仓库/Skill 库

自托管、多用户转录平台：录制或上传音频。支持说话人标记、带时间戳的转录、跨录音识别说话人、摘要、提取行动项，并可使用自有 OpenAI 兼容 LLM 与转录内容对话。你的音频、你的服务器、你的模型。已在笔记本 RTX4070、台式机 RTX3090 与 RTX5090 上测试。Self-hosted, multi-user transcription platform: record or upload audio. Speaker-labeled, timestamped transcripts, Recognize speakers across recordings, Summarize, extract action items and chat over your transcripts with your own OpenAI-compatible LLM. Your Audio, your Server, your Model. Tested on Laptop RTX4070, Desktop RTX3090 and RTX5090

🛠️ 通过 Local-LLM（一款基于 BERT 模型的轻量级 Python 库）在安全环境中实现离线 NLP 工作流，确保可复现性与可靠性。🛠️ Enable offline NLP workflows with Local-LLM, a lightweight Python library for secure environments using the BERT model, ensuring reproducibility and reliability.

金融与生活交易学习 RAG 与 QuantConnect LEAN 回测工作流（FastAPI、Qdrant、pgvector、Docker）。금융·생활거래 학습 RAG와 QuantConnect LEAN 백테스트 워크플로우 (FastAPI, Qdrant, pgvector, Docker)

自托管、隐私优先的个人 AI 知识伴侣，具备 RAG 驱动的检索、智能 Agent 与可扩展 SDK。Self-hosted, privacy-first Personal AI Knowledge Companion with RAG-powered retrieval, intelligent agents, and extensible SDK

面向商业智能 Agent 的 MCP server——基于真实 KPI 分析提供 6 个 tools / 6 个 resources / 1 个 prompt；兼容 Claude Desktop、LangGraph、CrewAI、DSPy。基于 FastMCP。MCP server for business-intelligence agents — 6 tools / 6 resources / 1 prompt over real KPI analytics; works from Claude Desktop, LangGraph, CrewAI, DSPy. FastMCP.

面向殡葬/保险/遗产服务机构的 To B 多租户 AI 平台。包含案件管理、审计日志、知识库、团队 RBAC、许可证及数据导出。基于 FastAPI + LangGraph 构建。To B multi-tenant AI platform for funeral/insurance/estate service organizations. Case management, audit logs, knowledge base, team RBAC, license & data export. Built on FastAPI + LangGraph.

面向 AI coding agents 的 local-first 分层记忆中枢——自动采集、LLM 整合、hybrid retrieval、实时 Neural Universe 监控、日常自审计。兼容 Claude Code / Codex / 任何 MCP 客户端。Local-first hierarchical memory brain for AI coding agents — auto-capture, LLM consolidation, hybrid retrieval, live Neural Universe monitor, daily self-audit. Claude Code / Codex / any MCP client.

空间智能工作站——53 路实时 OSINT 数据源，基于 PostgreSQL+pgvector 的 FollowTheMoney 实体图谱，9 阶段多 Agent 编排器（4 层防幻觉防护）、贝叶斯信源裁定、W3C PROV 证据 DAG、GNN 流水线（GAT+T-GCN+FedAvg+DP-SGD），3 节点 Ed25519 认证网格，通过 NVIDIA NIM Cascade 实现 24 小时自动简报Spatial intelligence workstation — 53 live OSINT feeds, FollowTheMoney entity graph on PostgreSQL+pgvector, 9‑phase multi‑agent orchestrator with 4‑layer anti‑hallucination guards, Bayesian source adjudication, W3C PROV evidence DAG, GNN pipeline (GAT+T‑GCN+FedAvg+DP‑SGD), 3‑node mesh with Ed25519 auth, automated 24h briefing via NVIDIA NIM Cascade

面向会议的对话智能：将转录文本转化为摘要、决策、行动项、风险与机会，支持多租户自托管。FIAP NEXT Challenge 2026。Conversation intelligence for meetings: turns transcripts into summaries, decisions, action items, risks and opportunities. Multi-tenant, self-hosted. FIAP NEXT Challenge 2026.

