Plugins

Article Review page in DSH's native right sidebar: turns Markdown, Word, PowerPoint and PDF articles into sentence-addressable text, lets you comment on any sentence, and hands the open review to the agent as a composer draft. Six agent tools let the model read every comment, revise the article (in place for Markdown, as a sibling <name>.revised.md for binary formats), resolve the comments, and record recurring wording preferences into a long-term writing-style memory injected into later prompts. Extraction includes a dependency-free DOCX/PPTX reader; PDF text extraction runs locally through the host's Python + pypdf (or poppler's pdftotext) and is unavailable without one of them. No network calls, no telemetry.

Windows companion window for DSH Desktop: todos, reminders, live session messages, approval handling and quick input. The standalone window reuses the Electron binary at %USERPROFILE%\.dsh\electron\electron.exe (usually installed by dsh-pet) and fails to open when it is missing.

Save any URL (intranet GitLab raw, public GitHub, etc.) to a local file — text or binary — with a text preview and skip-on-exists.

Exposes a DSH Harness Desktop Web GUI to another machine's browser through a Cloudflare quick or named tunnel, an SSH reverse tunnel, or a custom command, and hands out a tokenised link to open it; a settings panel and CLI start, stop and print the link. Needs DSH Desktop 0.2.1-rc.2 or later and a cloudflared binary.

DSH host bundle for the ShyPie privacy-first workflow: extracts attached images and PDF/Office files locally before they enter the session log (requires poppler pdftotext/pdftoppm on PATH and a local OCR engine — Windows OCR on Windows, optionally tesseract; without them attachments are rejected, not forwarded); default filter mode keeps text local (optional local masking); blocks unmaskable media/BLOB; provides local privacy_extract, privacy_ocr, privacy_scan and privacy_rehydrate tools; denies read_image and binary/document reads outside allowPaths; and enforces a fail-closed llm/stream egress guard before DSH dispatches a model request (the default provider allowlist is the local Magpie gateway).

Audio tools for DSH: inspect audio/video files, transcode or extract the audio track from a video, trim and normalize, and transcribe speech with local whisper.cpp or an OpenAI-compatible API. Requires ffmpeg and ffprobe on PATH (not bundled); local transcription also needs a whisper-cli binary and a ggml model.

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. From 0.4.9 the panel can no longer vanish. It used to be possible for the whole section to disappear from the plugin detail page while the host stayed up, the HTTP bridge kept answering, and no crash log appeared. The cause was a scope error: a helper was declared inside one component and referenced from another, which is valid JavaScript and passes a syntax check, so it only threw at render time - when the user pressed the scan button and React ran the state updater. The host wraps every slot entry in an error boundary whose fallback is an empty anchor, so the throw was contained silently: the panel was not uninstalled or crashed, it was replaced by an empty div. The helpers now live in the module scope both components share. The client half also gained its own test - a minimal React stand-in loads the real panel module, really calls the components, really runs the state updater at render time, and presses the scan button, so this class of error can no longer reach a release.

Matrix-style binary (0/1) digital rain for the Harness interface: columns spawn only while a task is running, fall off screen when it stops, and leave a blank canvas; frosted-glass token layer with adjustable digit and panel opacity.

Navegador integrado ao DSH: Chromium dedicado com perfil persistente para manter logins Feishu ou SSO após reinícios, controlado por CDP. Expõe sete ferramentas de agente (status, open, eval, read, feishu, screenshot, tabs) e espelha a página ao vivo em um painel lateral direito que as chamadas abrem automaticamente. Também converte documentos Feishu em Markdown percorrendo a rolagem virtual. Requer Chrome, Edge, Chromium ou build Chromium do Playwright instalado localmente (não baixa nenhum); defina o binário em DSH_IAB_CHROME.

Agrupa as 207 ferramentas de engenharia reversa da REA (backends Ghidra / Hopper / IDA) em 3 comandos e 6 entradas de domínio somente leitura: cerca de 5 KB de texto residente em vez de 757 KB. Não inclui engines; rea-agents, Ghidra 12.1.x e JDK 21 são instalados separadamente.

Ferramenta de recorte para o composer: congela a tela, permite arrastar uma região, anotá-la e inserir o resultado na caixa de mensagem. Apenas Windows; o seletor é um binário WinForms incluído (recompilado do código C# incluído se estiver ausente) e usa o .NET Framework 4.x integrado.

Ferramentas Agent baixam vídeos do Bilibili, transcrevem com whisper.cpp local e criam notas Markdown estruturadas com o LLM do próprio DSH; PDF opcional. Somente Windows. Dependências ausentes são baixadas para o diretório de dados: yt-dlp.exe, build ffmpeg.exe do GitHub/gyan.dev, binário whisper.cpp x64 do GitHub e modelo de ~547 MB do hf-mirror. Exportar PDF chama Edge/Chrome instalado localmente. Duas ferramentas: video_to_notes e note_to_pdf. JS puro, sem API key extra.

