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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.).

모든 OpenAI 호환 VLM(로컬 Ollama 또는 클라우드)용 view_image 도구를 갖춘 채팅 이미지 첨부 브리지: 붙여넣거나 드롭한 이미지가 텍스트 전용 DeepSeek 모델에 도달하기 전에 view_image 경로 마커가 됩니다.

`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 OpenAI 호환 API(qwen3.7-flash) 기반의 모델용 image_describe(识图) 도구와 붙여넣기 브리지: 텍스트 전용 세션에서 붙여넣은 이미지가 전송 시 파일 경로로 자동 변환되고 트랜스크립트에 다시 렌더링되어 이미지 허용 단계에서 걸리지 않습니다. DASHSCOPE_API_KEY는 BYOK, 엔드포인트/모델/예산 설정 가능, 리다이렉트 방지 HTTP 클라이언트, 모든 agent preset에서 동작.

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.

기존 Claude / ChatGPT 기록을 DSH로 가져오기: Claude 내보내기의 memories.json을 제로 토큰으로 즉시 가져오고, 로컬 Claude Code 트랜스크립트는 내보내기 없이 사용하며, 대화는 DSH에 이미 설정된 모델로 증류합니다. 모든 메모리는 코드로 소스와 대조된 축자적 증거를 동반하며, 의역은 폐기 후 집계되고 assistant 턴에서만 발견된 증거는 항상 저장이 아닌 검토로 보냅니다.

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.