基于 specter2 + BERTopic 的 arXiv 论文发现——通过 embedding 检索与主题聚类实现每日论文推荐。arXiv paper discovery with specter2 + BERTopic — embedding-based retrieval and topic clustering for daily paper recommendations.

面向 Kubernetes 环境的 air-gapped RAG pipeline。在 LibreChat/LiteLLM/Keycloak/vLLM 之上增加文档摄取、强制的分类/可发布性标签、策展人审核与基于声明的访问控制检索，并以自定义 MCP 工具的形式对外暴露。Air-gapped RAG pipeline for Kubernetes environments. Adds document ingestion, mandatory classification/releasability tagging, curator review, and claims-based access-controlled retrieval on top of LibreChat/LiteLLM/Keycloak/vLLM — exposed as a custom MCP tool.

面向银行内部产品审计的深度研究 LLM Agent + RAG 平台，提供带引用报告、PDF 导出与 pgvector 支持。Deep-research LLM agent + RAG platform for internal bank-product audit — cited reports, PDF export, pgvector

FastMCP 3.2 语音 gateway：FunASR 本地 STT、Gemini Live/TTS、Hume、ElevenLabs 声音克隆、唤醒词、RAG。MCP server + React webapp。FastMCP 3.2 speech gateway: FunASR local STT, Gemini Live/TTS, Hume, ElevenLabs cloning, wake word, RAG. MCP server + React webapp.

🐙 AI Agent Pipeline 基于意图将查询路由至文档、天气或聊天模块，结合 LangGraph、ChromaDB 与 LangSmith，实现跨 CLI 与 UI 的模块化、可观测工作流🐙 AI Agent Pipeline routes queries by intent to docs, weather, or chat, with LangGraph, ChromaDB, and LangSmith for modular, observable workflows across CLI and UI.

一条 RAG pipeline，从 Stack Overflow 抓取问答内容并转化为 RAG 格式，存储到 huggingface 数据集中。This is RAG pipeline that take question answer from Stack Overflow and convert it to rag and get stored in the dataset in huggingface

AI agent memory 与基础设施全景——912 个系统 × 68 列的对比目录，覆盖记忆层、agent 框架、运行时、vector store、知识图谱、MCP server、benchmark。支持按类型化边、谱系、引用进行检索。AI agent memory & infrastructure landscape — comparative catalog of 912 systems × 68 columns covering memory layers, agent frameworks, runtimes, vector stores, knowledge graphs, MCP servers, benchmarks. Searchable with typed edges, lineages, citations.

韩国加密货币 × AI 垂直媒体与社区——alpha.moss.land：频道立场、AI 简报、RAG、8 个带业绩记录的 AI persona、12 工具 MCP server。Korean crypto × AI vertical media + community — alpha.moss.land. Channel stance, AI briefs, RAG, 8 AI personas with track records, 12-tool MCP server.

面向 Zed 的智能代码库搜索与索引。异步 MCP server，支持 LanceDB/BM25 混合搜索、多桶 RAG 以及自主 self-healing workflow。高性能、内存安全，可直接用于生产代码。Intelligent codebase search & indexing for Zed. Async MCP server featuring LanceDB/BM25 hybrid search, multi-bucket RAG, and autonomous self-healing workflows. High-performance, memory-safe, and ready for your production code.

1,716 道 AI/ML 面试题（含答题框架）、69 张架构图、按角色的学习路径，以及语音驱动的模拟面试工具。覆盖 LLM、RAG、agent、MCP/A2A、系统设计、MLOps、安全与云部署。1,716 AI/ML interview questions with answer frameworks, 69 architecture diagrams, role-based study paths, and a voice-enabled mock interview simulator. Covers LLMs, RAG, agents, MCP/A2A, system design, MLOps, safety and cloud deployment.

使 AI agent 能够在任何市场上自主创建、评估与进化 Skill，无需用户干预Enable AI agents to autonomously create, evaluate, and evolve skills across any marketplace without user intervention.

