仓库/Skill 库

统一 9 种学术 API（PubMed、OpenAlex、arXiv、Semantic Scholar 等）的学术摘要获取器，以单一 Python 接口对外提供。Academic abstract fetcher unifying 9 scholarly APIs (PubMed, OpenAlex, arXiv, Semantic Scholar…) behind one Python interface

使用简洁的 markdown 工作空间（基于可检视的文件）管理持久化 agent 记忆，无需复杂框架。Manage durable agent memory using a simple markdown workspace with inspectable files instead of complex frameworks.

基于 3,377 篇 arXiv 预印本的混合搜索与有依据问答，并附带若检索质量回退则失败 CI 的检索消融实验。Hybrid search and grounded question answering over 3,377 arXiv preprints — with a retrieval ablation that fails CI if quality regresses.

🌐 通过 Geo-Llama 利用几何深度学习增强语言理解，结合 conformal manifolds 和递归等距变换提升 AI 模型性能。🌐 Enhance language understanding through geometric deep learning with Geo-Llama, leveraging conformal manifolds and recursive isometries for improved AI models.

本地 LLM 基准测试、RAG、语音交互与 AI 助手Local LLM benchmarking, RAG, voice interaction and AI assistant

2026 掌握 LLM 搜索：完整的语义 AI 手册Master LLM Search in 2026: The Complete Semantic AI Handbook

基于 MCP 的实时来源声明与引用验证，支持 FIGI 解析与签名投递回执。Live-source claim and citation verification over MCP, with FIGI resolution and signed delivery receipts.

面向 LLM Agent 的确定性写入时记忆整合与预取召回：采用本地模型，召回路径与整合决策中均不含 LLM。Deterministic write-time memory consolidation and pre-fetch recall for LLM agents. Local models, no LLM in the recall path or the consolidation decision.

使用 Dialectic Flow Financial Graph 分析市场趋势，模拟多方与空方辩论，获得清晰的投资洞察与报告。📊 Analyze market trends with the Dialectic Flow Financial Graph, simulating Bull and Bear debates for clear investment insights and reports.

一款全栈应用，通过 Chrome 扩展自动捕获求职申请，在统一面板中跟踪记录，并提供洞察以优化求职流程。A full-stack application that automatically captures job applications through a Chrome extension, tracks them in a centralized dashboard, and provides insights to improve your job search.

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.

Agentic AI 平台，用于仓库分析、迁移规划、上下文驱动的代码转换及迁移后验证。Agentic AI platform for repository analysis, migration planning, context-grounded code transformation, and post-migration validation.

为 LLM 记忆而构建。Built for LLMs memory

使用这款专为架构设计与引擎特定任务优化的专用 LLM，提升游戏开发工作流效率。Optimize game development workflows with this specialized large language model designed for architecture design and engine-specific tasks.

拥有持久记忆的终端 AI 角色扮演客户端 —— 本地 SQLite、语义召回以及每次调用的成本统计。兼容 OpenAI，已在 OpenRouter 上测试。A terminal AI roleplay client with a memory that does not forget — local SQLite, semantic recall, and what every call cost. OpenAI-compatible, tested on OpenRouter.

基于 LLM 的 AI 研究 Agent：检索 arXiv、生成假设，以及一个能够拒绝自身输出的 Critic Agent。支持通过 Ollama 在本地运行，或使用 Anthropic API。LLM-backed AI research agent: arXiv retrieval, hypothesis generation, and a Critic agent that can reject its own output. Runs locally with Ollama or via the Anthropic API.

Uniphore 是一家企业级 AI 公司——"The Business AI Company"——成立于 2008 年，总部位于加利福尼亚州帕罗奥图。其旗舰平台 Business AI Cloud（BAIC）是一个主权化、可组合且安全的 AI 平台，分为四层：用于对企业数据进行零拷贝访问的 Data Layer、用于检索的 Knowledge Layer……Uniphore is an enterprise AI company — "The Business AI Company" — founded in 2008 and headquartered in Palo Alto, California. Its flagship platform, the Business AI Cloud (BAIC), is a sovereign, composable and secure AI platform organised into four layers: a Data Layer for zero-copy access to enterprise data, a Knowledge Layer for retrieval and…

Space Frontiers 是一家怀俄明州公司，其搜索与 AI 产品 Machine Library（前身为 Space Frontiers search，于 2026-09-12 迁至 machinelibrary.ai）是一个全文检索 API 与托管 MCP server，覆盖约 29 亿条记录的语料库：包括同行评审论文（CrossRef、PubMed、arXiv）、书籍、USPTO 专利、Wikipedia……Space Frontiers is a Wyoming corporation whose search and AI product, Machine Library (formerly Space Frontiers search, moved to machinelibrary.ai on 2026-09-12), is a full-text retrieval API and hosted MCP server over a corpus of roughly 2.9 billion records: peer-reviewed papers (CrossRef, PubMed, arXiv), books, USPTO patents, Wikipedia…

Hands-on 实验 2：基于合成技术语料构建 RAG pipeline，并通过沙箱化 NemoClaw agent 提供服务，附带 EN/VI 双语 GUI demo。Hands-on 2 lab: build a RAG pipeline over a synthetic technical corpus and serve it through a sandboxed NemoClaw agent, with a bilingual (EN/VI) GUI demo.

基于书签或主题推荐 arXiv 论文的 AI AgentAI agent that recommends arXiv papers based on bookmarks or topics.

