仓库/Skill 库

面向全栈开发者的 66 个专业 Skill，将 Claude Code 打造为你的专家级结对编程伙伴。66 Specialized Skills for Full-Stack Developers. Transform Claude Code into your expert pair programmer.

用于构建 AI agent 的企业级 AI 开发框架。它提供多 provider LLM 的统一管理、具备高精度检索的安全企业知识库、可视化工作流编排与多 agent 协作。兼容主流 Agent Skill 标准，使开发者能够高效构建生产级 [原文未完整]。An enterprise AI development framework for building AI agents. It provides unified management of multi-provider LLMs, secure enterprise knowledge bases with high-precision retrieval, visual workflow orchestration and multi-agent coordination. Compatible with mainstream Agent Skill standards, it enables developers to efficiently build production-gra

由蓝图驱动的 AutoResearch 运行时，用于编排 AI 科研工作流，涵盖创意生成、实验、论文写作与同行评审。A blueprint-driven AutoResearch runtime for orchestrating AI research workflows from idea generation and experiments to paper writing and peer review.

One Forge, All Skills: A curated skill collection for academic writing and research. 点开即用，按需配置的一站式学术研究skills平台。

ClaudeKit.cc 的全部强力 skill！All powerful skills of ClaudeKit.cc!

跨技术栈的 Claude Skills，覆盖网站全生命周期：品牌、设计、内容、SEO、开发、运维、增长与研究。支持构建、发布、审计、优化。Stack-agnostic Claude Skills covering the full website lifecycle: brand, design, content, SEO, dev, ops, growth, and research. Build, ship, audit, optimize.

面向学术研究的 Skill 市场：覆盖项目管理、文献综述、绘图制作以及报告与基金申请书撰写。A skill marketplace for academic research: from project management to literature review, making figures and writing reports and grants.

用 Claude Code 完成你的学术工作：选题、文献调研、撰写大纲、正文写作、审校、LaTeX-PDF。面向非技术用户的 Windows 与 macOS 指南。Deine wissenschaftliche Arbeit mit Claude Code: Thema, Recherche, Exposé, Schreiben, Prüfen, LaTeX-PDF. Für Nicht-Techniker, Windows und macOS.

AIWritePaper｜AI学术写作全流程：跨Agent的论文方向路由、文献核验、学术配图、DOCX/PDF与分级验收Skill。

证据优先的 AI 科研平台，支持文献综述、科学写作、配图生成与可发表论文产出Evidence-first AI research platform for literature review, scientific writing, figure generation, and publication-ready papers.

一体化学术研究 Skill 套件：7 个仓库合并为一个路由式 Skill — 186 个 Skill、20 篇合并指南、148 篇工具指南，涵盖论文发现、阅读、文献综述、写作、同行评审、 rebuttal 与发表，以及 12/23 阶段自主研究流水线。All-in-one academic research skill suite: 7 repositories consolidated into one router-style skill — 186 skills, 20 merged guides, 148 tool guides, covering paper discovery, reading, literature review, writing, peer review, rebuttal, and publishing, plus 12/23-stage autonomous research pipelines.

在消费级 GPU 上自动化机器学习模型从论文阅读到实验执行与同行评审的完整研究工作流Automate the full research workflow from paper reading to experiment execution and peer-review for machine learning models on consumer GPUs.

本地优先、由 LLM 驱动的系统文献综述 (SLRs) 平台，以人机协同方式自动完成筛选与提取Local-first, LLM-powered platform for Systematic Literature Reviews (SLRs)—automating screening and extraction with human-in-the-loop precision.

本地优先的量化投资平台，支持可复现研究、证据约束决策、风险控制、论文/影子验证以及人工监督执行Local-first quantitative investing platform for reproducible research, evidence-bound decisions, risk controls, paper/shadow validation, and human-supervised execution.

Claude Code 学术写作框架：通过模式门控的 Agent 工作流，确保全文引用可核验、改写忠于原意、并保持作者风格Claude Code framework for academic writing: verified full-text citations, scope-faithful paraphrase, and voice preservation, enforced by a mode-gated agent workflow

由 LLM 编排的自主 EDA 与数据预处理框架 — 自动生成面向特定数据集的研究问题，编写并自我修正分析代码，自动选择可视化。基于 Claude/GPT 构建。LLM-orchestrated framework for autonomous EDA and data pre-processing — generates dataset-specific research questions, writes and self-corrects analysis code, and auto-selects visualizations. Built with Claude/GPT.

