Plugins

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. From 0.4.8 adopting a plain .gguf no longer registers an unrelated Ollama model. The port field used to follow the last click rather than the file: selecting an Ollama entry moved it to 11434, selecting a plain .gguf did not move it back, so a 11434 left over from an earlier click stayed. The server accepted it as given, probed the port, found Ollama answering and borrowed it (the skipped-launch log line about the port already answering is this path), and then the model id - derived from the file name - could not be matched against Ollama's /v1/models, where the fallback was to grab the first entry in the list. Embedding models tend to sort first, so the adopt registered a model the user never selected. Now the port follows the file: a plain .gguf landing on another engine's port (ollama 11434, vLLM 8000, LM Studio 1234) is moved to 8090 and the panel says so. Only ports belonging to a known engine are overridden - a hand-typed 9999 aimed at your own llama-server is left alone. The model-id fallback no longer guesses either: it takes the only entry when there is exactly one (a single-model backend may legitimately report a different id than the file name) and otherwise reports an error listing the ids it did see, rather than registering a wrong provider.

Consulta fechas límite de artículos de conferencias CCF con las herramientas ccf_deadlines, ccf_venue y ccf_update, el comando /ccf y un panel de escritorio que exporta la lista como calendario. Las fechas provienen de sincronización en vivo (CCFDDL es la fuente principal y se contrasta con otra; una diferencia de 36 horas o más se marca como conflicto de fuentes). No se incluyen datos y los próximos 30 días se mantienen en el system prompt.

Manual de criterios sobre dinámica estructural de pavimentos: 59 fichas en 8 capítulos que advierten qué títulos exageran el contenido, qué fórmulas fallan al copiarlas y qué cifras no deben extrapolarse ni citarse fuera de su contexto.

Muestra junto al composer DSH la cuota restante del canal Our Free Model (OFM) actual y lista en un panel todos los canales con sesión iniciada. Marca los agotados y cambia el canal mostrado al cambiar de modelo. Requiere dsh-our-free-model con al menos un canal conectado.

Recibos de entrega por sesión para artefactos locales: compara los hashes actuales con una base registrada, separa las afirmaciones del agente de la aceptación explícita del usuario, y señala archivos modificados o no disponibles. Exporta un recibo Markdown mediante herramienta, comando slash o panel de conversación.

Herramientas adaptativas a la versión de DeepSeek Harness. Lee la versión activa desde el gestor de plugins y replica las reglas de compatibilidad de app-boot: primero `engines.dsh`, luego peerDependencies `@deepseek-ai/dsh*`, compara incluyendo versiones prerelease y respeta excepciones de versión exacta. Evalúa todos los plugins instalados y marca estáticamente fuentes que aún usan APIs renombradas o obsoletas entre v0.1.6 y v0.2.1. Incluye panel de conversación, herramienta de agente y rutas HTTP state/scan/report. Solo lectura y sin conexión.

Audita el gasto del presupuesto de contexto de cada turno: desglosa AGENTS.md, catálogo de skills, esquemas de herramientas y contexto de ejecución; marca como desperdicio la reinyección de contenido sin cambios; clasifica el volumen de descripciones que el modelo lee cada turno con más de 60 skills instaladas y la inflación de esquemas por plugin. Determinista, sin LLM. Cuatro tools, incluido un barrido entre sesiones.

Contrasta cada afirmación terminada con la salida de herramientas leída en ese turno. Solo si en esa misma salida aparece la cita que demuestra una contradicción, guía una corrección una vez en el mismo turno. Marca afirmaciones sin respaldo en el registro de sesión y stderr. Primero analiza gratis términos absolutos y números no coincidentes; sin herramientas en ese turno, no llega al modelo.

Auditoría de credenciales pre-publicación: recorre directorios y detecta API keys, tokens, claves privadas, cadenas de conexión DB y contraseñas hardcodeadas antes de commit/publicar/entregar. Sin dependencias, totalmente offline; los hallazgos muestran solo los primeros seis caracteres.

Revisión de textos destinados a personas. Las herramientas write y edit requieren la marca human_facing; el plugin revisa en segundo plano el texto según reglas PROSE.md por capas, prepara anotaciones, las compara con el archivo completo y las incorpora mediante /humanize, /humanize-loop o post-compaction mientras la sesión principal sigue trabajando. /humanize-review procesa bajo demanda archivos seguidos por git.

