仓库/Skill 库

为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。

Google Deep Search 的实现，支持 1000+ 篇参考文献、本地推理、使用 RAPTOR 与抓取会话对话以及报告生成。An implementation of Google Deep Search with support for 1000+ references, local inference, chatting with your scraping session using RAPTOR, and report generation.

面向 Claude Code 的学术研究工作流：包含 20 个 skill，覆盖因果推断（DiD、RDD、IV、合成控制、田野实验、设计分流、预注册）、论文阅读、文献综述、参考文献审计、复现包、LaTeX 与 TikZ，并附带带渲染时质量门禁的 Quarto reveal.js 幻灯片系统。Academic research workflow for Claude Code: 20 skills covering causal inference (DiD, RDD, IV, synthetic control, field experiments, design triage, preregistration), paper reading, lit review, bib auditing, replication packages, LaTeX and TikZ, plus a Quarto reveal.js slide system with render-time quality gates.

将 Zotero 阅读器标注评论渲染为 Markdown 和 LaTeX 格式，同时保留存储的原始文本。Render Zotero reader annotation comments as Markdown and LaTex while preserving stored text.

THIS PAPER IS FOR ENTERTAINMENT PURPOSES ONLY.この小説はフィクションであり、実在する団体または個人とは関係ありません。

面向科学与医学写作的人性化润色的阿英双语 skill，同时保持学术准确性。Bilingual Arabic-English skill for humanizing scientific and medical writing while preserving scholarly accuracy.

语言 case 的范畴论处理，集成 Active Inference 与 CEREBRUM 架构：DisCoPy 弦图横跨类型学、范畴语法、拓扑斯理论及量子扩展，生成 30 个发表级图表与一篇 24 节的手稿。1,207 个测试，覆盖率 95.96%，零 mockCategory-theoretic treatment of linguistic case integrated with Active Inference and the CEREBRUM architecture: DisCoPy string diagrams spanning typology, categorial grammar, topos theory, and quantum extensions, generating 30 publication figures and a 24-section manuscript. 1,207 tests, 95.96% coverage, zero mocks.

基于仿真的 MRI 调度策略分析，结合统计分析与离散事件仿真，以优化资源利用率、等待时间、加班时长与患者吞吐量。Simulation-based analysis of MRI scheduling policies, combining statistical analysis and discrete-event simulation to optimize resource utilization, waiting times, overtime, and patient throughput.

基于 Torch2PC 的预测编码硕士论文可复现项目：在 Ubuntu/ROCm 上对 backpropagation 进行逐层与 compute-matched 对比，实现 PC-CATM/PC-TREF，对 state inference 进行机制诊断，并构建 QWake-PC 以实现自适应 exact inference —— 面向机制可解释的可复现预测编码研究。Воспроизводимый проект магистерской диссертации по predictive coding в Torch2PC: послойное и compute-matched сравнение с backpropagation, PC-CATM/PC-TREF, механизмная диагностика state inference и QWake-PC для адаптивного exact inference в Ubuntu/ROCm — reproducible research on mechanism-aware predictive coding.

SLM-PICO-Screener 是用于自动化系统综述筛选的轻量 NLP 流水线，基于 Phi-3 Mini 配合 LoRA 与 4-bit 量化，将文章分类为 8 个 PICO 标签。体积约 90 MB，可在消费级 GPU/CPU 上运行，加速生物医学研究中的证据综合。SLM-PICO-Screener is a lightweight NLP pipeline automating systematic review screening. Built on Phi-3 Mini with LoRA & 4-bit quantization, it classifies articles into 8 PICO labels. At ~90 MB, it runs on consumer GPUs/CPU, accelerating evidence synthesis in biomedical research.