Skill-uri pentru agenți pe care le poți verifica înainte să le instalezi
Skill-uri SKILL.md publice din depozite GitHub cu peste 50 de stele și de la producători, scanate și evaluate. Instalează oricare dintre ele cu o singură comandă în Claude Code, Cursor, Codex, GitHub Copilot sau Gemini CLI.
45.604 skill-uri din 2.205 depozite · actualizat acum 16 ore
45.602 skill-uri
- gws-gmail-sendgoogleworkspace/cliGmail: Send an email.ProductivitateFără avertismente★ 31,9 K↓ —
- recipe-log-deal-updategoogleworkspace/cliAppend a deal status update to a Google Sheets sales tracking spreadsheet.DateFără avertismente★ 31,9 K↓ —
- hf-cloud-sagemaker-deployment-plannerhuggingface/skillsPlan 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.OficialDevOpsFără avertismente★ 11,2 K↓ —
- huggingface-lora-space-builderhuggingface/skillsBuild and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.OficialDesignFără avertismente★ 11,2 K↓ —
- huggingface-local-modelshuggingface/skillsUse to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.OficialMarketingFără avertismente★ 11,2 K↓ —
- huggingface-besthuggingface/skillsUse when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.OficialPlanificareFără avertismente★ 11,2 K↓ —
- huggingface-trackiohuggingface/skillsTrack and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.OficialInginerieFără avertismente★ 11,2 K↓ —
- recipe-save-email-to-docgoogleworkspace/cliSave a Gmail message body into a Google Doc for archival or reference.ProductivitateFără avertismente★ 31,9 K↓ —
- persona-team-leadgoogleworkspace/cliLead a team — run standups, coordinate tasks, and communicate.ProductivitateFără avertismente★ 31,9 K↓ —
- trl-traininghuggingface/skillsPost-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.OficialRedactareFără avertismente★ 11,2 K↓ —Apare și în 1 alt depozit
- gws-modelarmorgoogleworkspace/cliGoogle Model Armor: Filter user-generated content for safety.AlteleFără avertismente★ 31,9 K↓ —
- transformers-jshuggingface/skillsUse Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.OficialMediaFără avertismente★ 11,2 K↓ —Apare și în 1 alt depozit
- huggingface-zerogpuhuggingface/skillsAI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.OficialRedactareFără avertismente★ 11,2 K↓ —
- hf-mcphuggingface/skillsUse Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.OficialAlteleFără avertismente★ 11,2 K↓ —Apare și în 1 alt depozit
- huggingface-datasetshuggingface/skillsUse this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.OficialInginerieFără avertismente★ 11,2 K↓ —
- gws-chat-sendgoogleworkspace/cliGoogle Chat: Send a message to a space.AlteleFără avertismente★ 31,9 K↓ —
- gws-slidesgoogleworkspace/cliGoogle Slides: Read and write presentations.DocumenteFără avertismente★ 31,9 K↓ —
- gws-sheetsgoogleworkspace/cliGoogle Sheets: Read and write spreadsheets.RedactareFără avertismente★ 31,9 K↓ —
- gws-scriptgoogleworkspace/cliManage Google Apps Script projects.AlteleFără avertismente★ 31,9 K↓ —
- gws-peoplegoogleworkspace/cliGoogle People: Manage contacts and profiles.AlteleFără avertismente★ 31,9 K↓ —
- gws-eventsgoogleworkspace/cliSubscribe to Google Workspace events.AlteleFără avertismente★ 31,9 K↓ —
- gws-gmail-replygoogleworkspace/cliGmail: Reply to a message (handles threading automatically).AlteleFără avertismente★ 31,9 K↓ —
- recipe-schedule-recurring-eventgoogleworkspace/cliCreate a recurring Google Calendar event with attendees.ProductivitateFără avertismente★ 31,9 K↓ —
- gtm-strategyphuryn/pm-skillsCreate a go-to-market strategy covering marketing channels, messaging, success metrics, and launch timeline. Use when planning a product launch, creating a GTM plan from scratch, or defining a launch strategy for a new market.MarketingFără avertismente★ 27,4 K↓ —
hf-cloud-sagemaker-deployment-planner
huggingface/skills
OficialDevOpsActualizat acum 3 zile
De la autor
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.
Instalare
npx skills add huggingface/skills --skill hf-cloud-sagemaker-deployment-planner -a claude-code
Se instalează în .claude/skills/hf-cloud-sagemaker-deployment-planner/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Descarcă .zip
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
La ce are acces
Doar instrucțiuniNu am văzut acces la rețeaLicență: APACHE-2.0
Verificări de siguranță pe SKILL.md
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De ce 94
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Licență100
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- SKILL.md