仓库/Skill 库

AI 驱动的文献综述工具利用机器学习加速并加强识别、分析与综合相关研究的学术流程。AI-powered literature review tools leverage machine learning to expedite and enhance the scholarly process of identifying, analyzing, and synthesizing relevant research.

面向法律与重型文档工作流的 AI-native IDE 工作空间：文件、Agent、插件、WPS 编辑、OCR、证据链。律师的 VS Code。AI-native IDE workspace for legal and document-heavy workflows: files, agents, plugins, WPS editing, OCR, evidence chains. VS Code for lawyers.

DeepSeek Harness 换肤 / 壁纸 / 主题包插件 (dsh-plugin) — 8 套 Mirage 主题、每用户强调色、壁纸2.0、主题包导入导出/分享链接、收藏与随机，纯原生 token 系统实现。

Zotero 9 的 AI 插件研究助手：与文献库对话、运行联邦学术搜索、RAG、OCR、系统综述，管理云存储，含独立 MCP、Agent 能力及 skills 库。Zotero AI plugin Research assistant for Zotero 9. Chat with your library, run federated scholarly search, RAG, OCR, systematic reviews, and manage cloud storage. Includes standalone MCP, Agentic capabilities, and skills library.

Promptfoo 的 GitHub Action。可用于测试 prompt、Agent 与 RAG；支持 LLM 的 AI 红队对抗、渗透测试与漏洞扫描；对比 GPT、Claude、Gemini、Llama 等模型的性能；通过简洁的声明式配置集成命令行与 CI/CD。The GitHub Action for Promptfoo. Test your prompts, agents, and RAGs. AI Red teaming, pentesting, and vulnerability scanning for LLMs. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and CI/CD integration.

基于对象存储的快速搜索引擎，原生支持 Parquet 上的全文检索、向量检索与 SQL。Fast search engine on object storage, with full text search, vectors, and SQL, natively on Parquet.

RuvNet Brain——基于 Reuven Cohen（rUv）的 RuvNet 技术栈（RuVector/RVF、Ruflo、AgentDB、RuLake、SPARC 及 21 个构建块）为 Claude Code 提供一个可下载、源码溯源的"大脑"。通过单一 MCP 工具（search_ruvnet）让 Claude 立足真实源码，而不是脱离技术栈空想。RuvNet Brain — a downloadable, source-grounded brain for Claude Code over Reuven Cohen's (rUv's) RuvNet stack: RuVector/RVF, Ruflo, AgentDB, RuLake, SPARC + 21 building blocks. Grounds Claude in real source via one MCP tool (search_ruvnet), so it builds with the stack instead of drifting off it.

一个专为辅助研究活动而精选的 AI 工具列表，涵盖文献综述、引文管理、数据分析等工具，面向希望借助 AI 提升研究工作流的研究人员、学生和从业者。A curated list of AI tools specifically designed to assist in research activities, including tools for literature reviews, citation management, data analysis, and more. This repository is intended for researchers, students, and professionals who want to leverage AI to enhance their research workflows.

视觉定位的文献综述Literature review of visual localization.

📚🔭 你的个人研究雷达 — 由 LLM 驱动的工具，可跨 arXiv / Crossref / Semantic Scholar / GitHub / RSS 自动聚合你关键词的最新论文。📚🔭 Your personal research radar — an LLM-powered tool that auto-aggregates the latest papers for your keywords across arXiv / Crossref / Semantic Scholar / GitHub / RSS.

ICLR 2026 Oral 论文 "Q-RAG: Long Context Multi-Step Retrieval via Value-Based Embedder Training" 的官方仓库。Official repository for the ICLR 2026 Oral Paper🔥 “Q-RAG: Long Context Multi-Step Retrieval via Value-Based Embedder Training”

面向编程 Agent 的更快运行时。平均提速 25%、降本 30%，同时质量持平或更优。同等任务、同等质量，更快更便宜。Faster runtime for coding agents. Make coding agents 25% faster and 30% cheaper on average while keeping the quality same or more. Same Task, Same Quality, Faster and Cheaper.

双模式 AI 研究工作空间：DeepSearch 在约 2 分钟内将查询转化为带引用的网络报告；Research Mode 运行 25 个 Agent 的流水线，撰写完整学术论文（含 PRISMA 流程图、经核验的引用，导出 PDF/DOCX）；设有 4 个人机协同检查点；基于 LangGraph、FastAPI 与任意 OpenAI 兼容 LLM 构建AI research workspace with two modes — DeepSearch turns a query into a cited web report in ~2 min; Research Mode runs a 25-agent pipeline that writes a full academic paper (PRISMA diagram, verified citations, PDF/DOCX export). 4 human-in-the-loop checkpoints. Built with LangGraph, FastAPI, and any OpenAI-compatible LLM.

