Plugins

Trending-topics collector aggregating Baidu / Toutiao / Douyin / Weibo hot lists in one call (Weibo optional cookie), returning titles with heat values for content creators — tool: `sxs_news_hot`.

Connects DSH to local Beav for Xiaohongshu (RED/RedNote) and social-media AI operations: topic research, knowledge, copywriting, image, audio, and short-video workflows with approval and artifact tracking.

Cross-session event-cue memory for DeepSeek Harness: auto-extracts task/conversation cues and user language habits from every turn into a persistent global cue bank, then re-injects remembered context on topic-switch detection (keyword or vector matching).

Floating community panel for the Web UI: 24h hot topics and hot plugins TOP10 with a draggable, always-on-top button.

Rename project conversations into clear, content-based Category｜Topic titles.

A collapsible column beside the conversation that organises conversations into a workspace/topic tree, opens the real sessions in place, and exposes the tree to the assistant through 9 model tools.

Feishu/Lark surface plugin for DeepSeek Harness (dsh): one topic per session, SOUL persona sync, streaming process cards, /status and /doctor.

Connects DeepSeek Harness to Postel over MCP: search a library of X posts that went viral by topic, write posts and X threads in the user's saved voice, and save, schedule or publish them to X, LinkedIn, Bluesky and Mastodon; adds a postel-writing skill. Research works without an API key.

Read-only /btw side questions for DeepSeek Harness: forks the current session, injects the running turn's task, recent tool calls and partial answer so a mid-turn question has context, denies every tool call at execution time, and keeps a per-session topic history with follow-up, stop, retry and delete.

Searches medical and life-science literature by topic, keyword or DOI through Europe PMC, PubMed, OpenAlex and Crossref; decides open-access status through Unpaywall/OpenAlex/Europe PMC; downloads the PDF when an OA route exists (verified by magic bytes and page count) and otherwise writes a decision card with the exact failure reason plus four manual fallback channels: AbleSci assistance, Sci-Hub, SciDown and X-MOL. The bundled Python tool uses only the standard library.

Discover and verify DeepSeek Harness plugins from GitHub topic:dsh-plugin — usage categories, trust score, official peer compatibility; one-click install through the official installer (disabled by default)

Network Engineer Assistant preset plus a 12-vendor, 96-document switch/router/firewall command knowledge base skill (Huawei, H3C, Ruijie, DPtech, Cisco IOS/IOS-XE, Cisco NX-OS, Juniper Junos, Arista EOS, Fortinet FortiOS, ZTE ZXR10, VyOS, MikroTik RouterOS) with KB-first retrieval, cross-vendor command translation, topology and CLI screenshot reading, cited online manual lookup, and mandatory risk annotations for dangerous commands; answers networking topics only. A community continuation of sososilencing/dsh-network-engineer (see NOTICE.md); it cannot be installed alongside that upstream plugin because both register the network-command-kb skill and a 网络工程师助手 preset.

Turns archived sessions into durable memory instead of dead disk. Distils each archive into reusable skills, a fact ledger with ADD/UPDATE/NOOP decisions plus validUntil and supersededBy so stale facts retire instead of piling up, and topic clusters by single-linkage similarity. Also digests sessions that are still in progress through a read-only seq watermark, leaving the session log byte-for-byte untouched, and can self-check the growing memory to merge duplicate facts, retire stale ones and consolidate duplicate skills — measuring only unless you opt in, and never hard-deleting, only superseding.

Layered cross-session memory kept as plain YAML: facts carry a version history instead of being overwritten, a briefing is injected at session start, and a nightly pass consolidates and shards by topic past a threshold. Zero runtime dependencies, so it also runs inside the dsh Android app (verified there).

A polish button in the composer trailing controls that rewrites the draft in place with the model. Context is matched by topic, not by recency: anchors from the draft (paths, identifiers, quoted text, CJK bigrams) pick the earlier messages about the same task, so a session that interleaves several tasks does not leak another task goal or files into the rewrite — and the prompt explicitly lets the model ignore the extract when it does not fit. The result goes back through the same undo-able editor path a human edit uses. Drafts holding reference chips (@file, folder, session) or a claimed /command, and drafts mid-submission, disable the button instead of silently dropping references.

A collapsible column beside the conversation that organises conversations into a workspace/topic tree, opens the real sessions in place, and exposes the tree to the assistant through 9 model tools.

Archives DSH conversation output into SiYuan notes: a single siyuan_archive call files content under `<archive path>/<year-month>/<type>/<date>_<time>_<topic>_<content>` with a metadata table and auto-maintains an index document at the notebook root; 6 siyuan_* host tools (list notebooks, full-text search, read document, archive, delete block, remove document) and a Web settings page for base URL with connection test, API token in the host credential store, archive target and title template, plus notebook/document-level read/write/delete permissions with default deny and child levels able to only tighten.

Scores each assistant answer on five dimensions plus topic coverage, shows evidence, and turns the weakest dimension into copy-ready prompt advice. Observational only; local by default, with optional Jev review.

Three-layer memory for DeepSeek Harness — a small always-on core block plus on-demand topic files (profile, preferences, decisions, lessons), with source-annotated writes and supersede-not-overwrite history, so preferences and project constraints stop drifting between sessions. Local-first: no vector DB, no server, no outbound calls.

Long-term memory on top of mem0 and a local Chroma store: user profile and recalled facts injected through the system prompt, capture at session end, a nightly pass that dedupes, merges paraphrases and resolves contradictions, a persistent topic graph written as Markdown cards, periodic export to a git repository, and a full-page memory UI with search, bulk merge, snapshots and undo. Needs a manual Python venv setup plus LLM and embedding API keys before use.

Topic grouping layered over the native workspace list: emoji folder groups, filtering and search, batch archive, and safe permanent deletion.

DocChain knowledge base, document conversion and knowledge-graph toolkit — filename, content and metadata search, locate-by-doc_id/name with content reading, topic listing, bulk document upload, document-to-Markdown conversion, image-similarity search, per-document image descriptions saved to a sidecar markdown (flowcharts and architecture diagrams are turned into PlantUML sketches, other images into text descriptions; the original image is replaced by a clickable link), a deep-research workflow, and a knowledge-graph layer (ontology inspection, read-only Cypher, ontology/entity/relation writes). Every script-backed capability also ships as a native DSH tool executed in-process: doc search, content search, filename search, topic info, metadata search, upload, image search, image describe, doc2markdown, plus kg schema / kg cypher / kg upsert node / kg upsert relation / kg delete node / kg delete relation. All graph operations go through the DocChain API, so no Neo4j password is needed. The configuration page keeps several docchain environments (each with its own address and credential), switches between them in one click, and pre-selects topics per environment. Needs a docchain service URL and API key (except doc2markdown); image descriptions additionally need an OpenAI-compatible multimodal endpoint (defaults to an internal gateway model, configurable).

Saves opt-in conversations as structured Markdown study notes with topic folders and a maintained index.

Local memory for DeepSeek Harness: what you write is filed under a topic and linked to related entries, recall ranks results by embedding similarity (word matching when no model is installed), and the data stays in a local SQLite database.