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.

Service HTTP/SSE loopback permettant à un plugin Obsidian de piloter les sessions DSH (liste, création, envoi) et d'injecter jusqu'à 10 notes du coffre dans le system prompt. Le plugin Bridge compagnon doit tourner dans Obsidian.

Plugin de déclenchement événementiel (la sonnette du réveil dsh-proactive) : fs watchers récursifs avec fenêtres debounce et filtres glob par règle ; endpoint webhook limité à loopback avec token par règle ; règles qui réveillent une session cible via le service agents du host (follow-up direct si active, reprise préalable si inactive) et injectent un prompt avec variables paths/count/payload. Recharge à chaud le fichier des règles sans redémarrer DSH et ajoute chaque déclenchement à un historique JSONL auditable, consultable avec trigger_list et trigger_history. Route health loopback pour l’état d’installation et d’exécution. Exemples : ranger les téléchargements, diagnostiquer les alertes CI de scripts locaux, surveiller les logs. JavaScript pur, sans build, MIT.

Passerelle d’appairage DeepSeek Harness. Par défaut, HTTP clair sur `127.0.0.1:3443` avec proxy inverse vers DSH local `http://127.0.0.1:3080` : aucun certificat pour un usage loopback. Production, accès intermachines et apps exigent TLS : configurer `tlsCert`/`tlsKey` et `publicUrl` HTTPS (auto-signé accepté). Caddy/Nginx en frontal peut terminer le TLS public, mais la passerelle garde besoin des clés et le proxy doit revenir en HTTPS. QR TOTP, connexion utilisateur/mot de passe, sessions glissantes avec plafonds absolus, révocation d’appareils, admin Web/journal d’audit et tunnel WebSocket. Assistant Web au premier démarrage. Tests indiqués : 171/171 unitaires, e2e en 29 étapes.

Notifications de bureau et badge non lu sur l’icône pour DeepSeek Harness : fin de tour, approbation/question/confirmation de plan en attente ou échec. Le badge compte les Sessions à traiter et s’efface après consultation ; cliquer sur une bannière ouvre la Session correspondante. Suit la langue DSH ; Sessions subagent silencieuses par défaut ; titre/longueur configurables ; interface HTTP loopback pour scripts et outils.

Réorganise un transcript de Session DeepSeek Harness en document Markdown structuré avec plan et table des matières, au lieu d’un export chronologique. Trois accès : outil model `session_to_doc`, bouton composer et endpoint Web réservé au loopback.

Exécute le CLI Codex local depuis DSH avec quatre outils (codex_run, codex_resume, codex_queue, codex_probe) et fournit un bridge loopback autonome avec API HTTP et boîte aux lettres fichier.

Exécute le CLI Claude Code local depuis DSH avec trois outils (claude_run, claude_resume, claude_probe), analyse `--output-format json` et renvoie le session id pour les suivis ; bridge loopback autonome inclus.

Accédez à ce Harness depuis votre téléphone sur un réseau privé : passerelle qui préserve loopback, codes d’appairage à usage unique et mise en page portrait pour petits écrans.

Audit en lecture seule pour vérifier si les routes HTTP d’un plugin DSH sont réellement protégées. Découvre les routes enregistrées en lisant le code du plugin et ne sonde que loopback.

Guide de déploiement NapCat sous Windows et vérifications en lecture seule du port loopback pour DSH, avec lanceur administrateur déclenché par l’utilisateur.

Prédit la suite de la saisie par un texte fantôme au curseur, mesuré avec la police et la ligne de l’éditeur ; Tab insère, Esc ignore. Un appel bref au routeur de modèles de la session reçoit le brouillon et les derniers tours. Même un champ vide obtient une suggestion à la voix de l’utilisateur ; les répétitions de l’assistant sont écartées. Host : route HTTP loopback à sous-chemins, corps limité à 64 KB, cache résultat 2 min/échec 20 s, un appel simultané par session. Client : overlay de hauteur 0 sur `conversation.input.overlay`, insertion protégée par CAS `draftRev` et fallback `setDraft`. Ignore composition IME et slash commands. 16 tests `node:test`, aucune dépendance runtime, captures réelles dans docs.

