DevOps 与云

hf-mem

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

hf-cloud-sagemaker-production-defaults

Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.

hf-cloud-python-env-setup

Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.

hf-cloud-sagemaker-deployment-planner

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

hf-cloud-serving-image-selection

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

hf-cloud-sagemaker-iam-preflight

Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.

hf-cloud-aws-context-discovery

Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.

huggingface-llm-trainer

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

ml-pipeline

设计并实现生产级机器学习管道基础设施：使用 MLflow 或 Weights & Biases 配置实验跟踪，创建 Kubeflow 或 Airflow DAG 进行训练编排，使用 Feast 构建特征存储模式，部署模型注册表，并自动化重新训练和验证工作流。在构建 ML 管道、编排训练工作流、自动化模型生命周期、实现特征存储、管理实验跟踪系统、设置 DVC 进行数据版本控制、调整超参数或配置 MLOps 工具（如 Kubeflow、Airflow、MLflow 或 Prefect）时使用。

chaos-engineer

设计混沌实验、创建故障注入框架，并组织分布式系统的游戏日演练——生成运行手册、实验清单、回滚流程及事后复盘模板。在需要设计混沌实验、实施故障注入框架或开展游戏日演练时使用。适用于混沌实验、韧性测试、爆炸半径控制、游戏日、反脆弱系统、故障注入、Chaos Monkey、Litmus Chaos等场景。

salesforce-developer

编写和调试Apex代码，构建Lightning Web组件，优化SOQL查询，实现触发器、批处理作业、平台事件以及Salesforce平台上的集成。适用于开发Salesforce应用程序、定制CRM工作流、管理调控器限制、批量处理或设置Salesforce DX和CI/CD流水线。

sre-engineer

定义服务等级目标，创建错误预算策略，设计事件响应流程，开发容量模型，并为生产系统生成监控配置和自动化脚本。在定义SLI/SLO、管理错误预算、构建大规模可靠系统、事件管理、混沌工程、减少琐事或容量规划时使用。

cloud-architect

设计云架构，制定迁移计划，生成成本优化建议，并制定跨 AWS、Azure 和 GCP 的灾难恢复策略。适用于设计云架构、规划迁移或优化多云部署。调用场景包括卓越架构框架、成本优化、灾难恢复、着陆区、安全架构、无服务器设计。

microservices-architect

设计分布式系统架构，将单体应用分解为有界上下文服务，推荐通信模式，并生成服务边界图和弹性策略。适用于设计分布式系统、分解单体应用或实现微服务模式时——包括服务边界、领域驱动设计（DDD）、Saga模式、事件溯源、CQRS、服务网格或分布式追踪。

terraform-engineer

在 AWS、Azure 或 GCP 上使用 Terraform 实现基础设施即代码时使用。适用于模块开发（创建可复用模块、管理模块版本）、状态管理（迁移后端、导入现有资源、解决状态冲突）、提供商配置、多环境工作流以及基础设施测试。

devops-engineer

创建 Dockerfile、配置 CI/CD 流水线、编写 Kubernetes 清单、生成 Terraform/Pulumi 基础设施模板。处理部署自动化、GitOps 配置、事件响应手册和内部开发者平台工具。在设置 CI/CD 流水线、容器化应用、管理基础设施即代码、部署到 Kubernetes 集群、配置云平台、自动化发布或响应生产事件时使用。适用于流水线、Docker、Kubernetes、GitOps、Terraform、GitHub Actions、值班或平台工程。

kubernetes-specialist

在部署或管理 Kubernetes 工作负载时使用。调用以创建部署清单、配置 Pod 安全策略、设置服务账户、定义网络隔离规则、调试 Pod 崩溃、分析资源限制、检查容器日志或调整工作负载大小。用于 Helm Chart、RBAC 策略、NetworkPolicy、存储配置、性能优化、GitOps 流水线以及多集群管理。

mcp-builder

**所有MCP服务器工作的必读内容** - mcp-use框架的最佳实践和模式。 **在进行任何MCP服务器工作之前，请先阅读此内容**，包括： - 创建新的MCP服务器 - 修改现有的MCP服务器（添加/更新工具、资源、提示、组件） - 调试MCP服务器问题或错误 - 审查MCP服务器代码的质量、安全性或性能 - 回答关于MCP开发或mcp-use模式的问题 - 对server.tool()、server.resource()、server.prompt()或组件进行任何更改此技能包含关键的架构决策、安全模式和常见陷阱。在实现MCP功能之前，请务必查阅相关的参考文件。

mcp-use

chatgpt-app-builder

**所有MCP服务器工作的必读内容**——mcp-use框架最佳实践与模式。 **在进行任何MCP服务器工作之前，请先阅读本文**，包括： - 创建新的MCP服务器 - 修改现有MCP服务器（添加/更新工具、资源、提示、组件） - 调试MCP服务器问题或错误 - 审查MCP服务器代码的质量、安全性或性能 - 回答关于MCP开发或mcp-use模式的问题 - 对server.tool()、server.resource()、server.prompt()或组件进行任何更改本技能包含关键的架构决策、安全模式和常见陷阱。在实现MCP功能之前，请务必查阅相关的参考文件。

mcp-use

mcp-apps-builder

**所有MCP服务器工作的必读内容**——mcp-use框架最佳实践与模式。 **在进行任何MCP服务器工作之前，请先阅读本文**，包括： - 创建新的MCP服务器 - 修改现有的MCP服务器（添加/更新工具、资源、提示、组件） - 调试MCP服务器问题或错误 - 审查MCP服务器代码的质量、安全性或性能 - 回答关于MCP开发或mcp-use模式的问题 - 对server.tool()、server.resource()、server.prompt()或组件进行任何更改本技能包含关键的架构决策、安全模式和常见陷阱。在实现MCP功能之前，请务必查阅相关的参考文件。

mcp-use

portless

设置并使用 portless，以命名的本地开发服务器 URL（例如 https://myapp.localhost）替代 http://localhost:3000。在将 portless 集成到项目中、配置开发服务器名称、设置本地代理、使用 .localhost 域名或排查端口/代理问题时使用。

vercel-labs

find-community

9.9Kdevops-cloud

Help identify and evaluate communities to build a minimalist business around. Use when someone is looking for a business idea, trying to find their community, or wondering where to start as an entrepreneur.

slavingia

web-video-presentation

9.8Kdevops-cloud

把一篇文章或口播稿，做成"看起来像视频"的点击驱动 16:9 网页演示，可选合成口播音频。流程：原始文章 → **一次产出**口播稿 + outline 开发计划 → 用户**一次对齐** 5 件事（稿子 / outline / 主题 / 素材 / 开发模式）→ 网页开发（逐章 / 顺序 / 并行）→ 可选音频合成（provider-agnostic：内置 MiniMax mmx-cli + OpenAI TTS，可换 ElevenLabs / edge-tts / Azure / 自带 TTS）。**outline 只规划节奏与信息密度，不规划动画** —— 动画由章节开发时按 PRINCIPLES + ANTI-AI 法则即时设计。每次点击推进口播稿的一个节拍，每一步独占整屏，进度条平时隐藏只在悬浮时出现。适用场景：用网页做视频（动态 PPT 但不像 PPT）、把口播稿 / 文章变成可交互的解说、为 B 站 / YouTube / 视频号录屏教程、做有电影感的产品 / talk demo。本 Skill 沉淀的是设计方法论 + 协作流程 —— 不绑定任何特定样式 / 字体 / 颜色 —— 因此能复用到任意主题与美学。

conardli