生产级多 Agent 编排引擎。从可插拔的 Agent、LLM、工具与 retriever 目录组合 Agentic 工作流；本地使用 LangGraph 执行，或通过 Temporal 分布式执行。内置 RAG 管道用于企业知识检索，支持 A2A 与 MCP 协议，提供可视化拖拽蓝图构建器。Production-grade multi-agent orchestration engine. Compose agentic workflows from a pluggable catalog of Agents, LLMs, tools, and retrievers. Execute locally with LangGraph or distributed with Temporal. Built-in RAG pipeline for enterprise knowledge retrieval. A2A and MCP protocol support. Visual drag-and-drop blueprint builder.

智能多 agent 对话助手，采用 LangGraph 编排，支持 Human-in-the-Loop、企业级可观测性与完整 i18n 支持（6 种语言）。Smart multi-agent conversational assistant with LangGraph orchestration, Human-in-the-Loop, enterprise-grade observability, and full i18n support (6 languages)

SPY put-credit 验证与真金白银的 control plane，具备券商账本支持、确定性 risk gates、hybrid RAG、对账及每月 $1k 税后收益证据追踪。SPY put-credit validation and real-money control plane with broker-backed ledgers, deterministic risk gates, hybrid RAG, reconciliation, and $1k/mo after-tax evidence tracking.

📚 AI 驱动的论文阅读工作台。基于项目的论文问答，集成 Hybrid RAG、多 Agent 工作流（ReAct/Plan-Act/RePlan）、长期记忆与可溯源证据。使用 LangChain + LangGraph + Streamlit 构建。📚 AI-powered research reading workbench. Project-based paper Q&A with Hybrid RAG, multi-agent workflows (ReAct/Plan-Act/RePlan), long-term memory, and traceable evidence. Built with LangChain + LangGraph + Streamlit.

面向 RAG 与 Agent 流水线的快速、确定性文档解析器与切分器，内置 MCP server，便于 Agent 精确导航长文档。Fast, deterministic document parser and chunker for RAG and agent pipelines. Ships an MCP server for agents navigating long documents with precision.

可自托管的 Agentic OS：通过签名插件构建、运行与审计多 Agent AI 团队。自带 LLM key，数据完全自有，符合 EU/GDPR 要求。Self-hostable agentic OS. Build, run & audit multi-agent AI teams from signed plugins. Bring your own LLM key, own all your data, EU/GDPR-ready.

生产级多 Agent 平台，提供编排、会话治理和审计能力。技术栈：Go/Python/Rust 微服务、多类型数据存储、Next.js 管理后台 UI、自动化 CI/CD 流水线、通过 PR 评论提及触发的 AI 代码审查。Production-ready multi-agent platform delivering orchestration, session governance and audit capabilities. Stack: Go/Python/Rust microservices, multi-type data storage, Next.js admin UI, automated CI/CD pipelines, AI code review triggered via PR comment mentions.

自动更新的开源知识库，面向 AI Agent、RAG 系统、MCP server、prompt、tool、模板以及新一代 Web 开发。An auto-updating open-source vault for AI agents, RAG systems, MCP servers, prompts, tools, templates, and next-generation web development.

可复现研究仓库的 GitHub 模板 —— 两层 Python 3.10+/uv monorepo，将通用基础设施与项目级代码分离，配有 pytest 覆盖率门禁（基础设施 60%、项目 90%）、16 阶段构建流水线、通过 pandoc/LaTeX 实现的 Markdown 转 PDF 渲染，以及 25 个标准示例。GitHub template for reproducible research repos — two-layer Python 3.10+/uv monorepo splitting generic infrastructure from per-project code, with pytest coverage gates (60% infra, 90% projects), a 16-stage build pipeline, markdown-to-PDF rendering via pandoc/LaTeX, and 25 canonical exemplars.

EnterpriseRAG-AI：面向 AI Agent 工作负载的 Linux 原生、eBPF 驱动的安全与治理网格。EnterpriseRAG-AI: The Linux-Native, eBPF-Powered Security & Governance Mesh for AI Agent Workloads

面向 AI 编程 agent 的无向量代码检索——基于 Tree-sitter 图，按业务意图（@intent、@domainRule）搜索代码，通过 MCP 为 MSA 构建。Vectorless code retrieval for AI coding agents — search code by business intent (@intent, @domainRule) over a Tree-sitter graph. Built for MSA via MCP.

