プラグイン

One-click launcher for the Web UI: a double-click Start-DSH-Web.cmd on the Windows desktop that boots dsh web and auto-opens the browser (polling until ready), plus a web_launcher model tool (install / open / status).

DSH bundle porting Matt Pocock's 25 promoted engineering and productivity agent skills (grilling, TDD, code review, domain modeling, etc.).

任意の OpenAI 互換 VLM（ローカル Ollama またはクラウド）向けの view_image ツールを備えたチャット画像添付ブリッジ：貼り付け/ドロップされた画像はテキスト専用の DeepSeek モデルに届く前に view_image のパスマーカーになる。

`describe_image` tool: a vision bridge that sends images to mimo-v2.5 through the opencode Zen API (credential `OPENCODE_GO_API_KEY`, free route first with paid fallback) and returns text descriptions for text-only models, with native passthrough and ImageMagick transcoding of SVG/TIFF/HEIC formats.

DashScope の OpenAI 互換 API（qwen3.7-flash）経由のモデル向け image_describe（識図）ツール。テキスト専用セッションでは貼り付けた画像が送信時にファイルパスへ自動変換され、トランスクリプトに再描画されるため画像受理でつまずかない。DASHSCOPE_API_KEY は BYOK、エンドポイント/モデル/予算は設定可能、リダイレクト耐性 HTTP クライアント、全 agent preset で動作。

Auto-discovery vision bridge for text-only DeepSeek Harness agents: automatically finds an image-capable model from your configured providers and returns picture descriptions as plain text via a vision tool.

Vision-augmented DeepSeek adapter: a vision-capable model describes image input, then a text-only DeepSeek model reasons over the description.

On-demand vision for text-only DeepSeek models: upload images, and the model calls a view_image tool backed by any OpenAI-compatible vision endpoint (Qwen/DashScope by default).

Plug-in vision for text-only models on DSH, with native interaction for image understanding and generation, GUI automation, through layered evidence memory and cache.

Synesthesia Encoder for DSH: a vision model translates images into compact structured spatial text (canvas/elements/percentage coordinates), giving text-only LLMs pixel-level image understanding via the `mm_vision` tool.

Reuses DeepSeek web's built-in vision mode for text-only models: the deepseek_vision tool drives the local deepseek-vision-cli browser automation (manual login helper, deep-think enabled, auto-closes browser) and returns image descriptions as text.

Routes chat images to a fixed OpenAI-compatible vision model, returns factual observations to the selected main model, and reuses session-scoped observations across replay, compaction, and restarts.

Ingestion-time tool-output curation: keeps error and warning lines plus head and tail, spills the full text to disk with a pointer the model can read back; covers every text tool, small results pass through untouched.

Jira Cloud issue-tracker integration exposing eight model tools for issue search, project and transition lookup, creation, updates, comments, and transitions; writes are opt-in and support dry-run.

A slim port of Headroom: compresses tool output, cuts up to 20% of context, without affecting the model's context cache or agent performance.

Auto-discovers model listings, pricing and capabilities from OpenAI-compatible API hosts, normalizes them, and emits ready-to-use model configs for dsh.

Stage-gate governance tools: model-callable gate_open / gate_check / gate_list / gate_close for in-memory, per-session acceptance checks with PASS/BLOCK conclusions.

Bi-temporal memory backed by a SodaMem daemon: recall is contributed to the prompt while each turn is assembled and every closed turn is ingested, with no tool call and no model call on the read path; each recalled fact cites the turn it came from and carries validity dates, so superseded facts stop being retrieved.

既存の Claude / ChatGPT 履歴を DSH へ：Claude エクスポート内の memories.json をゼロトークンで即時インポート、ローカルの Claude Code トランスクリプトはエクスポート不要、会話は DSH で設定済みのモデルで蒸留。すべてのメモリはコードでソースと照合された逐語的証拠を伴い、言い換えは破棄してカウント、assistant ターンのみに見つかった証拠は常に保存ではなくレビューへ回す。

Project-scoped persistent key-value memories with set/get/list/delete/search model tools and JSON-file storage.

Model-authored context pruning for DeepSeek Harness through the official compaction API.

Focus board for DeepSeek Harness agents: durable, model-maintained notes in the session workspace that pin the objective, constraints, and decisions across compaction and sessions, with automatic context injection, archive on clear, and an optional web panel.

Message review and quote chips for conversations: multi-model cross review (code/reply) with severity grading and summaries, review history, and host-persisted preferences.

Auto-fills reasoning-effort levels, context windows, output limits and image modality for models from unofficial (custom) providers, with data from models.dev.