Workspaces remotos para DeepSeek Harness: escolha um host SSH, monte uma pasta do servidor via SFTP como Workspace e use ferramentas comuns de arquivo para ler/gravar/editar. A monitoração remota usa polling para atualizar a árvore; prévias de imagem/binário usam leitura byte-range. `remote_exec`, `remote_search`, `remote_git` fornecem shell/busca/consultas Git remotas. Patch opcional roteia bash/pwsh do agente ao servidor. Toda escrita/comando remoto segue sandbox da Session e política de aprovação; o plugin gerencia hosts com senha, chave ou SSH agent.

Transforma capturas existentes do repositório de um plugin em reel hero silencioso de 6 s em loop: webm VP9 com alpha real, gif com fundo, frame de capa e manifest com durações de cena/sha256. A linha do tempo é exata: tempos inteiros mais transições somam `durationMs`; a última cena volta à primeira (t=0 = t=duration). Zero dependências runtime: PNG com node:zlib, compositor SDF e ffmpeg como único binário externo, localizado em execução. Painel para escolher, reordenar, gerar e copiar Markdown do README; CLI com contrato `storyboard.json`.

Monta o servidor MCP do app desktop pen.dev em uma sessão DSH para o modelo ler e editar o documento `.pen` aberto no Pen usando get_app_state, read_skill, execute, get_style e ferramentas browser. Procura o binário do servidor em caminhos usuais e adiciona um skill pen-dev e um listener que converte erros de limite do plano pen.dev em aviso bilíngue curto. Não oficial e sem binários do pen.dev.

Fixa uma barra de progresso sempre no topo enquanto o agente executa um turno; a porcentagem é estimada pelas chamadas de ferramentas e chega a 100% ao terminar. Exibe a faixa de tarifa pico/fora de pico do DeepSeek; operações grandes de tokens só pedem confirmação nos horários de pico. Executa em processo Electron separado e é ignorada se não encontrar o binário Electron.

Abre malhas STL na pré-visualização de documentos da barra lateral direita: análise binária/ASCII no navegador, viewport WebGL e estatísticas de tamanho, volume, área de superfície e estanqueidade.

Registra doc_ocr_sign_fix no DeepSeek Harness para macOS. Diagnostica e corrige Python incluído que não carrega extensões nativas onnxruntime/cv2/pypdfium2 por hardened runtime/library validation: injeta entitlement disable-library-validation, reassina o interpretador e verifica imports. Idempotente, salva o binário original antes e nunca altera o bundle externo do app.

Somente macOS arm64; requer dsh-pet instalado (o binário incluído é pré-compilado e fechado). Um controle na área de trabalho mostra/oculta o pet com um clique e pode iniciar o DeepSeek Harness se estiver fechado.

Abre malhas STL na pré-visualização de documentos da barra lateral direita: analisa formatos binário/ASCII no navegador e mostra um viewport WebGL com estatísticas de tamanho, volume, área de superfície e estanqueidade.

Provedor computer-use DSH Cordis para o desktop ZTE Nde. O postinstall do npm install baixa para o bin/ do próprio pacote o binário musl criado por computer-use-linux-zte-nde (v0.7.7-nde.3). resolveCommand() resolve o caminho cordis a partir de import.meta.url, fazendo o mount funcionar independentemente do cwd do dsh web. Reserva o slot compartilhado com ctx.computerUse.register e expõe ferramentas MCP com prefixo mcp__computer-use-linux-nde-mcp__* (screenshot, list_windows, click, type_text, doctor etc.). Duas correções Nde upstream: fallback KWin caption+coords para direcionar janelas e SnapshotCache, que limita AT-SPI a uma ativação por (app, pid) para não perturbar Chrome repetidamente.

Exige plano obrigatório de 7 etapas por solicitação, cada uma com critério binário; workers executam e verificadores independentes sem memória reportam PASS/FAIL com evidências.

Plugin host do DSH que controla pelo chat o gerenciador local de versões de runtime ployenv: listar/instalar/usar runtimes, fornecer o binário ployenv e injetar sua lista de caminhos nos shells da sessão.

Bancada de engenharia reversa: conecta o IDA Pro local ao DeepSeek Harness por `@deepseek-ai/dsh-mcp-client` e expõe as ferramentas `ida-pro-mcp` (decompile, xref, rename, comment) como `mcp__ida__*`. Seleciona automaticamente o modo headless `idalib` (analisa o binário no processo, sem GUI do IDA) ou GUI (RPC do plugin IDA na porta 13337). Adiciona `re_check`, `re_open`, `re_status`; o servidor MCP incluído é um proxy stdio sem dependências, utilizável por qualquer cliente MCP.