隐私优先、AI 原生的 Agent，面向电商及其他场景，基于 LangGraph workflow 以低代码/无代码方式图形化创建 skill、任务和 agent。支持跨网络部署 agent。Privacy-first, AI native agents For E-Commerce (and beyond), create skill (langgraph based workflow), tasks, agents, graphically with low code or no code. Deploy agents across network.

🎵 专属你的私人数字调音师|AI 音乐搜索推荐 Agent | 基于大模型 + 知识图谱 + 双模型声学向量的本地智能音乐推荐系统 | LLM-powered Music Recommendation Agent with Hybrid RAG, Neo4j, and Long-term Memory

面向 AI agent 的基于证据的评估——将每条断言与 agent 真实工具输出进行核对（受约束、基于证据的模型判断，而非整体式 LLM 评判的猜测），并附带置信区间。Evidence-grounded evaluation for AI agents — verifies each claim against the agent's real tool outputs (constrained, evidence-grounded model judgment, not holistic LLM-judge guesswork), with confidence intervals.

八平台全栈 AI 操作系统——通过 LangGraph + MCP + A2A 统一调度 176 个 LLM；340 表多租户 RLS、RAG 知识库、agent 市场，覆盖 Web/API/CLI/桌面/扩展/移动/小程序，Apache 2.0。Eight-platform full-stack AI operating system - unifies 176 LLMs via LangGraph + MCP + A2A. Multi-tenant RLS over 340 tables, RAG knowledge base, agent marketplace. Web/API/CLI/Desktop/Extension/Mobile/Miniapp. Apache 2.0.

🤖 面向智能体文献综述的最强开源 AI agent 工具精选清单 — 覆盖 11 个类别共 70+ 工具（Claude Code skill、深度研究、MCP server、PDF 解析、系统综述）。🤖 The strongest curated list of open-source AI-agent tools for literature review — 70+ tools across 11 categories (Claude Code skills, deep research, MCP servers, PDF parsing, systematic review). 面向智能体文献综述的最强工具大全。

开源 AI 虚拟财务部门 —— 与数据对话：NL2SQL 查询、财务建模（DCF/WACC/LBO）、RAG 文档问答、多 Agent 辩论、报告、预算、资金管理与税务。FastAPI + LangGraph + React。Open-source AI virtual finance department — talk to your data: NL2SQL querying, financial modeling (DCF/WACC/LBO), RAG document Q&A, multi-agent debate, reporting, budgeting, treasury & tax. FastAPI + LangGraph + React.

RAG 管道的回归测试与配置扫描，附带统计学指标以判断变更是否真正带来改进。Regression testing and configuration sweeps for RAG pipelines, with the statistics to know whether a change actually helped.

面向 AI Agent 的 Markdown 优先的长期记忆基础设施。通过 MCP 在 markdown/代码文件上实现 BM25 + 语义混合检索。Markdown-first, long-term memory infrastructure for AI agents. Hybrid BM25 + semantic search across markdown/code files via MCP.

SharpAI 是基于 llama.cpp（通过 LlamaSharp）构建的可嵌入 Embedding、补全与模型管理平台，内置 Ollama 兼容的 Web 服务。SharpAI is an embeddable embeddings, completions, and model management platform using llama.cpp via LlamaSharp, with a built-in Ollama-compatible webserver.

运行 Ollama 本地 LLM 服务的 Docker 镜像。默认安全，所有 API 请求需 Bearer token（首次启动时自动生成）。OpenAI 兼容 API。支持首次启动模型预拉取、NVIDIA GPU (CUDA) 加速和持久化模型存储。多架构：amd64、arm64。Docker image to run an Ollama local LLM server. Secure by default, all API requests require a Bearer token (auto-generated on first start). OpenAI-compatible API. Supports first-start model pre-pull, NVIDIA GPU (CUDA) acceleration, and persistent model storage. Multi-arch: amd64, arm64.

面向 Node.js 与 TypeScript 的 AI firewall。阻止 prompt injection、音频幻觉与 RAG 数据爬取。零依赖。MIT 许可。AI firewall for Node.js & TypeScript. Stop prompt injection, audio hallucinations, and RAG data scraping. Zero dependencies. MIT

自托管 MCP 服务器，让你的 Obsidian/markdown 知识库可被任意 MCP 客户端检索——涵盖文本、PDF、Office 文档、图片与音频。基于类型化、可治理语料库的混合检索，5 万条笔记下仍可达亚秒级响应；文件保持纯 markdown。Self-hosted MCP server that makes your Obsidian/markdown vault searchable — text, PDFs, Office docs, images, audio — from any MCP client. Hybrid retrieval over a typed, governed corpus, sub-second at 50k notes; your files stay plain markdown.