Nymbo 多合一工具集
v1.0.0
MCP ServerMCP Registry
net.nymbo/tools
远程 MCP server：网页抓取、搜索、Python、TTS、记忆、图像与视频。
sseregistry 收录
“Memory System” 共 1,996 个结果
v1.0.0
net.nymbo/tools
远程 MCP server：网页抓取、搜索、Python、TTS、记忆、图像与视频。
v1.0.1
ai.zine/mcp
你的记忆随 AI 到各处。一次构建知识，随处通过 MCP 访问。
io.github.anthropics/skills/claude-api
Claude API / Anthropic SDK 参考——模型 id、定价、参数、流式输出、工具调用、MCP、agent、缓存、token 计数、模型迁移。TRIGGER——在打开目标文件前先阅读本参考，不要因为“看起来只是一行代码”而跳过，凡是：提示中出现 Claude/Anthropic 的任何形式（Claude、Anthropic、Fable、Opus、Sonnet、Haiku、anthropic、@anthropic-ai、claude-*、us.anthropic.*、[1m]）；用户询问 LLM 相关问题（定价/选型/限制/缓存）——不要凭记忆作答；或任务呈 LLM 形态但未指明提供方（agent/MCP/工具定义/多智能体/RAG/LLM 评审/computer-use；对自然语言做生成/摘要/抽取/分类/改写/对话；调试拒答/截断/流式/工具调用/token）。仅当处理的是其他提供方时可 SKIP（覆盖所有触发条件）：查询中点名 OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama；或对项目执行 grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama' 命中（先跑这条 grep
skillsmp.ruvnet-ruflo-skill-md
Ruflo 是面向 AI 编码 agent（Claude Code、Cursor、Codex、Copilot、Gemini、Amp 及另外 12 家）的多 agent 编排平台。当用户希望：(1)在项目中安装/初始化 ruflo；(2)运行采用分层协同的多 agent swarm；(3)使用 ruflo 的 314+ MCP 工具处理记忆、路由、钩子、sub-agent 或工作流；(4)查看 ruflo 状态/版本/doctor 健康检查；(5)为任务挑选合适的 ruflo 插件（共 30+）时使用本 skill。
io.github.garrytan/gstack/design-consultation
设计咨询：理解你的产品、调研同类竞品，提出完整设计体系（美学、字体排印、色彩、布局、间距、动效），并生成字体与配色预览。（gstack）
io.github.affaan-m/ECC/agent-security-hardening
Security hardening guidance for AI agent frameworks that process untrusted content, invoke tools, write workspace files, manage runtime identifiers, or handle credentials. Use when building or reviewing an agent runtime, autonomous worker, tool gateway, memory service, or multi-tenant agent deployment. Do not use for general web application security or offensive testing.
io.github.anthropics/skills/claude-api
Claude API / Anthropic SDK 参考——模型 id、定价、参数、流式输出、工具调用、MCP、agent、缓存、token 计数、模型迁移。触发条件：提示中出现任何 Claude/Anthropic 相关名称（Claude、Anthropic、Fable、Opus、Sonnet、Haiku 等）；用户询问 LLM 相关问题（定价/选型/限额/缓存）时不要凭记忆回答；或任务属于 LLM 类且未指明供应商。仅当明确针对其他供应商（OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama）时跳过。
io.github.google/skills/gke-ai-troubleshooting-tpu-performance-degradation
Diagnose GKE Cloud TPU training throughput drops and step-time regressions (15%+ TPU duty-cycle drop) using ML Diagnostics Workload Monitoring (`gcloud alpha mldiagnostics monitored-events` / `hypercomputecluster.googleapis.com/v1alpha`) and 1-minute Cloud Monitoring system metrics (`kubernetes.io/node/accelerator/*`). Distinguishes hardware and network fabric throttling from workload resource bottlenecks (HBM capacity, host memory, or host CPU saturation). Use when TPU training throughput or duty cycle drops without crashing pods, when `PERFORMANCE_DEGRADATION` monitored events fire, or when triaging slow multi-slice training steps. Don't use for complete multi-slice XLA execution stalls with `HANG_DETECTED` logs (use gke-ai-troubleshooting-tpu-mxla-hang) or pod eviction/interruption restarts (use gke-ai-troubleshooting-jobset-interruption).
