搜索「3D Models」的结果

io.github.jwulff/whisper-mcp

基于 whisper.cpp 的本地音频转写，使用 OpenAI Whisper 模型。

io.github.neo4j-contrib/mcp-neo4j-data-modeling

用于创建图数据模型的简洁 Neo4j MCP 服务器。

ai.smithery/ScrapeGraphAI-scrapegraph-mcp

让语言模型以企业级可靠性执行高级 AI 网页抓取。

io.github.CodeCraftersLLC/local-voice-mcp

使用 Chatterbox TTS 运行本地语音模型，让你的 MCP 客户端具备说话能力。

io.github.shalevshalit/image-recognition-mcp

基于 OpenAI 视觉模型的 AI 图像识别与描述 MCP 服务器。

io.github.shalevshalit/image-recongnition-mcp

基于 OpenAI 视觉模型的 AI 图像识别与描述 MCP 服务器。

io.github.vllm-project/vllm/model-recipes

Find vLLM deployment recipes and configs for LLMs and Hugging Face models by model and hardware. Use for recipe lookup, creation, and updates.

io.github.openai/codex/openai-docs

用于 Codex 模型/定价、定时任务、skills、设置、安装、排障、定制、自动化与自我知识——包括指代 Codex 的“you”“your”“this app”“this coding agent”——以及 OpenAI API/产品与 ChatGPT Work 的问题。也用于模型选型/迁移、prompt 撰写、SDK、Responses、Realtime、agents、evals 以及 Chat/Work/Codex 的对比。仅提到 Codex 的普通应用/软件任务不适用。

io.github.asgeirtj/system_prompts_leaks/openai-docs

Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex.

io.github.asgeirtj/system_prompts_leaks/gemini-api-dev

Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, speech generation (TTS), voice design, voice replication, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. Covers SDK usage and best practices for Gemini models and agents in Python and TypeScript.

io.github.asgeirtj/system_prompts_leaks/gemini-live-api-dev

Use this skill when building real-time, bidirectional streaming applications with the Gemini Live API, or migrating legacy Live models (2.0/2.5/3.1) to Gemini 3.8 Live. Covers WebSocket-based audio/video/text streaming, voice activity detection (VAD), background reasoning (extended thinking), asynchronous function calling, session management, ephemeral tokens, live transcription, and live translation. SDKs covered - google-genai (Python), @google/genai (JavaScript/TypeScript).

io.github.lobehub/lobehub/lobe-design

Single entry for LobeHub UI design work. Routes any instruction — macro ("重做这个后台", "整体打磨一下"), medium ("把列表页的交互和空态补齐") or fine ("这个按钮颜色不对", "修 K-07") — into a business-first, layered pipeline of focused stages: 00 brief → 05 init → 10 decode → 20 direction → 30 structure → 40 flow → 50 visual → 60 system → 70 detail → 75 copy → 80 review. Covers seven scenarios: 0→1 visual pages (landing / portfolio / campaign), polishing existing product UI, admin consoles / dashboards / forms / workflows, design-system unification, screenshot restoration, pre-launch audit, and project bootstrap (AGENTS.md). Grounded in classic analysis models (double diamond, Gestalt, Nielsen heuristics, Kano, peak-end, Fogg, journey map, cognitive load) and in a layered component boundary: take atoms from the host's component library and build business composites yourself. Platform-aware (web / desktop / mobile, read from the project layer): web takes atoms from @lobehub/ui (/form, /pro-table) with cssVar tokens; React Native takes them fro

io.github.davila7/claude-code-templates/fastapi-endpoint

以面试式规划先行，构建生产级 FastAPI 接口：异步 SQLAlchemy、Pydantic v2 模型、用于认证的依赖注入以及 pytest 测试。写代码前先澄清数据模型、认证方式、分页策略与缓存方案。

io.github.skypilot-org/skypilot/skypilot

在启动云端 VM、Kubernetes pod 或 Slurm 作业以承载 GPU/TPU/CPU 负载、在云端 GPU 上训练或微调模型、部署带自动扩缩的推理服务（vllm、TGI 等）、编写或调试 SkyPilot 任务 YAML、用 spot/抢占式实例节省成本、跨云比较 GPU 价格、统一管理 25+ 云服务以及 Kubernetes、Slurm 与本地集群并在其间故障切换、排查资源可用性或 SkyPilot 错误、优化成本与 GPU 可用性，或迁移既有 Slurm 工作负载（把 sbatch 脚本、salloc 会话、job array 或 srun 调用转换为 SkyPilot 任务 YAML，并把 Slurm 命令、指令与 SLURM_* 环境变量映射到对应的 SkyPilot 写法）时使用。