用于拆解复杂技术文档的 RAG 应用A RAG app to breakdown complex technical documents

本地优先的 AI 论文阅读助手 —— Agentic RAG、引文追溯、Electron 桌面应用，完全离线。Local-first AI reading assistant for academic papers — Agentic RAG, citation tracing, Electron desktop app, fully offline

🖥️ 通过终端中键盘优先的 Kanban board 简化工作流，实现快速、专注的任务管理。🖥️ Streamline your workflow with a keyboard-first Kanban board in your terminal for fast, focused task management.

使用 SQLite 和 FAISS 存储、整合与召回 coding agent 记忆，附带 provenance 追踪，实现快速、结构化的知识访问Store, consolidate, and recall coding agent memories with provenance tracking using SQLite and FAISS for fast, structured knowledge access.

AI Research Wiki 2026：通过深度引用综合自动构建知识库AI Research Wiki 2026: Auto-Building Knowledge Base with Deep Citation Syntheses

🌟 利用 GPU 加速 Apple Silicon 上的 SQLite 数据库操作，提升分析速度与数据管理效率🌟 Accelerate SQLite database operations on Apple Silicon with GPU power for faster analytics and efficient data management.

基于 LlamaIndex、Redis 与 PII 脱敏的 RAG 搜索引擎。RAG search engine using LlamaIndex, Redis, and PII masking.

智利国家机构网络安全合规扫描器，对标 21.663 号法令及 ANCI 通用指令。结合主动网络扫描与声明式问卷，生成 PDF 差距报告以及面向 CSIRT Chile 的 JSON 报告。Escáner de cumplimiento de ciberseguridad para organismos del Estado chileno, alineado a Ley 21.663; y las Instrucciones Generales de la ANCI. Combina escaneo activo de red con un cuestionario declarativo para producir un informe de brechas en PDF y un reporte JSON listo para CSIRT Chile.

opencode 的持久化记忆 MCP server：Redis 存储事实、ChromaDB 语义搜索、文档存储、幂律记忆衰减、agent 身份；内置官方 Claude.app OAuth，可使用 Claude Max 套餐且规避第三方检测；一键安装。Persistent memory MCP server for opencode — Redis facts, ChromaDB semantic search, document storage, power-law memory decay, agent identity. Plus first-party Claude.app OAuth: use your Claude Max plan without third-party detection. One-command install.

使用先进的 RAG 系统发现你的下一部心仪动画，提供精准推荐与增强语义搜索。🎬 Discover your next favorite anime with this advanced Retrieval-Augmented Generation system, offering precise recommendations and enriched semantic search.

面向 AI Agent 的低开销记忆后端 —— 单二进制，内存占用约 50MB，支持验证增强准确性Low-footprint memory backend for AI agents — single binary, ~50MB RAM, verify-augmented accuracy

🤖 生产级 AI agent 编排平台，支持实时流式输出与多工具执行🤖 Orchestrate AI agents with ease using this production-ready platform, featuring real-time streaming and multi-tool execution capabilities.

为 AI agent 提供可验证的记忆：写入受控、读取带溯源、双时态历史、无法回答时选择 abstention 而非幻觉。AGPL/商业双重许可。Verified memory for AI agents: gated writes, provenance on every read, bi-temporal history, abstention instead of hallucination. AGPL/commercial.

基于 RAG、LangGraph 多 agent workflow、FastAPI 与 PostgreSQL（pgvector）构建的 AI 教育内容生成平台。生产级架构，集成 LangSmith tracing。AI-powered educational content generation platform using RAG, LangGraph multi-agent workflows, FastAPI, and PostgreSQL with pgvector. Production-ready architecture with LangSmith tracing.

一个面向 PDF 文档问答的全栈 RAG 应用：上传 PDF，将其索引到本地向量库，然后基于页面级答案进行对话，并在内置阅读器中通过可点击引用跳转到对应页面。A full-stack retrieval-augmented generation (RAG) application for question answering over PDF documents. Upload a PDF, index it into a local vector store, then chat with page-grounded answers and clickable citations that jump to the right page in the built-in viewer.