AGENT SKILLS · EQUIPO DE MISIÓN
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- Skills
- 23.248
- repositorios
- 3589
- última sincronización
- 4 nov 2026
huggingfacehf-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 regi
- Instalaciones
- —
- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehf-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
- Instalaciones
- —
- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehf-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 thi
- Instalaciones
- —
- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehf-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 SageMa
- Instalaciones
- —
- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehf-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 createendpoint, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deployic.py for real-time endpoints that scale to zero instances via inference components, and deployasync.py for async endpoints (a
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehf-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 Lear
- Instalaciones
- —
- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehf-mcp
Use 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.
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehf-mem
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
- Instalaciones
- —
- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-best
Use 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 ski
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-gradio
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-local-models
Use 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.
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-lora-space-builder
Build 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 t
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-paper-publisher
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-papers
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-spaces
Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with hf spaces …, @spaces.GPU, Space README frontmatter, or the spaces Python package.
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-tool-builder
Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-vision-trainer
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Tr
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacehuggingface-zerogpu
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses @spaces.GPU, configuring pythonversion 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 sk
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacetrain-sentence-transformers
Train or fine-tune sentence-transformers models across SentenceTransformer (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), CrossEncoder (reranker, pair scoring for two-stage retrieval / pair classification), SparseEncoder (SPLADE, sparse embedding model for learned-sparse retrieval), and MultiVectorEncoder (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-ne
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacetransformers-js
Use 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.
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
huggingfacetrl-training
Post-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.
- Instalaciones
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- GitHub Stars
- 11,3 mil
- Actualizado
- 30 oct
nexu-ioarticle-magazine
Substack / Medium 高级感长文排版, 适合公众号、博客发布
- Instalaciones
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- GitHub Stars
- 9,4 mil
- Actualizado
- 13 oct
nexu-ioarticle-sketchnote-editorial
把一个概念铸成杂志专题档案——真问题→失败→转折→顿悟→命名, 6 个 layout 模具 + 4 字族对比 + 探案档案细节
- Instalaciones
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- GitHub Stars
- 9,4 mil
- Actualizado
- 13 oct
nexu-ioblog-post
杂志感长文, 含 masthead、hero、figures、pull quote、作者署名
- Instalaciones
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- GitHub Stars
- 9,4 mil
- Actualizado
- 13 oct