Registra una herramienta `valuation_service` que lee un árbol workpaper con JSON 01/02/04 y devuelve un schema unificado: seis grupos de ratios, ratios previstos, campos de entrada DCF con marcadores explícitos de ausencia, PS/PE comparables e IRR. Es determinista y local: si faltan WACC, crecimiento, capex, D&A o capital circulante, los deja vacíos en vez de inventarlos y señala diferencias entre supuestos para revisión.

Registra una herramienta `context_extract` en DeepSeek Harness. Comprime documentos largos (informes financieros, contratos, libros contables) en Markdown estructurado denso y JSON con métricas clave, tablas, cláusulas numeradas, entidades y alertas de comprobación cruzada, para que el flujo posterior no relea el original. Detecta y trata de forma distinta estados financieros, contratos legales y libros empresariales; concilia balance y efectivo, y señala discrepancias como alertas en vez de corregirlas silenciosamente. Solo lectura, nunca modifica el archivo fuente. Requiere Python con openpyxl y python-docx.

Estudio de skins de usuario de War Thunder: selector de cuadriláteros UV por cuatro puntos, capas gráficas recortadas al cuadrilátero y exportación PNG/DDS/.blk.

Añade al compositor un botón ✨ y el comando /enhance para convertir el borrador en un prompt estructurado, señalar preguntas abiertas bajo la entrada, usar el modelo de la sesión actual y permitir deshacer.

Buscaminas en una ventana flotante arrastrable, con primera casilla siempre segura, banderas, apertura en acorde y temporizador.

Memo personal en tablas por semana ISO, navegación semana/mes/trimestre/año, archivo trimestral, análisis IA y exportación Markdown. Editar semanas pasadas exige force flag; analiza fallos telemetry de toda la suite en informe de issue GitHub.

Señala pares de códigos ICD que requieren revisión (combinaciones dagger/asterisk y combination codes) según GB/T 14396-2016 y notas públicas. Informa los pares sin resolverlos; decide el coder.

Recibos por sesión para artefactos locales: compara hashes actuales con baseline, separa afirmaciones del agente de la aceptación explícita del usuario, señala archivos cambiados/no disponibles y exporta un recibo Markdown con herramienta, slash command o panel.

Extrae del texto el número y la fecha de factura, las partes, los identificadores fiscales y el importe, impuesto y total. Verifica que importe más impuesto coincidan con el total y señala los documentos que no cuadran.

Verificador de compatibilidad de plugins dsh: compara peerDependencies de los plugins instalados con la versión dsh activa para detectar cuáles rechazará el preflight de inicio y muestra comandos de reparación copiables. Un banner superior en dsh web permite aislarlos, desinstalarlos o actualizarlos; crea una copia de seguridad antes del cambio y revierte si falla la comprobación posterior.

Insignia del saldo de DeepSeek API en el pie de la barra lateral, junto a Settings: lee la clave que DSH ya almacena, muestra al pasar el cursor el desglose entre saldo concedido y recargado, avisa si baja de ¥2 y se actualiza cada 60 segundos o al hacer clic.

Fija sesiones en una única sección Pinned por encima de todos los grupos del sidebar Web sin cambiar el orden guardado; el pin es una marca en el documento de ajustes del host. El menú contextual ofrece renombrar, fijar, bifurcar, archivar, eliminar y copiar título o ID de sesión, con etiquetas según el idioma de la GUI.

Integración Git para DSH inspirada en el VCS de IDEA: el icono Git junto al modo de permisos muestra rama y cambios pendientes; al pasar el cursor abre el selector de rama y al hacer clic un panel VCS tipo IDEA (árbol, grafo de commits, cambios y preparación, detalles y diff de archivo). Incluye ajustes de identidad de commit, binario Git y flags de red. No registra herramientas de modelo en DSH.

QQ Mail y cualquier buzón IMAP/SMTP en DeepSeek Harness: las herramientas Agent `qqmail_*` permiten listar, buscar, leer, enviar, responder, marcar y descargar correo por IMAP/SMTP con un código de autorización QQ Mail (sin OAuth ni redirección 2FA). Incluye la CLI `qqmail`.