Routage OpenRouter par session : un relais loopback ajoute `service_tier`, `provider.sort` et la variante `:floor` à chaque requête selon l’un des neuf préréglages choisis dans le badge du compositeur. Chaque préréglage épingle une URL de base pour les valeurs par défaut de route.

Relay loopback servant de route proxy à l’ensemble du processus DSH : `web_fetch` fonctionne sous TUN fake-ip ; si le proxy tombe, la même requête passe en direct, puis une sonde revient au proxy.

Pilote un client MaaEnd (MXU) local via son API HTTP loopback : 10 outils agent `maend_*`, panneau de tableau de bord latéral et étapes de mise en sourdine au démarrage/fermeture du jeu conditionnée au succès dans la chaîne de tâches. Installation locale de MaaEnd avec service MXU actif requise.

Pool de routes sortantes DSH : 15 seeds + 20 candidates, 7 types de cible. Vérifie par handshake TLS avec chaîne complète (TCP seul peut déclarer saine une IP morte) ; cooldown 2/5/15 min après échec, route saine la plus rapide par type. Réécrit URL via 4 règles (prefix/registry/index/host-replace) pour git/pip/npm/pnpm/gh/curl/wget par shell bash (seuls downloads lancés par le modèle ; pas pwsh/host/renderer), tools line_pool_status/line_pool_run ou child-process spawn. Garde les retours ligne, change seulement le segment download. Jamais user:token@host ; aucun token aux mirrors, config globale intacte. Panel : latence/échec/cooldown, retest/activation par route, TUN/proxy via network-adapter/loopback. Revalide candidates toutes les 24 h.

Prévisualise un OpenWiki du workspace dans la barre latérale droite de DSH Web : détecte le répertoire wiki, le sert avec le lecteur officiel `openwiki visualize` sur un port loopback et l’intègre dans son onglet. Compare les versions installées d’openwiki et DSH aux seuils configurables et vérifie ses mises à jour dans le registry.

Connecte une session locale MiniMax Code existante à DeepSeek Harness via un proxy inverse loopback, sans stocker de clé API ni se déconnecter.

Expose tous les modèles de tous les fournisseurs configurés dans DeepSeek Harness via un endpoint unique compatible OpenAI sur un port loopback fixe. Une page latérale présente l’URL de base, la clé, le port et les IDs de modèles.

Transforme la barre de navigation entre tours DSH en visualiseur musical : chaque barre fine grandit et s’épaissit selon sa bande de fréquence, à partir du son système capturé par WASAPI loopback. Géométrie uniquement, sans couleur ni halo ; l’aperçu au survol, le saut au clic et le défilement à la molette restent disponibles.

Réimplémente sur loopback l’API de lettres Olivia Lin de BSide, arrêtée, afin que le client du jeu continue d’échanger. Chaque lettre reçue va à une session DSH persistante et la réponse est écrite dans le stockage local.

Interface de contrôle du gateway LAN dsh-lan-gate : bouton près de Settings indiquant l’état et le nombre de téléphones/tablettes en attente ; ouvre la page d’approbation limitée à loopback sans saisir l’URL. À l’arrêt, lance d’abord la tâche planifiée. Masquée sur téléphone/tablette.

Restaure les réglages persistés par l’hôte lorsque dsh web passe par un proxy inverse sur un nom d’hôte non loopback. Marque la connexion comme détenue par l’hôte afin que Settings et Models se chargent au lieu d’afficher une indisponibilité dans ce navigateur. Pour dsh 0.1.7–0.2.x.

Transmet en loopback l’état de l’agent DeepSeek Harness (état, appels d’outils, erreurs, attentes d’approbation) à WhalePal, une application de compagnon de bureau indépendante, afin que l’animal suive l’état réel de l’agent plutôt que de le deviner à partir de captures d’écran.