仓库/Skill 库

经过最佳基准测试的开源 AI 记忆系统，而且是免费的。The best-benchmarked open-source AI memory system. And it's free.

找到在你的硬件上真正能跑且性能最优的本地 LLM。排名基于真实且时新的基准测试，而非参数量。一条命令，即刻运行。Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly.

大型多模态模型 LMM 的开源评估工具包，支持 220+ LMM 与 80+ 基准测试。Open-source evaluation toolkit of large multi-modality models (LMMs), support 220+ LMMs, 80+ benchmarks

一个精简且可定制的高效大模型（LLM、VLM、AIGC）评估与性能基准测试框架。A streamlined and customizable framework for efficient large model (LLM, VLM, AIGC) evaluation and performance benchmarking.

面向治理、风险与合规（GRC）的 Claude Skills：针对 ISO 27001、SOC 2、FedRAMP、GDPR、HIPAA、NIST CSF、PCI DSS、EU AI Act、ISO 42001、ISO 27701、DORA、CSRD、印度 DPDPA、CMMC 2.0、NIST AI Risk、SWIFT、澳大利亚 ISM、EU NIS2、CCPA/CPRA 等的专家级合规指导。使用 skills 基准 97%，不使用 81%。Claude Skills for Governance, Risk, & Compliance (GRC): Expert-level compliance guidance for ISO 27001, SOC 2, FedRAMP, GDPR, HIPAA, NIST CSF, PCI DSS, EU AI Act, ISO 42001, ISO 27701, DORA, CSRD, India's DPDPA, CMMC 2.0, NIST AI Risk, SWIFT, Australia's ISM, EU NIS2, CCPA/CPRA, and others. Benchmark 97% (with skills) vs 81% (without skills).

一个 LLM 相关论文、学位论文、工具、数据集、课程与基准的合集。A collection of LLM related papers, thesis, tools, datasets, courses, benchmarks

论文 LitLLMs, LLMs for Literature Review: Are we there yet?（TMLR 2025）的代码仓库。Code for LitLLMs, LLMs for Literature Review: Are we there yet? (TMLR 2025)

面向长时对话记忆层的综合基准测试框架A Comprehensive Benchmarking Framework for Long-Term Conversational Memory Layers

实时更新的向量数据库项目、集成和基准评测全景图——每……刷新。Live-updating landscape of vector database projects, integrations, and benchmarks — refreshed every

硕士论文（UiB，计算语言学，2023）及扩展工作。针对系统综述筛选的文本分类。原始 11 工作流分析结合 NEO ontology 集成，以及 Cohen 等人（2006）药物类别基准的扩展，比较 BoW 与 BiomedBERT。Master's thesis (UiB, Computational Linguistics, 2023) and extension work. Text classification for systematic review screening. Original 11-workflow analysis with NEO ontology integration plus Cohen et al. (2006) drug-class benchmark extension comparing BoW and BiomedBERT.

🔍 在 Weaviate 混合检索中评测 embedding 模型，基于自有数据或 MTEB 数据集评估 MRR@K、Hit@K、延迟与内存占用🔍 Benchmark embedding models in hybrid search with Weaviate. Evaluate MRR@K, Hit@K, latency, and memory using your data or MTEB datasets.

UMD 行星长期记忆算法、公式、基准、SDK 与可复现研究。UMD planetary long-term memory algorithms, formulas, benchmarks, SDKs, and reproducible research

论文《Physics-Informed Neural Networks and Neural Operators for PDEs: A Unified Taxonomy and Systematic Review》（Neural Networks）的补充材料：PRISMA 数据集、PDE 复杂度评分标准以及概念验证的统一基准。Supplementary materials for 'Physics-Informed Neural Networks and Neural Operators for PDEs: A Unified Taxonomy and Systematic Review' (Neural Networks): PRISMA datasets, PDE complexity rubric, and proof-of-concept unified benchmark

可复现研究基准实验：二维度量空间下的贪心 t-Spanner 构造算法A reproducible research benchmarking lab for Greedy t-Spanner Construction Algorithms in 2D Metric Spaces.

