プラグイン

LLM-Wiki型の複数セッションMarkdownメモリとSOUL.md人格。既定では最初のユーザーメッセージ後、アクティブセッションだけに注入。remember/recall/consolidate/forget。要session format v4（公式DeepSeek Harness 0.1.7+）とsource kind plugin:memory。v0.13 engines.dshは公式0.2.1-rc.5系必須、旧host拒否；Review nowはPOST /api/memory/control action growth-nowでfloor遵守recapを1回送信。v0.14共通session gate（チップ、/memory on|off|status、設定）：offは注入/digest/想起/last_accessを止めるが削除しない。disabledWorkspacesでプロジェクト全体を既定offにし、session選択は7日保持。v0.15 Basic soulはSOUL.mdの管理ブロックのみ更新し他を維持、BOOTSTRAP完了。既存soulを反映し、未設定なら作成を促す。 checkup報告（優先行動リスト）。

Windows用Git Bashシェルツール:Git for Windowsにバンドルされたbashでコマンドを実行し、タイムアウト、サンドボックス、出力切り詰め、バックグラウンドジョブを備え、遅いpwshツールとWSL専用のbashツールを置き換えます。

エージェントの MCP サーバー・スキル・ツールを管理 — コンテキスト内の実際の状態（ロード済み／切り詰め／退避）とセッション単位の切り替えを表示します。

タスク品質を落とさずにトークンを積極的に節約する DeepSeek Harness プラグイン：ツール結果の可逆な先頭／末尾切り詰め（既定）、grep 結果の折りたたみ、圧力トリガーによる dsh-compaction-basic への委譲、アグレッシブモード用の LLM 要約 CompactionEngine サブクラス。8 シナリオの合成スイートで入力トークンを 97.6% 削減し、すべての削減は dsh のイベントフローを通るため「モデル可視 = 記録済み」が保たれます。

TRAE スタイルの会話ロールバック。10 ターンのスライディングウィンドウによる各ターンチェックポイントが touch されたファイルをロールバックし、モデルコンテキストをその場で切り詰める。セッション ID は同一を維持。人間主導のみで起動：/rollback コマンドまたは各ターン Web ボタン（中断ターンでも動作）。影響を受けるファイルとそのアクション（復元、復旧、削除、スキップ）を一覧表示するプレビュー付き。

Web GUI向けCodexスタイルの`@file`メンション：あらゆる形式（.java、.vue、PDF、画像、拡張子なし、隠しファイル）のワークスペースファイルをインデックス切り捨てなしで検索・参照。dsh-at-fileを置き換え。

自分のメッセージに編集・削除を追加します。編集すると後続ターンを切り詰め、新しいテキストを再送信します。削除するとそのメッセージと後続ターンを除去します。どちらも、削除対象のターンで記録されたファイル変更を任意でロールバックできます（その後にファイルが変更されていれば拒否）。サイドバーには会話を1つだけ保持します。

「New Session」ボタンに埋め込まれたプリセットモードバッジ: クリックして次のセッションのデフォルトエージェントプリセットを選択(長い名前は省略記号で切り詰められ、ラベルが読みやすく保たれます)。

dsh Web GUI向けの自動続行機能：ローカルモデルがoutput-token上限に達してmax-tokens通知が表示された場合、短い続行メッセージを自動でキューに追加し、返信を続けます。2系統の切り詰め検出、ループ保護、UIなし。

組み込みのキャッシュヒット率を小数3桁で表示し、切り捨てを防ぐためにComposerの統計行の幅を広げます。

スキルが認識・選択されない原因（説明の切り詰め、不正なfrontmatter、リソースファイル不足）を診断し、インストール済みプラグイン間のローダー設定の競合も調べます。プロファイルごとに記録して前回との差分を示し、重複する指摘を根本原因ごとにまとめ、長すぎる説明の書き直し用シートを作成し、スキルディレクトリ設定に触れるプラグインパッチを一覧表示します。

単一のAGENT.mdからペルソナとメモリを構成します。各セッションのsystem promptに先頭40,000文字を注入し、それを超える場合は注記付きで切り詰めます。Settingsページで編集可能。各ターンの日記行は類似度で統合し、想起頻度でグループ化し、合計2 MBの上限に達した場合だけ整理します。

ターン異常終了時に自動復旧：一時障害後に再開、出力切詰め後に続行、未完了todo後に再実行。リクエスト単位の429対策と、編集して再送/手動再試行/停止して再質問できるクライアントドック。

DSH 0.1.7-rc.2向けに作り直したTRAE風の会話ロールバックです。ターンごとのファイルcheckpointで変更ファイルを復元し、同じsession idのままモデルコンテキストをその場で切り詰めます。/rollback doctorでframework-contractを自己点検し、影響ファイルのdiffプレビューは再起動後も正確なよう現在のディスク状態と比較します。Ctrl+Shift+Zのワンクリック直前操作取消、ロールバック済みメッセージを隠す設定、英語UI文言も追加します。

コンポーザーの統計フッターの制限を解除：長いターンおよびトークン統計行が省略記号で切り詰められず、コンポーザーの全幅に広がります。

Keeps a turn from closing on an unfinished reply: a stream guard turns silent truncation into an in-step retry, and a completion protocol asks the model to declare how each turn ended.

Adds a relay_check tool that runs relaycheck against an LLM API relay to detect model swaps, misreported usage, truncated context and injected prompts; keys are read from environment variables.

Idle-time context truncation for a single DeepSeek Harness session. After a configurable number of idle hours the older conversation is dropped without a summary, so one long-lived session cannot leak one task into the next; it ships a per-session toggle, a tab next to the chat, and a /truncate command that IM bridges can drive.

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.

DSHの自動セッションタイトルを設定。最初のpromptと最初のassistant返信から「project｜task」形式で命名し、promptの切り詰めを置き換えます。bundle patchで組み込みtitle providerを差し替え、セッション行メニューにpreview dialog付きの「AI rename」を追加。設定画面ではprovider/model、命名ルール、診断event logを管理。unit test 49件。

DeepSeek HarnessエージェントのMCPサーバー、スキル、ツールを管理。実際のコンテキスト状態（読み込み済み/切り詰め/退避）と、各機能のセッション別切替を表示します。

各skillがmodel contextで使う容量を表示し、削除せずcatalogから非表示にします。ディスク上の全skillについて、modelに渡る正確なcatalog行（descriptionを正規化し上限で切り詰め）、本文サイズ、token推定を一覧表示。agentが受け取るcatalog messageをbyte単位でプレビューし、現在のsessionで公開済みの内容と比較し、frontmatter不正または優先度の高いrootによるshadowingでcatalogに載らないskillも報告します。非表示化はfrontmatterにdisable-model-invocationを書き、ファイルは保持するため`/name`経由では引き続き使用可能。ClawHub、Skills.sh、GitHub URLからのimportとユーザー所有skillのuninstallにも対応。

自分が公開したQQ Zone投稿をローカルライブラリに保存し、思考／夢／レビュー（本・映画の推薦）の3列へ自動分類。各列は個別に手修正でき、1列の取り消しが他の列に影響しない。パスワードロック可能な非公開Webビューア、上流APIが約500文字で切る長文の修復、年・月別語数集計、添付画像のオフライン用バックアップを備える。Cookieはローカルマシン内だけに保存。