Plugin

Takes the user's own .pptx template plus a course outline (lesson plan / document / plain text) and produces a courseware .pptx that adopts the template's background, color scheme, fonts, and page skeleton. Pure plugin package, no built-in API keys; the image backend must be configured by the user.

Digests the images in a prompt with a vision model before admission, so any model — a text-only one included — can read them without the session ever switching models, and adds a describe_image tool for image paths.

Trims historical images in outgoing chat requests down to a recent-image count, learns the provider per-prompt image cap from HTTP 400 responses, and retries with fewer images so image-heavy sessions keep working.

Per-wallpaper offset for dsh-dream-skin - nudge the current wallpaper along X and Y in pixels from Settings, remembered separately for each image, URL or gradient.

Replace the dsh-whale-widget mascot with your own image through a built-in cutout pipeline, plus a session-header restart button, a browser heartbeat, and an opt-in patch self-heal that is off by default.

Drive a local DeepSeek Harness agent from QQ and other IM channels through AstrBot: send tasks with /dsh, continue by quoting or @-mentioning the bot, answer approvals and ask_user_question prompts from chat (in parallel with the DSH web UI), and get results, inline images and file cards back. Ships session / workspace / model / permission commands, /compact and /last recovery, three progress-display modes, and group-shared or per-user session scoping.

Three tools for DeepSeek Harness: typeset LaTeX formulas into images, draw mathematical figures from a declarative spec, and convert a formula or SVG into an image ready to embed in a document.

Configure multiple image providers in Settings and call image_generate with the one selected model; images save under generate/image and show inline in the conversation.

Measures what an llm-pi-ai endpoint really accepts — context window from its own model list, output-token limit and reasoning levels read out of its own range errors, image support from a generated test image — then reports declared values that contradict that evidence and rewrites them only after confirmation.

Cross-session TODO board as a draggable floating panel: tasks are grouped by working directory and reorderable by drag, each one carries a run mode (remind only, continue in the current session, or open a new session in the same directory), up to four attached images sent to the model as real image attachments, and an optional local time it may run at; two checkboxes track AI-done and user-verified, and the agent picks up the next unfinished task in that directory when it finishes one.

Connects DeepSeek Harness to Xiaohe Canvas, letting you create nodes, build flows, and trigger AI image and video generation from chat.

Declare whether a configured third-party model accepts image (multimodal) input; writes the modality into the owning provider settings and verifies it through runtime model resolution.

Web management panel for Docker on DeepSeek Harness: manages Docker MCP servers (deploy/remove, official catalog search/pull, gateway start-stop-restart, custom deploy) and containers/images (list, start/stop/restart, remove, logs, inspect, interactive terminal, create, pull, build, load) across local CLI and SSH remote targets with one-click switching. Credentials are encrypted with Windows DPAPI, HTTP is loopback-only, and command parameters are whitelist-filtered. Build-free pure ESM; client is zero-dependency.

Auto-switch DeepSeek models: routes every request to the vision model when images are present, to the pro model for complex tasks, and to the fast model otherwise; includes a switch_model tool for manual overrides.

Local file upload: paperclip button and drag-and-drop, per-session storage under .dsh-uploads, and a read_document tool that pages text and OCRs images.

Translate text-based PDFs via DeepSeek or OpenAI-compatible APIs while preserving layout, fonts, images and links, with editable output.

DeepSeek Harness (DSH) 会话内图片渲染插件：全局 show_image 工具 + 点击放大 lightbox。 | Inline image rendering plugin for DSH: global show_image tool + click-to-enlarge lightbox.

DeepSeek Harness plugin: a model-facing show_image tool that presents a local image to the human viewer in the conversation.

Bridges WeChat private messages to DSH with two-way text, image, file, and media transfer, aiming to restore the native DSH experience in WeChat.

Vision bridge for text-only DeepSeek models: transcribes attached images with deepseek-v4-flash-vision-exp before they reach deepseek-v4-pro, with no third-party dependencies.

Apple on-device Vision tools for text-only dsh models: local OCR (zh-Hans + 30 langs), image classification, face detection, document layout, and a combined describe — 100% offline, no API key, tall-screenshot slicing.

Model-facing image-generation tool with a configurable channel and normalized image parameters.

Adds image input and recognition through configured DSH providers or an OpenAI-compatible endpoint.

Turns a Xiaohongshu, Douyin, TikTok, YouTube or X link into text: transcript with timecodes, on-screen text, a description and OCR of every image, caption and metadata. Mounts the hosted MCP server over streamable HTTP.