Plugins

Failure memory for DeepSeek Harness. Captures tool failures as they happen, classifies them with 16 built-in rules into lessons pairing a symptom with a fix, adds session/daily/weekly summaries, and injects a short briefing into later sessions through systemPrompt context. Ships 5 pitfall_ tools and a web settings page.

Stores the agent's long-term memory as plain Markdown in an Obsidian vault — every memory is a .md file with YAML frontmatter that Obsidian indexes natively, so you can read, edit, link and back up it with tools you already use. Nothing is locked in a database. Ships a sidebar panel (tag filter, sort by recency or importance, edit in place), 7 agent tools, three-tier token-budgeted recall, BM25 ranking (no embeddings, no API key, works offline, CJK bigram tokenization), importance decay with archiving (moved to .archive/, never deleted), and optional GitHub Contents API sync. A note containing a secret is refused rather than redacted, because a redacted memory reads as complete while the important part is gone. Zero runtime dependencies; everything comes from the DSH host.

Local Ollama worker tools for DeepSeek Harness: model inventory, a single unverified ask, and a batch runner that chunks long text, checks every item deterministically, returns a small verbatim sample for review and can optionally write the labelled result inside the session workspace.

Named model routes for subagent delegations, configured from a settings page: each alias binds a subagent provider, model and optional reasoning effort, token cap, agent preset, tags, plus a "when to use" line the model reads to pick it, so a delegation names only the alias. Adds five tools (delegate, list routes, edit config, wait, release), shows the route a child actually ran on in its session header, lets each UI element be switched off, and keeps all configuration in one local JSON file instead of YAML.

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.

WYSIWYG Markdown notes editor in the DSH right sidebar, with tabbed documents, table editing, chat integration, assistant read/write tools, and local file export.

WeChat Reading (WeRead) right-sidebar bookshelf and chapter reader: QR or Cookie login, store search, resume reading, and weread_shelf / weread_search / weread_book / weread_chapter agent tools.

A composer control: shows the current model's context window, the tokens in use and a rough system / tools / messages breakdown, switches the model's reasoning effort in one click (writing reasoningEfforts into the model config when the provider declares none), and edits the model's contextWindow from the same panel so custom-provider models stop silently inheriting the adapter default. Makes no model calls; reads only the official session projections, model directory and config-forms services.

Optional source-preserving chat rendering with selectable themes, offline Uiverse component tools and a report-local evidence workspace. Preview for DSH 0.2.1-rc.2.

Una vista previa visual que convierte todas tus conversaciones con la IA en un mar de estrellas.

Administrador de servidores Linux remotos en la interfaz web Harness: explorar y editar archivos, consultar el estado del host, iniciar/detener/reiniciar contenedores Docker, inspeccionar imágenes, configurar mirrors de registro Docker, gestionar reglas firewalld/ufw, cambiar fuentes apt y detectar herramientas de desarrollo o instalarlas/actualizarlas con apt.

Añade un chip Git junto al compositor para abrir un espacio central (cambios, historial, archivos, etiquetas, stash) y sección Git en ajustes. Admite OAuth device flow multi-cuenta y PAT, con permisos por repositorio (full/read/none). Un interruptor global controla escrituras; las lecturas se limitan a directorios permitidos. Rechaza push si ninguna cuenta puede escribir en el remote destino. Muestra diffs en el sitio con números de línea, 4 diseños, énfasis por palabra, resaltado sintáctico y búsqueda Ctrl/⌘+F; renderiza Markdown e imágenes inline, permite buscar y generar mensajes de commit desde el diff staged mediante el LLM del host. 8 herramientas Agent, 51 endpoints HTTP locales y 8 idiomas; tokens cifrados en reposo con AES-256-GCM.

Preset de flujo de entrega para DeepSeek Harness: protocolo de agente más 7 herramientas que conservan literalmente las solicitudes del usuario, registran decisiones sobre comentarios, planifican tareas con criterios de aceptación, anotan comandos reales de verificación y códigos de salida, y guardan resúmenes de finalización en archivos JSONL por proyecto.

