Plugins

给 AI Agent 的中文互联网研究 Skill：检索 8 大平台最近 30 天的讨论，追踪跨平台热点，生成附原始来源的报告，并提供每日全网热榜。支持 Claude Code、Codex、OpenClaw。

Unified search with evidence verification for agents: 120+ engines with multilingual routing, vertical domains and structured modality cards, 10 MCP tools plus wide_research orchestration, and a fetch-verify loop that backfills evidence scores.

Mounts 88 packaged research, social-intelligence, marketing and business Agent Skills into dsh through the filesystem Skill provider.

Adaptive deep-research orchestrator built on the official workflow engine.

Ported from the ARS Claude Code plugin (44k+ stars on GitHub): academic research skills for DeepSeek Harness — a deep-research agent team, a paper-writing pipeline, a multi-perspective peer-review panel, and an end-to-end orchestrator, plus sixteen /ars-* commands.

Adaptive deep-research orchestrator (workflow engine).

Multi-platform research and deep search pipeline: harvest_scout for parallel discovery across GitHub, Web, Twitter, Reddit, Xiaohongshu, Bilibili, and YouTube, harvest_deep_research for automated comprehensive research reports, plus extract, cross-source verification, and credibility auditing.

Chinese industry and technology research report skill with an offline DOCX build pipeline (build_docx.py). Enforces a 9-chapter structure with a plain-language timeline (Section 2.1), 5-dimensional technical bottleneck criteria, and Make-vs-Buy cost/compensation trade-offs, verified by check_threads.py and compiled to .docx with a 3-level auto TOC. Requires Python 3.9+ with lxml.

PubMed deep-research toolset: literature search, author investigation, same-name disambiguation, institution statistics and mentor-student matching.

Multi-round deep research in the DSH sidebar: each round fans sub-questions out to parallel sub-agents, then continues from where the last round stopped and writes a cited report.

Biomedical research plugin for DeepSeek Harness (dsh): biomcp MCP server tools, scientific plotting skills, and deep-research subagent

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).