仓库/Skill 库

面向 AI Agent 与软件服务的去中心化自主 Agent 商务中枢。Decentralized Autonomous Agent Commerce Hub for AI agents and software services.

RSI-EAF：基于 XRPL 的自治工厂。LIVE x402 商户——使用 testnet Tag 1/2 支付。RSI-EAF: XRPL-grounded autonomous factory. LIVE x402 merchant — pay testnet Tag 1/2. https://published-zeta.vercel.app/pay.html

0n1x——Agent 执行证明（Proof of Agent Execution）。面向 AI Agent 的中立、签名式信任层：付款前先验证，签名的是事实而非判断。AI Agent 领域的 Carfax。0n1x — Proof of Agent Execution. The neutral, signed trust layer for AI agents: verify before you pay, signed facts not judgments. The Carfax for AI agents.

面向 Bun 的、强约定的全栈框架，主要用户是 AI Agent。八个原语，统一的 authz 系统覆盖所有入口，错误信息附带可直接执行的修复命令。可在 PaaS 上免费起步，扩容无需改动应用代码。Bun-only, opinionated full-stack framework where the primary user is an AI agent. Eight primitives, one authz system across every surface, errors that carry an exact fix command. Start free on a PaaS; scale out without changing app code.

精选实用 Agent Skill 列表，覆盖 Claude Code、Codex、Gemini CLI、OpenClaw、Hermes 等 AI Coding Agent。A curated list of useful Agent Skills for Claude Code, Codex, Gemini CLI, OpenClaw, Hermes, and other AI coding agents.

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.

开源核心的「AI Agent 版 Spotify」——可自托管的可运行 Agent 工件注册中心：skill、bundle、loop 与 personality。docker compose up 即可在 60 秒内启动可用注册中心，零注册。MCP 原生，MPL-2.0 许可。Open-core 'Spotify for AI agents' — a self-hostable registry of runnable agent artifacts: skills, bundles, loops, and personalities. docker compose up → working registry in 60s, zero signup. MCP-native, MPL-2.0.

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.

拥有超能力的 Github。Github with super powers

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.

将 Justin Sun 的营销逻辑提炼为心智模型、决策启发式与文案模板，用于注意力驱动的增长。Distill Justin Sun's marketing logic into mental models, decision heuristics, and copywriting templates for attention-based growth.

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

Run the native 284B-A13B DeepSeek-V4-Flash-0731 LLM locally on a single laptop CPU: pure C, 8 GB RAM minimum, no GPU, best TPOT 0.892 s/token. ｜ 在笔记本单颗 CPU 上本地运行原生 284B-A13B DeepSeek-V4-Flash-0731 大模型：纯 C，最低 8 GB 内存，无需 GPU，最优 TPOT 0.892 秒/token。

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 Python（Anthropic SDK）的多 Agent 系统，为私人教练生成个性化训练与饮食计划——内置免费规则引擎 + 可选 LLM 层、显式状态编排器、Streamlit 仪表盘与确定性安全校验器。A multi-agent system in Python (Anthropic SDK) that generates personalized workout routines and meal plans for a personal trainer — free rule engine + optional LLM layer, explicit-state orchestrator, Streamlit dashboard, and deterministic safety validator.

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

🐙 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

BMAD 的精简版本，对较小 AI 计划额度更友好。A slimmed-down version of BMAD made to be friendlier to smaller AI plan limits

用知识图谱刻画 AI/ML 模型从创建到部署的完整生命周期。Knowledge Graph to capture AI/ML model lifecycle from creation through deployments.

AI Agent 集群的开源任务控制中心 —— 对话即可配置，Agent 负责执行，人类负责审批。Open-source mission control for AI agent fleets — chat to provision, agents to operate, humans to approve.

教学用 LLM 应用teaching LLM applications

面向 agent 间通信的私有点对点身份、信任与验证基础设施——agent 注册中心、已验证身份、防篡改消息历史。Private peer-to-peer identity, trust, and verification infrastructure for agent-to-agent communication — agent registry, verified identity, and tamper-proof message history

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.

面向人类与 AI Agent 的开放、无需登录的 KiCad 器件注册中心——提供经验证的 footprint、symbol 与 3D 模型（STEP/GLB），附带 datasheet 来源溯源、静态 JSON API 与 MCP server。Open, no-login KiCad parts registry for humans & AI agents — verified footprints, symbols & 3D models (STEP/GLB) with datasheet provenance + static JSON API + MCP server

用于实时技术分析与市场执行的自主 agentic 框架。Autonomous agentic framework for real-time technical analysis and market execution.

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.

语音 AI API 的开放价格数据库 —— STT、LLM、TTS、S2S、VAD。Open price database for voice AI APIs - STT, LLM, TTS, S2S, VAD

首个内置 MCP（server + client）的语言；Semantic Pipeline Runtime 提供 198 个内建函数、AI pipeline、沙箱化 Agent，单一二进制约 7 MB，零依赖。The first language with built-in MCP (server + client). Semantic Pipeline Runtime: 198 builtins, AI pipelines, sandboxed agents, single ~7 MB binary, zero dependencies.

Spec Seatbelt 让 AI 编程 Agent 保持诚实：先写计划、逐步证明、以全新视角验证；一次加载一个阶段（token 用量减少约 70%）；持久化的 .specs/ 记忆、循环计划波次与任务图，支持安全的并行工作。支持 Cursor 与 ClaudeSpec Seatbelt keeps AI coding agents honest: write the plan first, prove each step, verify with fresh eyes. Load one phase at a time (~70% fewer tokens). Persistent .specs/ memory, loop-plan waves, and task graphs for safe parallel work. Cursor & Claude.

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