io.github.getsentry/sentry/react-component-documentation
按 Sentry 的 MDX stories 格式创建或更新组件文档。当被要求“document a component”“add stories”“write component docs”“create an mdx file”“add a stories.mdx”，或为设计系统组件编写文档时使用。生成结构化 MDX，包含实时示例、无障碍指引，以及由 TypeScript 类型自动生成的 API 文档。
io.github.github/awesome-copilot/python-mcp-server-generator
生成包含工具、资源和规范配置的完整 Python MCP server 项目。
io.github.stas00/ml-engineering/ml-engineering
Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference performance and memory; debugging multi-node/ multi-GPU hangs; and writing/running tests. Use when the user is training or fine-tuning large models, hits low TFLOPS/MFU, OOM, slow dataloading, a loss spike/divergence, a NCCL/InfiniBand or multi-node hang, node/GPU failures, checkpoint or preemption problems, storage/network bottlenecks, or needs to pick GPUs/cloud/file-systems or size inference latency/throughput. Distilled from "Machine Learning Engineering", the latest version of which can be found at https://github.com/stas00/ml-engineering The latest SKILL.md version can be found at https://github.com/st
io.github.mem0ai/mem0/mem0-integrate
以目标驱动、TDD 的流程将 Mem0 集成到现有仓库。自动检测仓库语言,并让用户在 Mem0 Platform(托管)与 Mem0 开源(自托管)之间选择。先写失败测试再做实现。产出本地功能分支和供配套验证技能使用的 .mem0-integration/ 产物。触发条件:用户说 "integrate mem0"、"add mem0 to this repo"、"wire mem0 into <repo>",或询问如何在现有项目中加入记忆。不触发:一般 SDK 使用(用 skill:mem0)、CLI 使用(用 mem0-cli)、Vercel AI SDK(用 skill:mem0-vercel-ai-sdk)。成功后调用 skill:mem0-test-integration 在同一工作区验证(松耦合)。
io.github.mem0ai/mem0/mem0-cli
Mem0 CLI——mem0 记忆操作的命令行界面。触发条件:用户提到 "mem0 cli"、"mem0 command line"、"@mem0/cli"、"mem0-cli"、"pip install mem0-cli"、"npm install -g @mem0/cli",或在终端/shell 中运行 mem0 命令(mem0 add、mem0 search、mem0 list、mem0 get、mem0 init、mem0 config、mem0 import)。查询包含 --user-id、--output、--json、--agent 等 CLI 参数,或描述 bash/zsh/终端使用时也触发。不触发:Python/TS 代码中的 SDK 集成(用 mem0 技能),或 Vercel AI SDK provider(用 mem0-vercel-ai-sdk)。
io.github.mem0ai/mem0/mem0-vercel-ai-sdk
Mem0 为 Vercel AI SDK 提供的 provider(@mem0/vercel-ai-provider)。触发条件:用户提到 "vercel ai sdk"、"@mem0/vercel-ai-provider"、"createMem0"、"retrieveMemories"、"addMemories"、"getMemories"、"searchMemories"、"mem0 vercel"、"AI SDK provider"、"AI SDK memory",或在 generateText/streamText 中使用 mem0。也适用于需要记忆增强 AI 的 Next.js 应用。不触发:不经 Vercel 的直接 Python/TS SDK 调用(用 mem0 技能),或 CLI 终端命令(用 mem0-cli 技能)。
io.github.topoteretes/cognee/company-brain-company-qa
Build one company memory from a SQL database, a support ticket export and a folder of documents, linked into one graph by cognee, then answer questions that need several of those sources together. Use when someone wants to connect their company's database, tickets and docs, or asks who owns, decided or is handling something across them. Runs locally; reads the sources, writes only to cognee's memory.
io.github.thedaviddias/Front-End-Checklist/back-forward-cache
审查导航性能、浏览器生命周期事件或内存恢复行为时使用。检查真实的浏览器生命周期与恢复路径，而不是只看静态代码模式。
io.github.thedaviddias/Front-End-Checklist/cascade-layers
审查存在优先级问题的 CSS 架构、搭建设计系统，或把大型代码库从 utility-first 迁移到分层架构时使用。
io.github.thedaviddias/Front-End-Checklist/content-without-css
审查可能依赖表现层传递语义的渲染 HTML、布局组件或设计系统模式时使用。检查 DOM 顺序、语义结构、表单关联，以及 CSS 生成内容是否承载了关键信息。
io.github.thedaviddias/Front-End-Checklist/css-at-property
实现动画渐变、涉及自定义属性取值的复杂 CSS transition，或构建类型化设计令牌系统（希望自定义属性误用能直接报错）时使用。
io.github.nodejs/node/v8-understanding
Guides the methodology for investigating V8 concepts, codebase structure, performance, memory, and architecture. MANDATORY to use whenever triaging bugs, investigating crashes, reading C++ files, analyzing compiler optimizations (Ignition, Maglev, TurboFan, Turboshaft), or doing static research on V8 subsystems. Essential for locating high-level design documents in the local docs/ directory before reading C++ code. Do not use for raw build commands or Git workflows.
io.github.The-Vibe-Company/quivr/mermaid-diagrams
Comprehensive guide for creating software diagrams using Mermaid syntax. Use when users need to create, visualize, or document software through diagrams including class diagrams (domain modeling, object-oriented design), sequence diagrams (application flows, API interactions, code execution), flowcharts (processes, algorithms, user journeys), entity relationship diagrams (database schemas), C4 architecture diagrams (system context, containers, components), state diagrams, git graphs, pie charts, gantt charts, or any other diagram type. Triggers include requests to "diagram", "visualize", "model", "map out", "show the flow", or when explaining system architecture, database design, code structure, or user/application flows.
io.github.K-Dense-AI/scientific-agent-skills/dask
Scales pandas, NumPy, and custom Python research workflows beyond memory or across clusters with Dask. Covers DataFrames, Arrays, Bags, Futures, chunking, schedulers, and distributed diagnostics. Use for partitioned file processing, scientific array computation, or parallel tasks whose memory and dependency structure require Dask.
io.github.K-Dense-AI/scientific-agent-skills/optimize-for-gpu
将科学 Python 工作负载加速到 NVIDIA GPU 上，并验证结果正确且更快。适用于 CUDA/GPU 优化；CPU 受限的 NumPy、SciPy、pandas、scikit-learn、NetworkX、scikit-image、向量检索、图像处理、图、仿真或文件 I/O 负载；CuPy、cuDF、cuML、cuGraph、cuVS、cuCIM、KvikIO、Warp、Newton、Numba-CUDA、RAFT 相关问题；以及性能剖析、内存搬运、kernel 或多 GPU 瓶颈。当大规模数据并行的 Python 代码很慢且 GPU 加速是合理选项时也应使用，即使用户没有提到 CUDA。
io.github.thedotmack/claude-mem/cloud-sync
设置或检查 claude-mem 与 cmem.ai Pro 的云同步。当用户说「set up cloud sync」「sync my memories」「cmem pro」「cloud backup」「sync status」，或希望备份/同步记忆数据库到其 cmem.ai 账户时使用。