Plugins

Agent Context Editor is a cross-agent plugin for manually excluding, editing, and selectively compressing AI conversation context. It also supports searching, filtering, selecting, hiding, restoring, and undoing conversation changes while preserving the original Session.

AI4Scholar academic search: 38 tools over Semantic Scholar, PubMed, Google Scholar, arXiv, bioRxiv/medRxiv, and DOI resolution (search, citation graph, authors, recommendations, PDF full text in slices, auto-cite, scientific figures), a unified cross-platform search with de-duplication, an API-key card in Settings, and per-call credit accounting with an `/ai4scholar` balance card.

A Live2D whale-girl companion for the DeepSeek Harness Web UI: the model stays on screen and reacts to what the agent is actually doing (thinking, tool calls, streaming text, errors, turn completion); click her to send the agent a message and watch the reply stream into a speech bubble; right-click for a menu of expressions, dress-up items, desk scenes and one-shot animations, every action mapped from the original model author hotkey sheet so nothing overlaps and everything expires on its own. Non-commercial model assets (CC BY-NC-SA 4.0), MIT code, by Andersen216 (https://github.com/Andersen216).

Local literature knowledge-base RAG: 10 tools (kb_ingest, kb_search, kb_rag, kb_zotero, kb_dedup, kb_clear, kb_fetch, kb_scope, kb_stats, kb_status), hybrid BM25+vector+reranker search, cited QA with clickable DOI links, all-local bge embeddings over a single-file SQLite index. Needs Python 3.9+ with pymupdf, faiss-cpu and sentence-transformers; the `npx dsh-kb-rag-install` installer sets those up and pre-downloads the model.

Session-based task board in the sidebar: drag sessions into todo/doing/review/done columns, create sessions with model and reasoning-effort selection, trash with restore, notes, export/import.

Learning Mode agent preset: a coding agent that teaches while coding — concrete scenario-grounded explanations, Socratic guidance, and TODO(你) practice blanks, modeled on Claude Code's Learning output style; installable via dsh plugin add (dsh-learning-mode on npm).

Learning Mode agent preset: a coding agent that teaches while coding — concrete scenario-grounded explanations, Socratic guidance, and TODO(你) practice blanks, modeled on Claude Code's Learning output style; installable via dsh plugin add (dsh-learning-mode on npm).

Writes the harness agent live state (thinking, working, waiting, done, error) to a state file that a bundled Electron Live2D desktop pet polls, so the pet bubble and face follow what the agent is doing.

Citation tools for agents: exact DOI lookup, Crossref bibliographic search, GB/T 7714 / APA / MLA / Chicago formatting, BibTeX generation, bounded-concurrency DOI validation and health self-check.

macOS desk pet forked from anneheartrecord/dsh-desk-pet, in a real always-on-top window that covers fullscreen Spaces: the pose follows whatever the local DSH is doing across six states, the right-click menu is a real AppKit one carrying quiet mode and a session list, and seven skins ship with it. Two of the seven are drawn from geometry by a script in the repo rather than generated, so making a skin needs no image model and no API credits; the bundled skill still expands one photo into all eighteen poses on your own image tool and credentials. There is no Electron and nothing to install alongside it: the window is driven through ctypes on the Python that ships with macOS.

Ships a skill about handing work to subagents instead of doing it inline - which tasks are worth fanning out, how many to run at once, how to send them in parallel, and how to check what they returned instead of trusting the summary.

Voice for the DeepSeek Harness: speak to the agent, hear it back, and see what it is doing from a floating window that stays on top of whatever you are running.