de olvido: las entradas ganan peso cuando se mencionan, disminuyen al olvidarse y las fijadas nunca decaen. Incluye seis herramientas y un panel de ajustes.

Memoria a largo plazo entre sesiones para DeepSeek Harness: inyecta reglas fijadas en cada turno y recupera el resto mediante evidencia de palabras clave ponderada por IDF. Deduplica por fact-key, usa almacenamiento JSON sin formato y permite editar o borrar entradas en Ajustes.

Muestra sesiones de Claude Code y Codex en el navegador DSH Workspace para abrirlas, continuarlas, fijarlas, renombrarlas o archivarlas; incluye pestañas terminal, file y git.