仓库/Skill 库

有界研究 Agent：plan → retrieve → critique，具备步骤预算、执行轨迹、引用追溯与自由路径 UI，无需 API key。Bounded research agent: plan → retrieve → critique, step budgets, traces, citations, free-path UI. No API key.

在 Claude 中强制以引用优先的研究流程，通过结构化五阶段工作流验证事实、识别不确定性并消除幻觉。Enforce citation-first research with Claude to verify facts, identify uncertainty, and eliminate hallucinations through a structured five-phase workflow.

Subconscious Memory 2026：基于混合语义搜索与 MCP 的 AI 编码上下文引擎。Subconscious Memory 2026: AI Coding Context Engine with Hybrid Semantic Search & MCP

大语言模型（LLM）是一种人工智能（AI）程序，能够识别并生成文本，以及执行其他任务。A large language model (LLM) is a type of artificial intelligence (AI) program that can recognize and generate text, among other tasks.

针对 PX4 飞控软件的覆盖率引导事务语义审计研究 artifactResearch artifact for coverage-guided transactional semantic auditing of PX4 flight-control software.

基于多 Agent LLM 工作流的智能 AI 学术研究助手，可自动化完成学术论文发现、验证、文献分析、对比矩阵（含表格）、RAG 驱动的研究对话，并产出可直接用于稿件的文献综述An agentic AI academic research assistant that automates academic paper discovery, validation, literature analysis, comparison matrices with table, RAG-powered research chat, and manuscript-ready literature reviews using multi-agent LLM workflows.

完全在浏览器中通过 WebAssembly 实现文档转换、检查、OCR 与翻译。保留版式的翻译，全程本地、离线优先。Convert, inspect, OCR, and translate any document entirely in the browser via WebAssembly. Layout-preserving translation, fully local, offline-first.

基于 LangGraph、LangChain、Groq、ChromaDB 与 Streamlit 构建的多 Agent AI 研究助手，通过联网检索、获取 arXiv 论文、利用 RAG 索引 PDF 并生成有研究依据的回答，实现文献综述自动化Multi-agent AI Research Assistant built with LangGraph, LangChain, Groq, ChromaDB, and Streamlit. Automates literature review by searching the web, retrieving arXiv papers, indexing PDFs with RAG, and generating research-backed answers.

面向 AI 饮食评估研究的 3 周入门计划：文献综述、分割、LLM 与 RAG。3-week onboarding plan for AI dietary assessment research: literature review, segmentation, LLMs, and RAG

面向有据可循的 Agent 工作流的 TypeScript 参考实现，支持混合检索、人工审核与可回放的审计追踪。TypeScript reference implementation for grounded agent workflows with hybrid retrieval, human review, and replayable audit trails.

KARZOUN-X —— 在地球通信延迟或不可用条件下，面向航天器自主故障诊断的资源感知、安全门控本地 AI 开源研究。KARZOUN-X — Open research on resource-aware, safety-gated local AI for autonomous spacecraft fault diagnosis under delayed or unavailable Earth communication.

Astro 静态站点，英意双语，安全设计（security-by-design），AI 辅助的月度 RAG 杂志。部署于 Cloudflare。Astro (static), bilingual EN/IT, security-by-design, AI-assisted monthly RAG magazine. Deployed on Cloudflare.

对 LLM 文献筛选方法的受控双研究对比——RAG 与 zero-shot、自适应与被动选文——并以真实系统综述的真值标注进行评估。Controlled two-study comparison of LLM-based literature screening methods — RAG vs. zero-shot, and adaptive vs. passive paper selection — evaluated against real systematic review ground truth.

涵盖 RAG、AI Agent、评估与商业智能的实战 AI 应用项目集。Practical AI application projects across RAG, AI agents, evaluation, and business intelligence.

Pinakes —— 可移植、Agent 优先的知识库。一个目录即一个 KB。Pinakes — a portable, agent-first knowledge base. One directory = one KB.

GeneTech 14 — 多领域实体集合（6700+ 实体，覆盖 14 个领域）GeneTech 14????? - ?????????? (6700+ entities, 14 domains)

个人知识图谱：抽取、验证阶梯、检索与衰减，通过 MCP 服务任意客户端A personal knowledge graph: extraction, a verification ladder, retrieval, and decay — served over MCP to any client

AI 时代的工具联盟：可追溯的知识（Nexus·Archon）、可问责的审查与治理（Arbiter·Observer）、可理解的变更质量（Adept·Probe）。AI 构建，你来理解。An alliance of tools for the AI era: grounded knowledge (Nexus·Archon), accountable review & governance (Arbiter·Observer), and comprehension & mutation quality (Adept·Probe). AI builds it — you understand it.

多阶段气候声明检索与排序：BM25、稠密 ANN、融合、重排序与评估Multi-stage climate claim retrieval and ranking: BM25, dense ANN, fusion, reranking, and evaluation

FinHop 是专为机构股票分析打造的多跳金融研究助手。它摄取 SEC 文件（10-K、10-Q），将其建模为时序感知、按公司组织的检索图，并回答需要跨多份文档、多家公司与多个财周期证据的对比与趋势类研究问题。FinHop is a multi-hop financial research assistant purpose-built for institutional equity analysis. It ingests SEC filings — 10-Ks, 10-Qs ,models them as a time-aware, company-aware retrieval graph, and answers comparative and trend-based research questions that require evidence from across multiple documents, companies, and fiscal periods.

