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。

精选的 LLM、AI 绘画等领域的教程和资源。Curated tutorials and resources for Large Language Models, AI Painting, and more.

Fengshenbang-LM(封神榜大模型)是IDEA研究院认知计算与自然语言研究中心主导的大模型开源体系，成为中文AIGC和认知智能的基础设施。

总结Prompt&LLM论文，开源数据&模型，AIGC应用

面向 AI 辅助草稿的可读性与自然节奏改进的开源 pipeline 与参考实现。Open-source pipeline and reference implementations for improving the readability and natural cadence of AI-assisted drafts.

Prompt工程师指南，源自英文版，但增加了AIGC的prompt部分，为了降低同学们的学习门槛，翻译更新

AGI资料汇总学习（主要包括LLM和AIGC），持续更新......

OpenSkills：使用任意 LLM 在本地运行 Claude Skills。OpenSkills: Run Claude Skills Locally using any LLM

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.

使用 GPT-3 辅助撰写基金申请书的实验Experiment to use GPT-3 to help write grant proposals.

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.

通用学术写作 Agent Skill：论文润色、中英互译、学位论文与基金申请、审稿回复与投稿材料，适用于各学科；不改数据、不编文献、不夸大结论。支持 Claude Code、Codex、Cursor、Grok Build、OpenCode，复制提示词即可安装。

Tox21 多终点毒性预测：可复现的研究与推理制品（冻结模型、FastAPI 服务、已审计的安全机制）Tox21 multi-endpoint toxicity prediction: a reproducible research and inference artifact (frozen model, FastAPI service, audited security)

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。

一个可复用的 Codex Skill，用于证据对齐、AI 与人工审稿人就绪的学术写作。A reusable Codex skill for evidence-aligned, AI- and human-reviewer-ready academic writing.

面向生产环境的、有内存约束的跨模型 KV-cache 传输，具备受保护的回退机制与可复现研究工具链。Production-oriented, memory-bounded cross-model KV-cache transfer with guarded fallback and reproducible research tooling.

本代码库提供个人研究成果《Deep Learning Approach in Time Series Forecasting: Literature Review and Extension on Feature Extraction》的代码与报告存档。This repository provides code and report archive of individual research: Deep Learning Approach in Time Series Forecasting: Literature Review and Extension on Feature Extraction

精选并经核验的资源合集：面向学术与科学写作的 LLM 研究论文、数据集、工具与实现。A curated, verified collection of research papers, datasets, tools, and implementations on LLMs for academic and scientific writing.

贝叶斯统计研究语料库：面向贝叶斯统计（推断、计算、先验、模型选择、层次模型、非参数、深度贝叶斯、应用）的数据驱动、自动化校验文献综述Bayesian Statistics Research Corpus: Data-driven, auto-validated literature review for Bayesian statistics (inference, computation, priors, model selection, hierarchical, nonparametric, deep Bayesian, applications)

面向几何感知量子机器学习的可复现研究框架。A reproducible research framework for geometry-aware quantum machine learning.

本地优先、隐私至上的 AI 驱动学术写作人性化工具。AI-Powered Academic Writing Humanizer - Local-First, Privacy-Centric Solution

SILVA Networks 是一个 Python 包，提供扩展的深度均衡层、定点求解器、隐式微分、结构化算子、诊断工具以及可复现研究 notebook。SILVA Networks is a Python package for extended deep equilibrium layers, fixed-point solvers, implicit differentiation, structured operators, diagnostics, and reproducible research notebooks.

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.

系统综述《Techniques for Adapting Large Language Models to Low-Resource, Morphologically Rich Languages》的补充材料。Supplementary materials for the systematic review: Techniques for Adapting Large Language Models to Low-Resource, Morphologically Rich Languages

Codex skill，面向交通运输与低空出行领域的学术写作（知识蒸馏自 LT）。Codex skill for transportation and low-altitude mobility academic writing (Knowledge distillation from LT)

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.

RIFT — Race-state Inference From Telemetry。一项可复现研究实现，仅基于位置遥测重建具备事件感知的关联性比赛状态，涵盖路线进度、共享事件标识、检查点通过、物理顺序、前车关系、间距与间隔。RIFT — Race-state Inference From Telemetry. A reproducible research implementation for reconstructing occurrence-aware relational race state from positional telemetry alone, including route progress, shared occurrence identity, checkpoint crossings, physical order, car-ahead relations, gaps and intervals.

目标：将 State Space Models（SSM）/ Mamba 用于医学时间序列分析；总体研究问题：Mamba 模型能否提升基于步态信号检测帕金森病的分类模型性能: To use State Space Models (SSM)/ Mamba for medical time series analysis General research question: Can Mamba models improve the performance of classification models trained to detect Parkinson's disease using gait signals.

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.