プラグイン

A draggable floating card for the DeepSeek Harness Web UI that combines a Pomodoro timer, an off-work countdown, and focus statistics. Each launch opens on a fresh focus phase; phase completion triggers a chime, card shake, and particle burst. Includes today's pomodoros, focus minutes, streak and a 7-day chart, rhythm reminders for wrapping up and eye rest, optional system notifications, tab-title timer, auto-start, +5 minutes, and a custom long-break interval.

Wall-clock session and turn timing for the DSH Web GUI: a timer chip in the session header shows the session total, every finished turn shows its own duration above the action row, and a sidebar panel totals today, the last 7 days, the last 30 days or all sessions. The numbers are folded from turn/start and turn/end in the session log, so paging, compaction and restarts do not change them.

A countdown pill under the message composer showing how long is left before you clock off, with a workday progress bar; click it to set your own start and end times (saved in this browser), and after the end time it turns into an overtime timer.

Floating Pomodoro dial pinned to the bottom-right of the Harness UI: custom focus, short-break and long-break lengths, a long break that can be switched off entirely, a round counter, and five Web Audio-synthesized alarm sounds with preview, volume and ring duration. No audio assets and no network requests; timer state survives a reload.

ADHD-friendly focus soundscape docked under the DSH composer: five procedurally synthesised noise colours (white, pink, brown, rain, fan), a live-waveform orb, a 15-90 minute fade-out timer and a liquid-glass popover. No audio assets and no network requests.

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.

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.

ホストモデルと会話し、クリック時にアニメーションを再生し、タイマーでささやき、タスク進捗を読み上げ、アカウント残高を表示する透明な常時最前面デスクトップペットです。DSH Desktopの同梱Electronランタイム（~/.dsh/electron）で動作し、ランタイムがなければ表示されません。Windows専用です。

DeepSeek Harness Web UI向けPomodoro timer。集中/短休憩/長休憩cycleを備えたsidebar pageと、composer横のcountdown pillを提供します。

DeepSeek Harness向けZen mode。タスク中はDSHを隠す全画面の簡潔なtimerを表示し、経過/残り時間、そのround中に完了したsession、終了buttonだけを示します。

タスクの途中で一息。現在のstepを完了してから一時停止します。再開しても新しいrequestは送られず、tokenも消費しません。timerは停止中に進まず、現在のsessionだけが対象です。

チャットcomposerに送信時刻を指定する機能を追加し、選んだ時刻にHostがメッセージを配信します。既存の会話から設定したタスクはその会話に参加。新規セッション画面からの場合は、各タスク用のSessionを事前作成します。

DSH Web GUI向けのドラッグ可能なフローティング時計です。アナログ／デジタル表示、集中タイマー、4つのテーマを備え、配置を保持します。

composer横のボタンで現在の下書きを実行しやすいpromptへその場で書換え。軽い整文から大幅展開まで4つのpresetと独自presetに対応。composerを離れずボタンからpreset、prompt model、推論強度を切替。下書きに内容がある時だけ表示し、強化中はtimer、失敗時はretryを表示。

DSHのターンタイマー。現在のターン経過時間を表示し、`/set-time`スケジュールで締めくくりpromptを注入してエージェントに実行を続けず終了準備させます。composer上に実行時間カードも表示。

四象限タスク付きPomodoroタイマー。集中ラウンドをタスクに結び付け、EXPと実績でデスクトップペットを育て、エージェントがラウンドを開始したりタスクを提案したりできます。

Move45clock：DSH向け健康時計。45分timer（5–120分）、12種の座位/立位休憩（各12–15分）、視覚plan editor、200%まで拡大するreminder cards、21:30/22:00/22:30の夜間通知、任意の動作名voice coaching。window wakeはWindowsのみ。

アイドル時またはタイマーでAkasha sleep蒸留を予約し、新規セッションへ起床メモ、各ターンへメモリパルスを注入する。アイドル後の任意再開は既定オフ。連携先：Hindsight（repo memory）、OpenViking（user memory）、組み込みsession search（repo README参照）。設定：repoをcloneしcore/を別コピー、cordis.patch.ymlの<AKASHA_DIR>を絶対パスに置換するかAKASHA_DIRを設定。残ると明示的に失敗。

DSH用の標準25/5/15ポモドーロタイマー。各回を現在のセッションタスクまたは手入力プロジェクト名に紐付け、統計パネル、365日ヒートマップ、ドラッグ可能な浮動タイマー、隅のピルを備える。ローカルJSON保存とCSV出力。

控えめなコンパニオンパネルを追加。ポモドーロタイマー、todoリスト、水分補給・立ち上がりリマインダーに加え、インストール済みデスクトップペットによる授業解説と宿題指導を利用できます。

ドラッグ可能なフローティングウィンドウで遊ぶマインスイーパー。初手で地雷を踏まない配置、旗、周囲一括開示、タイマーを備えます。

DeepSeek向け料金認識型タスクランナー。オフピーク料金帯ではプロンプト一括処理をdeepseek-flashへ直送し、ピーク時間が近い場合は自前タイマーで待機し、次のオフピーク開始時に実行します。peak_run、peak_schedule、peak_cancelを提供し、ピーク時間帯と先行時間を設定できます。

明確な結論が得られる最も安価な検証方法を選ぶskill。静的チェックから単一テストスイートまでL0～L3のコスト段階、固定sleepの代わりにタイムアウト付き条件ポーリング、実際に失敗できるassertion、記憶に頼らない実測時間を定めます。skillが使う依存ゼロのNodeツール2つも同梱：`wait-until.mjs`（コマンド、ファイル、ポートの準備完了までポーリング）と`measure-runtime.mjs`（任意コマンドの実時間計測、ライブラリ利用向けの区間タイマー）。

DeepSeek Harness の座りすぎ防止リマインダー。Web GUI の隅に猫を表示します。30～90分の集中サイクルが終わると、中央オーバーレイと3連チャイムで立ち上がって歩き、水を飲むよう促します。「later」ボタンでは、長時間座位と脱水について、出典付きの事例を各1件表示します（出版社・日付・原典リンク付き17件の内蔵ライブラリ）。停止時は作業時間、完了サイクル、成功した中断、スヌーズ、推奨飲料、良／中／悪の評価を含むセッションレポートを作成します。猫はドラッグでき、位置を記憶し、状態ごとにアニメーションをループします。タイマーと記録はホスト側にあるためページ更新でサイクルは失われません。DSH ページが開いている間だけ通知します。サイクル時間、チャイム音量、猫の不透明度、評価基準を設定できます。