仓库/Skill 库

用于演示可复现研究的 demo 项目a demo project for demonstrating reproducible research

一款面向学生的友好界面应用，用于介绍 Diana Ridley（2012）的文献综述方法（SQ3R），并融入适用于社会科学学生的 SPIDER 方法，辅助学生主动完成文献标注与综述写作This app is a student friendly interface to introduce Diana Ridley's (2012) method of literature review (SQ3R) and incorporating 'SPIDER' method for students of Social Science. This is an assistive application for students to actively annotate and write review of literature.

一个 Claude Managed Agent，可回答 SlideRule 研究问题并返回独立的 HTML 仪表板；每个用户需在其自己的 Anthropic 账号中部署专属 AgentA Claude Managed Agent that answers SlideRule research questions and returns a self-contained HTML dashboard. Each user provisions their own agent in their own Anthropic account.

研究问题：在脓毒症发生前检测患者 — 使用分类算法实现脓毒症的早期检测Research Question: Detect Sepsis in Patients Before it Emerges - Early Detection of Sepsis using Classification algorithms

AI 驱动的学术研究助手，使用 RAG 与 LLM 进行论文分析、生成文献综述、识别研究空白、产出深度解读，并生成带来源引用的演示文稿。AI-powered academic research assistant that uses RAG and LLMs to analyze research papers, generate literature reviews, identify research gaps, create deep explanations, and generate presentations with source citations.

使用工具研究问题并生成答案的 AI AgentAn AI agent that uses tools to research questions and generate answers.

通用深度研究 Agent 团队：13 Agent 流水线，提供包含系统综述与 Meta 分析在内的 8 种模式。Universal deep research agent team: 13-agent pipeline, 8 modes incl. systematic review and meta-analysis

实习项目：针对癌症治疗前营养的系统综述、证据综合、证据图谱以及面向患者与临床工作者的资源开发Placement project: systematic review, evidence synthesis, evidence mapping, and development of patient and clinician resources on pre-treatment cancer nutrition.

论文一：NL2SQL 歧义检测——M1.5 分类法、确定性基线、人工金标评估与可复现研究制品Paper 1: NL2SQL Ambiguity Detection — M1.5 taxonomy, deterministic baseline, human-gold evaluation, reproducible research artifact

自主研究 Agent：搜索本地文档、互联网及其内置知识，回答复杂研究问题An autonomous research agent that searches your documents, the web, and its own knowledge to answer complex research questions.

本仓库记录了我通过对研究论文的结构化分析、文献综述以及对基因组学、转录组学、生物信息学和 AI 在生命科学中应用的笔记，对计算生物学的持续探索。This repository documents my ongoing exploration of computational biology through structured analyses of research papers, literature reviews, and notes on genomics, transcriptomics, bioinformatics, and AI applications in life sciences.

基于 CrewAI、PostgreSQL 与科学 API 集成的多 Agent PRISMA 系统综述编排器Multi-agent PRISMA systematic review orchestrator with CrewAI, PostgreSQL, and scientific API integrations

新西兰 PIC Party 项目，涉及为期 59 天的政党注册议题、独立选区候选人、基于良知的政府组建以及正在浮现的生活成本政策。本站呈现提案、研究问题、政治讽刺与政策建模；非选举委员会建议，不代表官方选举。PIC Party New Zealand project, the 59-day party-registration issue, independent electorate candidates, conscience-based government formation and emerging cost-of-living policy. This site presents proposals, research questions, political satire and policy modelling. It is not Electoral Commission advice and does not represent an official election.

一个生产级 RAG 系统，能自主决定检索方式——从本地向量库、Web 搜索或两者结合，以引用、多源综合的方式回答研究问题A production-grade RAG system that autonomously decides how to retrieve information — from a local vector store, web search, or both to answer research questions with cited, multi-source synthesis.

独立的自主证据综合与系统综述工作台Standalone Autonomous Evidence Synthesis & Systematic Review Workbench

展示学术写作与技术传播成果的专业数字作品集。Professional digital portfolio showcasing my academic writing and technical communication work.