io.github.alirezarezvani/claude-skills/senior-ml-engineer

面向将模型产品化的 ML 工程技能，涵盖 MLOps 流水线构建与 LLM 集成。包括模型部署、特征存储、漂移监控、RAG 系统与成本优化。当用户询问向生产部署 ML 模型、搭建 MLOps 基础设施（MLflow、Kubeflow、Kubernetes、Docker）、监控模型性能或漂移、构建 RAG 流水线，或集成带重试逻辑与成本控制的 LLM API 时使用。聚焦生产与运维，而非模型研究或初始训练。

io.github.fastapi/fastapi/fastapi

FastAPI 最佳实践与约定。在处理 FastAPI 接口、Pydantic 模型、依赖、包含 Server-Sent Events（SSE）的流式响应，以及托管前端应用时使用。让 FastAPI 代码保持整洁并跟上最新的特性与模式。

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.browser-use/browser-use/open-source

基于 browser-use 开源库编写 Python 代码的文档参考。凡涉及 Agent、Browser、Tools 配置、导入 browser_use 的代码、@sandbox 部署、支持的 LLM 模型、Actor API、自定义 tools、lifecycle hooks、MCP 服务器搭建、Laminar 或 OpenLIT 监控/可观测性，以及 browser-use 安装、prompt 策略、敏感数据处理时都适用。不适用于 Cloud API/SDK 用法与定价——用 cloud skill；也不适用于通过 CLI 命令直接驱动浏览器——用 browser-use skill。

io.github.K-Dense-AI/scientific-agent-skills/gtars

在 Python、Rust 与 CLI 中使用 Gtars 完成本地基因组区间模型与集合代数、重叠与计数、consensus 与覆盖度、tokenization、片段处理，以及 refget/BEDbase 规划。

io.github.K-Dense-AI/scientific-agent-skills/modal

Modal 是按需运行 Python（含按需 GPU）的无服务器云平台。在使用 Modal SDK 部署或托管 AI/ML 模型、运行 GPU 加速负载（训练、微调、推理）、托管 Web 端点、调度批处理任务，或将 Python 代码扩展到云容器时使用。

io.github.coollabsio/coolify/infer-conventions

Use this skill to analyze how a Laravel application is actually written and record its conventions as shared rules. Trigger when the user wants to detect, infer, document, or standardize project conventions or coding style, set up or grow `.ai/rules`, resolve mixed or conflicting patterns (e.g. "are we using Form Requests or inline validation?"), or onboard agents and teammates to "how we do things here". Covers: a systematic sweep of ~49 Laravel convention dimensions (validation, models, architecture, testing, frontend, database, console), open-ended house-pattern discovery, conflict reporting, and recording rules scoped to the right paths via the Boost `record-rule` MCP tool. Only run this skill when the user explicitly asks for it; never start a sweep as part of another task. Do not use for one-off code review, enforcing formatting a linter already handles, or editing `.ai/rules` files by hand.

io.github.coollabsio/coolify/laravel-best-practices

在编写、审查或重构 Laravel PHP 代码时使用。包括创建或修改控制器、模型、迁移、表单请求、策略、任务、定时命令、服务类与 Eloquent 查询。涉及 N+1 与查询性能、缓存策略、授权与安全模式、校验、错误处理、队列与任务配置、路由定义和架构决策时触发。也用于 Laravel 代码审查及将现有 Laravel 代码重构至最佳实践。覆盖一切 Laravel 后端 PHP 代码模式的任务。

skillsmp.stas00-ml-engineering-skill-md

端到端训练与运维大规模 LLM/VLM/多模态模型的实战方法论与具体配方——选择并基准测试加速器、存储与网络；SLURM/Kubernetes 编排；最大化训练吞吐并在显存内装下模型；诊断并撑过训练不稳定、NaN/Inf 与硬件/作业故障；检查点与容错；推理性能与显存；调试多节点/多卡挂起；以及编写/运行测试。当用户在训练或微调大模型、遭遇低 TFLOPS/MFU、OOM、数据加载缓慢、损失尖峰/发散、NCCL/InfiniBand 或多节点挂起、节点/GPU 故障、检查点或抢占问题、存储/网络瓶颈，或需要挑选 GPU/云/文件系统，或估算推理延迟/吞吐时使用。提炼自《Machine Learning Engineering》，最新版本见 https://github.com/stas00/ml-engineering。

io.github.sickn33/agentic-awesome-skills/model-serving-kubernetes

Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server.