플러그인

DeepSeek Harness 플러그인: 「推理等级」(reasoning effort) 단계 이름을 중국어로 번역하고, 연결된 커스텀 서드파티 모델에 추론 레벨 표시를 주입합니다(공식 DSH는 기본적으로 주입하지 않음). 선택한 단계는 아웃바운드 요청 본문에 기록됩니다.

DSH Web GUI 모델 능력 구성: '설정 → 모델'의 모델 카탈로그 편집기에서 구성된 각 모델에 대해 이미지 지원 여부와 사고 수준(reasoning efforts)을 직접 설정하고 llm-pi-ai 설정 네임스페이스에 기록합니다.

DeepSeek Harness에서 추가 모델의 추론 강도(reasoning effort)를 선택할 수 있으며, 끄기(Off)와 5가지 표준 레벨을 제공합니다.

비공식(사용자 정의) 프로바이더의 모델에 대해 추론 강도 수준, 컨텍스트 윈도, 출력 한도, 이미지 모달리티를 models.dev 데이터로 자동 채웁니다.

기본 모델 옆에 GUI 서브에이전트 모델 선택기를 추가합니다. 세션별 모델과 추론 effort를 선택해 같은 프로세스의 모든 서브에이전트에 적용합니다.

플로팅 추론 노력 슬라이더 팝업이 있는 공식 스타일 컴포저 모델 트리거.

An Auto toggle next to the model selector that schedules reasoning_effort per request: complexity is scored by a MiniMax/OpenAI-compatible model with a zero-dependency heuristic fallback (fallbacks not cached), effort ladders adapt to each model real effort list (low/high/max, low/high/highx/max/ultra, ...), heavy tool calls upgrade the tier within the same session, the lit button and reply badges show the effort actually used, and every decision plus observed token usage lands in a JSONL ledger. A standalone-button fork of imkingjh999/dsh-adaptive-effort; do not install it and the original in the same profile.

Adds twelve chain_* tools that turn a multi-agent role chain into a round state machine with one frozen baseline, per-signature failure counts, role-tagged evidence, blocking approval gates and one audit file per round; also adds a settings card that routes each role to its own model and thinking effort. Requires the sibling l1-presets bundle (plan/work/code/review) - without those presets every chain_* tool returns ROLE_DENIED.

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.

Fills in a cheaper sibling route for subagent calls that name no provider, model or effort (for example Opus to Sonnet 5.5 at medium, Qwen max to Qwen flash); explicit choices and subagent_fork are left alone. Requires the subagent-model-selection policy to be enabled with the target route in its allowedModels - without it the plugin does nothing.

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.

Adds a Speed setting (Standard, Fast, Ultrafast) for OpenAI and Codex models on a patched dsh build: the pinned dsh 0.2.1-alpha.2 web client needs the Model / Effort child-slot patch, the host pi-ai must be 0.87.1 or 1.1.0 (other versions refuse to load), and the terminal controls need a patched dsh-TUI. Then it appears in the Model / Effort menu; in dst /speed opens a picker or sets the speed directly, and the current speed shows in the bottom bar. The choice is kept per session, provider and model and is sent as the request service tier.

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.

Replaces the composer Effort switch with a Claude Desktop-style five-level slider (Low/Medium/High/Max/Ultracode): a pill opens a popover above the composer, dragging snaps with a spring and writes once, and Ultracode maps to the model's maximum reasoning effort. Ships the Ultracode WebGL2 energy grid, a per-session Ultracode mode, and passes modifier-click through to the shipped level list.

DeepSeek Harness 모델 메뉴에 추론 노력 슬라이더를 추가하며, 노력 메타데이터를 선언하지 않은 공급자와 모델에도 표시합니다. Microqian2th/dsh-codex-effort-slider를 기반으로 합니다. 이 플러그인을 설치하기 전에 상류 플러그인을 제거하세요.

DeepSeek Harness에서 기록된 생성 provider/model과 reasoning effort를 응답 사용량 및 시간 옆에 표시합니다.

DeepSeek Harness composer용 2단계 모델 선택기입니다. 먼저 provider를 탐색한 다음 모델을 고르며, 모든 provider 전체 또는 한 provider 안에서 검색할 수 있습니다. 지원되는 추론 강도를 선택하고 화살표 키, Enter, Esc로 탐색합니다.

Claude Desktop에서 영감을 받은 composer 슬라이더로 클릭, 드래그, 키보드 조작을 통해 모델 추론 강도를 조절합니다.

composer 옆 버튼으로 현재 초안을 더 실행 가능한 prompt로 바로 다시 씁니다. 가벼운 다듬기부터 적극적 확장까지 기본 preset 4개와 사용자 preset을 지원합니다. composer를 떠나지 않고 버튼에서 preset/prompt model/추론 강도를 바꿉니다. 초안에 내용이 있을 때만 보이고 강화 중 타이머와 실패 시 재시도를 표시합니다.

DeepSeek Harness 모델 순환 기능입니다. 제공자별 모델을 출처 기준으로 묶고 수동 또는 측정된 성공률/지연 시간으로 순서를 정합니다. 모델과 reasoning-effort 사전 설정을 저장하고 할당량 초과/사용 불가 시 다음 항목으로 전환해 같은 단계를 다시 보내며 실제 답변 모델을 표시합니다. 이미 설치된 제공자 플러그인의 모델(Antigravity accounts, Codex subscription, DeepSeek API keys)을 사용하며 모델 접근 자체는 제공하지 않습니다.

composer용 Codex 스타일 모델 선택기입니다. profile의 모든 provider/model을 나열하고 reasoning effort 4단계와 세션별 저장을 제공합니다. 기본 선택기 비활성화 기능도 패키지에 포함됩니다.

입력창의 모델 버튼을 제공자별 모델 목록, 검색, 고정 즐겨찾기, 모델이 제공하는 단계마다 눈금 하나인 reasoning-effort 슬라이더 패널로 교체합니다. 단계가 높을수록 aurora 그라데이션과 빛남이 강해집니다. 모델별 마지막 effort를 복원하고 기본 선택기도 fallback으로 등록 상태를 유지합니다.

네이티브 DeepSeek Harness `subagent_fork` 하위 세션을 Google AI Pro(Antigravity) 구독으로 라우팅합니다. API 비용 없이 Gemini 3.8 Flash, Claude Sonnet 5.5, Claude Opus 5.5, GPT-OSS 120B를 사용하며 실시간 SSE, 전체 도구 호출, prefill cache 워밍업 게이트, 약 200ms HTTPS quota 확인, 채팅 헤더 버튼과 슬라이드형 설정/effort 패널을 제공합니다.

composer 기록에서 전체 입력 상태를 복원합니다. 내용, 이미지/파일 첨부, @ 참조, slash command와 사용했던 model, thinking effort, permission, agent preset을 함께 되돌립니다.