接收研究问题 → 检索网页 → 阅读关键页面 → 综合输出带引文与置信度分数的结构化报告。Takes a research question → searches the web → reads key pages → synthesizes a structured report with citations and confidence scores

完整的 S&P 500 质量评分项目：数据采集与剖析、修正板块/子行业错配、清洗异常值与缺失值、将 5 项指标归一化为综合得分、回答全部 4 个研究问题，并搭建交互式 Power BI 仪表盘Complete S&P 500 Quality Score project: sourced and profiled data, fixed the sector/sub-industry mismatch, cleaned outliers and missing values, normalized 5 metrics into a composite score, answered all 4 research questions, and built an interactive Power BI dashboard.

可复现研究流程，通过改造预训练 LLM tokenizer 降低尼泊尔天城文分词成本：连续/字素感知的 BPE 候选挖掘、基于边际增益的贪心词表选择、保留 ID 的 merge 拼接、基于分解的 embedding 初始化，以及 embedding-only/LoRA/CPT 适配；面向 6GB 笔记本 GPU 调优。Reproducible research pipeline that reduces Nepali Devanagari tokenization tax by retrofitting a pretrained LLM tokenizer: continued/grapheme-aware BPE candidate mining, greedy marginal-gain vocabulary selection, ID-preserving merge splicing, decomposition-based embedding init, and embedding-only/LoRA/CPT adaptation. Tuned for a 6GB laptop GPU.

一款连接 AI 助手与 Sci-Hub 的 MCP 服务器，用于自动化学术研究。专为 RAG 设计，可绕过付费墙、处理验证码、通过 OCR 提取文本，并作为循证推理的主要数据获取器An MCP server that bridges AI assistants with Sci-Hub for automated academic research. Designed for Retrieval-Augmented Generation (RAG), it bypasses paywalls, handles captchas, extracts text via OCR, and serves as the primary data-fetcher for evidence-based reasoning.

应用 AI、可复现研究与可靠工程。Applied AI, reproducible research, and reliable engineering.

基于 CrewAI 的 Agent 系统，用于辅助研究者围绕特定高层级研究问题定义研究项目与假设。This is a CrewAI agent system set up to assist a researcher with defining research projects and hypotheses for a specific high-level research question.

面向人脸域去风格化、结构条件化、评估与质量过滤的紧凑型可复现研究流水线。Compact reproducible research pipeline for face-domain destylization, structural conditioning, evaluation, and quality filtering.

多 Agent AI 研究助手——由多个专门化 Agent 协作，自动检索论文、总结发现、对比方法、生成文献综述并产出可演示的内容Multi-Agent AI Research Assistant — An AI-powered research automation system where multiple specialized agents collaborate to search research papers, summarize findings, compare methodologies, generate literature reviews, and create presentation-ready content.

使用多 Agent RAG 系统与混合索引、LangGraph 编排，自动获取 arXiv 研究论文并生成文献综述。Automate arXiv research paper retrieval and literature review generation using a multi-agent RAG system with hybrid indexing and LangGraph orchestration.

从学术数据库中采集学术元数据，用于组织文献检索与构建研究参考目录。Harvest scholarly metadata from academic databases to organize literature searches and build research bibliographies.

基于 PRISMA 2020 的 LLM 微调技术系统综述（2020–2025）— 84 项研究，涵盖 LoRA/QLoRA/RLHF/DPO，本科毕业论文（PUCE）。PRISMA 2020 systematic review of LLM fine-tuning techniques (2020-2025) - 84 studies, LoRA/QLoRA/RLHF/DPO, undergraduate thesis (PUCE).

基于 LangGraph、LangChain、Groq 和 Tavily 构建的多步骤研究 Agent。分解复杂的研究问题，执行迭代式网络研究，评估证据，并生成结构化的最终报告。Multi-Step Research Agent built with LangGraph, LangChain, Groq, and Tavily. Decomposes complex research questions, performs iterative web research, evaluates evidence, and generates a structured final report.

项目名称：用于自动化文献综述与研究空白发现的自适应 AI 研究智能平台。Project Title: Adaptive AI Research Intelligence Platform for Automated Literature Review and Research Gap Discovery

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面向中英双语生物医学研究的术语循证、主张强度与科学写作审计 skill。Evidence-aligned terminology, claim-strength, and scientific-writing audit skill for Chinese and English biomedical research.

培育持久化数字生命的开放协议：协议、课程、研究问题、治理An open protocol for raising persistent digital beings: protocol, curriculum, research questions, governance

关于 AI Agent 系统与编排的定向研究与文献综述的最终交付成果Final deliverables for directed studies and literature review on AI Agent Systems and Orchestration