AI 诊断近视准确性的系统综述：完整数据、提取流程、代码与稿件。Systematic review of AI diagnostic accuracy for myopia detection: full data, extraction, code, and manuscript

在 DABench 数据分析任务上对 DSPy RLM（Recursive Language Models）进行基准测试，使用自动评分实现基于代码的迭代评估Benchmark DSPy Recursive Language Models on DABench data analysis tasks with automated scoring for iterative code-based evaluation

HateMirage——可解释的伪仇恨检测与多维推理，ICON 2026 共享任务。在 HateMirage 语料库（4,530 条带标注的伪仇恨评论）上进行目标识别 + 意图与隐含意义生成。HateMirage - Explainable Faux Hate Detection and Multi-Dimensional Reasoning - Shared Task @ ICON 2026. Target identification + Intent and Implication generation over the HateMirage corpus (4,530 annotated Faux Hate comments).

SURF2026 计算金融项目，研究感知自由边界的美式期权风险曲面，使用 CN/PSOR 基准、文献综述、实验报告以及 Codex 辅助的分步研究规划SURF2026 computational finance project on free-boundary-aware American option risk surfaces, using CN/PSOR benchmarks, literature review, experiment reports, and step-by-step Codex-assisted research planning.

可复现研究：基于基准、统计模型与走步前向验证，检验可解释的市场信号能否预测 SPY 的五日方向。Reproducible research testing whether interpretable market signals can forecast SPY’s five-day direction using benchmarks, statistical models, and walk-forward validation.

追踪并比较每一个严肃的 OpenClaw 替代方案——基于实测仓库数据与 AI 撰写的决策支持，且刻意保持独立。Track and compare every serious OpenClaw alternative — measured repo data, AI-written decision support, kept apart on purpose.

用于复现 https://arxiv.org/abs/2106.12905 中图示所使用代码与数据的仓库。A repository to story the code and data used to create the figures shown in https://arxiv.org/abs/2106.12905

Shadow / Unmanaged API 的发现、分类、安全验证与可复现研究基准。Shadow / Unmanaged API discovery, classification, security validation, and reproducible research benchmarks

无人机系统（UAS）在生态学研究中应用的系统综述。Systematic Review of UAS use in ecological research

面向系统综述数据抽取、证据定位与偏倚风险评估的冻结式（frozen）防泄漏基准。Frozen, leakage-aware benchmark for systematic-review data extraction, evidence localization, and risk-of-bias support

以问题为先、步骤可验证、可自我进化的 agent skill，用于系统综述与 Meta 分析。A question-first, step-verified, self-improving agent skill for systematic reviews and meta-analysis

系统综述的主动学习摘要筛选。在全部 26 个 SYNERGY 数据集上基准测试：WSS@95 均值 64.1。Active-learning abstract screening for systematic reviews. Benchmarked across all 26 SYNERGY datasets: mean WSS@95 of 64.1.

中文學術寫作的後設論述量尺 · A descriptive metadiscourse scale for Chinese academic writing (not a detector)

🎨 利用 AI 实时生成高保真 UI 设计，对比多版本方案，并跨多个模型导出可直接使用的代码。🎨 Generate high-fidelity UI designs in real-time with AI, compare variations, and export ready-to-use code across multiple models.

将问题框架视为计算状态的可复现研究框架，包含理论、算法与基准，用于内生探究。A reproducible research framework for treating problem frames as computational states, with theory, algorithms, and benchmarks for endogenous inquiry.

用于 Bosniak 分级可重复性系统综述与 meta 分析的数据及 R 代码Data and R code for the systematic review and meta-analysis of Bosniak classification reproducibility

人工智能（AI）已成为提升乳腺 X 线摄影对乳腺癌诊断准确性的潜力工具。本系统综述与荟萃分析旨在探讨 AI 与放射科医生通过乳腺 X 线摄影检测乳腺癌的准确性。Artificial intelligence (AI) has emerged as a promising tool to improve the diagnostic accuracy of 49 mammography for breast carcinoma detection. This systematic review and meta-analysis aim to 50 explore the accuracy of AI and radiologists in breast carcinoma detection via mammography