Entrada de voz push-to-talk totalmente local para macOS: mantén una tecla global, habla y suéltala; la transcripción se pega donde está el cursor. STT local-first (Qwen3-ASR 0.6B 4bit en MLX/Metal por defecto; alternativas SenseVoice y mlx-whisper), cero APIs externas, servicio launchd autorreparable y diagnóstico de 8 puntos. La Skill incluida permite al agente instalar, configurar (conexión de tecla Karabiner, launchd, cambio de motor) y resolver problemas de toda la cadena.

Prepara archivos antes de que los abran read, read_image y las herramientas vision. Reduce imágenes de más de 2048 px o 8 MB (un reintento a 1280 px durante el mismo turno), convierte BMP/TIFF a PNG y corrige cargas mal etiquetadas examinando sus bytes. Convierte docx/xlsx/pptx, PDF con capa de texto, csv/tsv y html a Markdown sin programas externos.

Una instalación añade dos herramientas PDF a la barra lateral DSH: saltar a cualquier página y recorrer el documento con páginas y miniaturas renderizadas localmente, o abrir un PDF en la vista previa nativa de DSH. También incluye el comando /pdf.

Generación de imágenes sin clave: dos herramientas, generate_image (sin API key, una llamada a la vez y reintentos limitados ante límites de velocidad) y list_image_models.

Inicia el espacio local publicado BeatDesign 0.2.5 y conecta 30 herramientas MCP para Projects, Canvas, Assets y Timeline; incluye una Skill para proyectos compartidos y pautas de aprobación para generación remota. Requiere Node.js 22.12+ y un puerto libre 3020. Extraer fotogramas y renderizar la línea de tiempo también requiere ffmpeg/ffprobe en PATH; la exportación desde el navegador no.

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.

Herramientas de VPS con prioridad de lectura: estado y acciones de systemd, uso de disco, estado de Docker, últimas líneas de logs, copias de seguridad configuradas y horarios de silencio.

Herramientas de agente para un descargador Gopeed local: crear, consultar, pausar, reanudar y eliminar descargas; diagnosticar o iniciar el proceso Gopeed; y recibir eventos de descarga terminada mediante un trabajo en segundo plano de DSH que despierta a un agente inactivo. El diagnóstico y el inicio del proceso solo funcionan en Windows; las herramientas de descarga funcionan donde se ejecute Gopeed.

Lista de bloqueo de herramientas deny-only por preajuste de agente. Al desactivar una herramienta en Ajustes, desaparece del esquema de herramientas de ese modo y se rechazan sus llamadas. No se puede habilitar ni incluir nada en una lista permitida; las herramientas que nadie haya bloqueado permanecen intactas. Cada fila muestra la descripción de la herramienta y las reglas viven en un archivo JSON fuera del perfil.

Memoria para DeepSeek Harness con cinco secciones (recuerdos, árbol, perfil, gráfico y bandeja), una política de captura basada en reglas para decidir qué conservar y descartar, y recuperación híbrida BM25 más vectores sobre fragmentos padre e hijo. Incluye doce herramientas de agente que leen y escriben los mismos archivos locales.

Kit de investigación local primero para DSH que registra las herramientas de modelo kb_grep, kb_list y kb_save. kb_grep busca en la base Markdown con ripgrep si está disponible y recurre a grep en caso contrario; kb_list y kb_save usan el servicio de archivos ctx.fs con alcance limitado. La base está en ~/dsh-kb de forma predeterminada. Incluye las habilidades local-first-research y session-memory, que debes copiar manualmente a un directorio de habilidades de DSH: el bundle del complemento no las instala. Es Node ESM puro, sin dependencias npm en tiempo de ejecución, e incluye un banco de pruebas node sin framework. Se instala con plugin_manager install_bundle; no incluye interfaz de cliente.