Plugins

Registers a web-design skill and a compact system-prompt rule set for a three-layer shadcn/ui, Canvas UI and ThreeUI workflow, with Radix UI Colors and strict interface rules.

Read-only Linux port, service and Caddy route map: listening sockets with their owning unit or container, a reverse service view, and reverse_proxy liveness checks. Requires iproute2 (ss); docker and a Caddyfile are optional.

Connects the Google Stitch MCP endpoint as 16 native `mcp__stitch__*` tools for projects, screens and design systems, plus an in-panel card for an API key or OAuth access token.

Windows multi-drive dashboard that labels cleanable, caution, and system space in green, yellow, and red, audits download folders, and suggests how to split data across drives.

Lingshu (灵枢) is a brain-inspired persistent memory, persona and growth system for AI coding agents: pure Python standard library, zero dependencies, local-first, requires Python 3.9+. Hippocampus/cortex layered memory with forgetting curves, dream consolidation, fuzzy associative recall, emotion system, persona drift protection, knowledge base, self-authored skills, and a local web panel. Ships as a DSH bundled skill; the separately installable cordis startup-injector plugin auto-injects sessions only when Lingshu is installed at ~/.dsh/skills/lingshu (or brainPy is configured) and Python 3 is available; the optional update check contacts GitHub/Gitee.

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.

Injects a Chinese-thinking reminder around the system prompt and the per-step runtime snapshot, so the agent reasons in Simplified Chinese instead of drifting back to English as the transcript grows.

Persona state and slow evolution for DSH: five state dimensions (valence, arousal, energy, intimacy, focus) move after each turn and decay toward baseline over time; four scene types (work, tech, support, chat) modulate reply length and warmth without changing the state itself; the Soul panel keeps a persona card plus weighted evolution entries and injects them into the system prompt persona-first under a 4000-character budget. Ships a browser panel with a persona card editor and a live state view (the five metrics plus the last six events), appends a JSONL trace to disk, and seeds a usable default persona on first install.

Multi-select system-prompt picker behind one bolt icon in the composer: check saved rows to inject in order, each scoped to main agent, sub-agents or both; every row (the built-in 「火力全开」, "Full Throttle", included) can be edited and deleted, and Clear all only unchecks every row without deleting anything.

Injects a configurable whale-girl persona into every DeepSeek Harness system prompt.

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.

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.

A pomodoro timer docked below the composer in DeepSeek Harness, with focus/short/long cycles, a resumable countdown, chime, optional system notifications and bilingual settings.

Mouse gestures for files in the DSH Web GUI: Ctrl+click a file-tree row, a file mention in a chat message or a file link to open it with the desktop default application; right-click opens a menu with open, the operating system own Open With chooser, save a copy, copy the absolute path, copy the file contents and reveal in the file manager, while a right-click on an ordinary hyperlink offers Copy Link. A plain click is left to DSH own sidebar preview and directory rows are left to the page. Open and reveal go through DSH own sessionController, the Open With chooser is the operating system dialog (Windows Shell verb, open -a on macOS) rather than an application list kept by the plugin, and file reads stop at 8 MiB.

Two split composer controls for output style: a tone switch with its own preset menu, and an output-form switch with its own preset menu; each session's system prompt follows the selections, with a preset settings page.

Prompt manager for DSH: imports prompts from JSON or Markdown sources and DSH's own system sections, then controls what is injected into the system prompt — per prompt, or per agent preset — with title editing, drag ordering, LLM translation and the option to inject either the original text or its translation.

Keeps a DSH agent turn open when the model ends it by narrating work it has not done: on agent/turn-stopping the guard replays the turn log and classifies the trailing message with regexes — in the default mode wait / ask / done / gray end the turn with no model call, and only a promise of pending work reaches the judge — then has a judge chain of four dialect kinds (System One three-question noul, OpenAI-compatible, OpenAI Decisions, or the session LLM) rule on whether the turn is really over, and steers the same turn with agent.steer when it is not.

Loopback-HTTP/SSE-Dienst, über den ein Obsidian-Plugin DSH-Sitzungen steuert (auflisten, erstellen, senden) und bis zu 10 Vault-Notizen in den System-Prompt einfügt. Das passende Bridge-Plugin muss in Obsidian laufen.

Fügt eine persönliche Regeldatei in den System-Prompt von DeepSeek Harness ein; Schutzregeln gegen stille Schritte und wiederholte Aktionsschleifen lassen sich im Einstellungsdialog bearbeiten.

Sucht CCF-Konferenz-Einreichungsfristen mit den Tools ccf_deadlines, ccf_venue und ccf_update, dem Befehl /ccf und einem Desktop-Panel, das die Liste als Kalender exportiert. Termine stammen aus Live-Synchronisierung (CCFDDL als Hauptquelle, Abgleich mit einer zweiten Quelle; Abweichungen ab 36 Stunden gelten als Quellenkonflikt). Keine Daten sind mitgeliefert; die kommenden 30 Tage stehen im System-Prompt.

VPS-Werkzeuge mit Lesevorrang: systemd-Status und Aktionen, Speicherplatz, Docker-Status, Log-Enden, konfigurierte Backups und Ruhezeitenpläne.

Local-first-Recherchekit für DSH mit den Modellwerkzeugen kb_grep, kb_list und kb_save. kb_grep durchsucht die Markdown-Wissensbasis mit ripgrep oder, falls es fehlt, mit grep. kb_list und kb_save nutzen den eingeschränkten ctx.fs-Dateisystemdienst; Standardpfad ist ~/dsh-kb. Die Skills local-first-research und session-memory sind ebenfalls enthalten, müssen aber selbst in ein DSH-Skill-Verzeichnis kopiert werden; das Plugin-Bundle installiert sie nicht. Reines Node ESM ohne npm-Laufzeitabhängigkeiten, inklusive frameworkfreiem node-Test-Harness. Installation mit plugin_manager install_bundle; keine Client-Oberfläche enthalten.

Gemeinsame Architektur-Wahrheitsquelle für Agent und Menschen in DeepSeek Harness, ohne den gesamten Code zu lesen. Der System Prompt enthält nur `MEMORY.md` und Links zu den `SPEC.md` der Module; `JOURNAL.md` protokolliert Aktionen separat.

Vom Nutzer verwaltete System-Prompt-Einfügung für DeepSeek Harness: Eigener Text lässt sich am Anfang, in der Mitte oder am Ende des zusammengesetzten Prompts platzieren und mit Live-Vorschau im Web-Panel der linken Spalte bearbeiten.