Telegram RAG 机器人，以暗黑荒诞的《权力的游戏》谋士风格回答问题；支持英中双语，基于 LLaMA 3.3 70B + Firestore 向量搜索。A Telegram RAG bot that answers your questions as a darkly absurdist Game of Thrones advisor — bilingual (EN/ZH), powered by LLaMA 3.3 70B + Firestore vector search.

面向 LLM 的阶段感知上下文窗口治理框架。提供不变的上下文长度上限、基于熵的稳定性控制，以及针对降级（碎片化）状态的概率性保证，适用于生产级 LLM 系统。Phase-aware context window governance framework for Large Language Models (LLMs). Provides invariant context length caps, entropy-based stability control, and probabilistic guarantees against degraded (fragmentation) states for production LLM systems.

🎨 利用 GPT、Gemini 等模型，AI 驱动的学术图表一键生成与定制工具🎨 Generate academic diagrams effortlessly with this AI-driven tool, leveraging models like GPT and Gemini for seamless creation and customization.

自动化 n8n 工作流，抓取 LinkedIn 产品经理职位，通过向量相似度与简历匹配打分，并使用 Claude 为每个职位生成简历修改建议Automated n8n workflow that scrapes LinkedIn PM jobs, scores them against your resume with vector similarity, and uses Claude to suggest per-job resume edits.

🧪 实验性本地优先 RAG，附带桌面 GUI：将文档拖入文件夹即可对话聊天，内置 Ollama / fastembed / OCR / reranker。🧪 Experimental local-first RAG with desktop GUI — drop documents in a folder, chat with them. Ollama / fastembed / OCR / reranker built in.

部署并运行本地 RAG 实例Deploy and run a local RAG instance

基于 Neo4j 知识图谱回答生物医学研究问题的多 Agent 系统——采用 LangGraph 编排、图+向量混合 RAG、只读 Cypher 护栏、Docker 隔离的代码执行与人在环上审核。Multi-agent system answering biomedical research questions over a Neo4j knowledge graph — LangGraph orchestration, hybrid graph+vector RAG, read-only Cypher guardrails, Docker-isolated code execution and human-in-the-loop review

系统综述与 Meta 分析的检索、提取与分析代码，主题为基于可穿戴设备生命体征的被动认证。Retrieval, Extraction and Analysis Code for a Systematic Review and Meta-Analysis on Passive Authentication using Vital Signs Collected Via Wearable Devices

🔍 Self-Corrective RAG 基于 LangGraph 与 Google Gemini 优化查询并评估文档相关性，提升检索效果🔍 Enhance your searches with Self-Corrective RAG, a system that optimizes queries and evaluates document relevance using LangGraph and Google Gemini.

自托管的个人上下文服务器 — 你与 AI Agent 共享统一的知识与信任边界：知识库、检索、凭证与 Agent 记忆，通过 MCP 挂载Self-hosted personal context server — one knowledge and trust boundary shared by you and your AI agents: knowledge base, retrieval, credentials, and agent memory, mounted over MCP

基于本地 RAG 的 AI 研究助手，用于分析学术论文、总结研究发现、对比研究，并以页级引用回答研究问题。构建于 Python、Streamlit、Chroma、Sentence Transformers、Ollama 和 Llama 3。An AI research assistant using local RAG to analyze academic papers, summarize research findings, compare studies, and answer research questions with page-level citations. Built with Python, Streamlit, Chroma, Sentence Transformers, Ollama, and Llama 3.

一款本地化、隐私优先的 LLM 助手，提供学术写作辅助、音频转写、文档检索与数据分析。A local, privacy-first LLM assistant that performs academic writing assistance, audio transcription, document retrieval, and data analysis.

基于 .NET 的 RAG 文档摄取流水线 — 追踪 git 备份仓库中的文件夹，提取并分块文件，保持向量索引同步。.NET document ingestion pipeline for RAG — tracks a folder of files in a git-backed vault, extracts and chunks them, and keeps a vector index in sync.

为 AI agent 提供持久化、结构化记忆。基于 Go 的本地优先 MCP server：语义召回、去重、provenance、冲突解决。单一静态二进制，零依赖。Persistent, structured memory for AI agents. Local-first MCP server in Go: semantic recall, dedupe, provenance, conflict resolution. One static binary, zero dependencies.

多租户 SaaS 自动化平台，具备事件驱动工作流、AI 驱动的文档处理与实时集成。Multi-tenant SaaS automation platform with event-driven workflows, AI-powered document processing, and real-time integrations.

ChatGPT 风格 AI 助手，面向 UET Taxila 学生——通过 RAG 基于真实大学数据回答招生/学费/课程问题，而非猜测。采用 Next.js、Convex 与 multi-LLM 路由。ChatGPT-style AI assistant for UET Taxila students - answers admissions/fees/courses questions using real university data via RAG, not guessing. Next.js, Convex, multi-LLM routing.