面向研究的 Skill 定义精选集合，覆盖文献综述、证据综合与研究空白分析；汇总多个社区驱动的 Skill，便于在 Codex 或 Copilot 工作流中对比、审阅与按需安装A curated collection of research-focused skill definitions for literature review, evidence synthesis, and research-gap analysis. It brings together multiple community-driven skills so they can be compared, reviewed, and selectively installed for use in Codex or Copilot workflows.

由 AI 驱动的交易研究与市场情报平台，使用智能 AI agent、SQL、RAG、市场数据以及外部工具来分析研究问题并生成结构化的市场洞察。An AI-powered trading research and market intelligence platform that uses an intelligent AI agent, SQL, RAG, market data, and external tools to analyze research questions and generate structured market insights.

Autonomous AI Algorithm Discovery Laboratory：一个有边界、可复现的研究平台，在固定算力预算下提出、实现、测试、拒绝、改进并报告 AI 系统的候选改进方案。Autonomous AI Algorithm Discovery Laboratory: a bounded, reproducible research platform that proposes, implements, tests, rejects, refines, and reports candidate improvements to AI systems under a fixed compute budget.

ResearchOS 是一款 MVP 级自主研究平台：输入研究问题，AI Agent 自动发现来源、验证证据、提取关键信息、交叉核对结论，并产出结构化的最终报告 — 全程实时可视化ResearchOS is an MVP autonomous research platform. Enter a research question; AI agents discover sources, verify evidence, extract key information, cross-check findings, and produce a structured final report — all visualized in real-time.

模块化的学术写作 skills 套件，面向 AI 辅助研究，覆盖摘要、引言、相关工作、方法、实验与结论，具备证据感知推理、引用核查、术语一致性以及 clean/audit 模式。A modular suite of academic writing skills for AI-assisted research, covering Abstract, Introduction, Related Work, Method, Experiments, and Conclusion with evidence-aware reasoning, citation checks, terminology consistency, and clean/audit modes.

证据合成自动化框架——PRISMA 流水线：检索、去重、AI 筛选、Meta 分析（D-L 随机效应）、PRISMA 流程图与森林图。Evidence Synthesis Automation Framework — PRISMA pipeline: search, dedup, AI screening, meta-analysis (D-L random effects), PRISMA/forest plots.

一个端到端的研究智能系统：摄取学术论文并进行语义理解，映射研究领域的关系与演进，探测尚未探索的研究空白，并使用多 Agent AI 流水线生成文献综述与回答研究问题——全部以纯 Python 构建。An end-to-end research intelligence system that ingests academic papers, understands them semantically, maps how research areas relate and evolve, detects unexplored research gaps, and uses a multi-agent AI pipeline to generate literature reviews and answer research questions — all built in pure Python.

面向 Text-to-Motion、Physics RL 与 Pose Estimation 文献综述的自动化多 Agent 研究框架，抓取 ArXiv 论文并去重，通过 LLM 子 Agent 抽取结构化指标，自动下载 PDF 以供 RAG/NotebookLM 摄取。Automated multi-agent research framework for academic literature reviews on Text-to-Motion, Physics RL, and Pose Estimation. Fetches ArXiv papers, deduplicates, extracts structured metrics via LLM sub-agents, and auto-downloads PDFs for RAG/NotebookLM ingestion.

支持 PRISMA、Kitchenham 指南等标准系统综述框架的循证综合方法Evidence synthesis that will support standard systematic review frameworks like PRISMA, Kitchenham guidelines, etc.

从研究问题到论文——全自动化。基于多框架的机器学习研究流水线（CrewAI · AutoGen · LangGraph），支持流式 UI 与人在环控制。From a research question to a paper — automatically. Multi-framework ML research pipeline (CrewAI · AutoGen · LangGraph) with streaming UI and human-in-the-loop control.

多 Agent 研究编排框架，覆盖文献检索、论文分析、证据抽取、跨论文对比、知识综合与经过验证的研究空白发现。A multi-agent research orchestration framework for literature retrieval, paper analysis, evidence extraction, cross-paper comparison, knowledge synthesis, and validated research gap discovery.

系统综述与验证的质量模板——扩展 Consultant 角色原型。Quality template for systematic review and validation - Extends Consultant archetype