AI/RAG 平台蓝图，配备 Python 发布门控，覆盖路由、隐私、引用、向量恢复与离线证据验证。AI/RAG platform blueprint with Python release gates for routing, privacy, citations, vector recovery, and offline evidence validation.

通过分析 trace 诊断与调试 RAG 流水线，定位并修复影响答案质量与相关性的问题Diagnose and debug Retrieval Augmented Generation pipelines by analyzing traces to identify and fix issues affecting answer quality and relevance.

🌐 利用多跳图推理结合知识图谱和向量搜索，挖掘隐藏的全球供应链风险，获得更深层洞察。🌐 Map hidden global supply chain risks with multi-hop graph reasoning combining knowledge graphs and vector search for deeper insights.

AI 驱动的加密情绪日记——基于 RAG 的 LLM 反馈（TAIDE + ChromaDB）结合 Random Forest 情绪预测。Live demo：heartbox.tw（test1/test1）。AI-powered encrypted mood journal — RAG-grounded LLM feedback (TAIDE + ChromaDB) and Random Forest emotion forecasting. Live demo: heartbox.tw (test1/test1)

🌐 在数据受限条件下重新思考多模态大语言模型的设计与扩展，以 NaViL 通过 Native Training 提升效率与性能🌐 Rethink Multimodal Large Language Models design and scaling under data constraints with NaViL, enhancing efficiency and performance through Native Training.

一个 agentic、循证的研究系统：通过工具调用、RAG 与显式的论据—来源核验从研究问题生成结构化、带引用的报告，避免盲目的 LLM 摘要；能识别来源冲突并如实报告不确定性An agentic, evidence-grounded research system that generates a structured, citation-backed report from a research question — using tool-calling, RAG, and explicit claim-to-source verification instead of blind LLM summarization. Detects conflicting sources and reports uncertainty rather than hiding it.

一个基于 C# 与本地 LLM 的学术论文 RAG（Retrieval-Augmented Generation）系统。目标是摄入研究论文语料，构建可回答文献综述类问题（如"其他论文关于 X 发现了什么"）的系统，回答严格基于真实源材料。A RAG (Retrieval-Augmented Generation) system for academic papers using C# and a local LLM. The goal is to ingest a corpus of research papers and build a system that can answer literature review questions like "what have other papers found about X"; grounded in the actual source material.

本地优先的印尼语字幕生成，面向授权学习资源。Local-first Indonesian caption generation for authorized learning sources

OncoRAG 是一个 RAG 系统，为临床医生和研究人员提供即时获取最新肿瘤学证据的能力——来源包括直接来自 PubMed 的随机对照试验、Meta 分析、系统综述和 III 期临床试验。提出临床问题，即可获得带引文的可靠答案。OncoRAG is a Retrieval-Augmented Generation system that gives clinicians and researchers instant access to the latest oncology evidence — drawing from Randomized Controlled Trials, Meta-Analyses, Systematic Reviews, and Phase III Clinical Trials sourced directly from PubMed. Ask a clinical question, get a grounded answer with citations.

自动化监控、打补丁与 Telegram 推送 Augment VSCode 插件更新，附带版本追踪与清理Automate monitoring, patching, and Telegram delivery of Augment VSCode plugin updates with version tracking and cleanup.

面向文档溯源 QA Agent 的生产级 Harness：检索、起草、自评、重写、升级循环，配套 LLM-as-judge 评测套件、分级权限工具、对破坏性操作的人工审批，以及一键容器化部署。A production-grade harness for a document-grounded QA agent: a retrieve, draft, self-score, re-draft, escalate loop with an LLM-as-judge eval suite, permission-tiered tools, human-in-the-loop approval for destructive actions, and a one-command container deploy.

面向 agentic AI 创业领域的战略情报平台——FastAPI + SQLite + 原生 JS SPA，内置专业 LLM agent 集群、咨询级行业档案、业务线领域矩阵以及 RAG copilot。Strategic intelligence platform for the agentic-AI startup landscape — FastAPI + SQLite + vanilla-JS SPA with a specialist LLM agent fleet, consulting-grade industry dossiers, a line-of-business domain matrix, and a RAG copilot.

个人 AI 工程博客与作品集——12 篇文章，覆盖 agentic AI、RAG、embeddings、chunking、token 及生产实践。基于 Astro 构建，部署于 Cloudflare Pages。Personal AI engineering blog and portfolio — 12 posts covering agentic AI, RAG, embeddings, chunking, tokens, and production patterns. Built with Astro and deployed on Cloudflare Pages.

🩺 为嵌入式医疗设备提供安全、合规级别的 AI 本地化方案，具备术语库控制与屏幕适配校验能力。🩺 Deliver safe, compliance-grade AI localization for embedded medical devices with glossary control and screen-fit validation.

多工具编排 Agent，可针对复合型金融研究问题自动决策调用哪个金融数据 API，每项论断均附引用来源；包含配套的 Go 数据接入服务A multi-tool orchestration agent that answers compound financial research questions by deciding which of several financial-data APIs to call, grounding every claim in a cited source. Includes a companion Go ingestion service.

面向 AI coding agents 的 token 预算 context pack，基于编译器解析的 Kotlin 结构（Analysis API/PSI）构建，并附带衡量其是否优于 chunk RAG 的 benchmark。Token-budgeted context packs for AI coding agents, built from compiler-resolved Kotlin structure (Analysis API/PSI) — with the benchmark that measures whether it beats chunk RAG