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Found 62 Skills
Générez des prompts optimisés pour chaque modèle de génération vidéo IA (Veo 3, Runway Gen-3, Kling 2.6, Pika), en exploitant leurs forces spécifiques. Use when: **Animer des frames de storyboard** - Transformer des images fixes en vidéo; **Choisir le bon modèle** - Sélectionner Veo, Runway, Kling ou Pika selon le besoin; **Optimiser la qualité de génération** - Prompts structurés pour meilleurs résultats; **Créer des transitions fluides** - Scene extension, first/last frame; **Utiliser le mo...
Self-contained design transformer — invoke directly, do not decompose. Transforms a design reference HTML file into a Vibes app. Use when user provides a design.html, mockup, or static prototype to match exactly.
Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.
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, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.
Use this skill when building computer vision applications, implementing image classification, object detection, or segmentation pipelines. Triggers on image classification, object detection, YOLO, semantic segmentation, image preprocessing, data augmentation, transfer learning, CNN architectures, vision transformers, and any task requiring visual recognition or image analysis.
Self-hosted ML coding practice platform with 68 problems covering Transformers, diffusion, RLHF, and more — instant browser feedback, no GPU required.
Développez une idée créative et structurez un script vidéo optimisé pour la génération IA, en suivant la méthode des scènes de 8 secondes de PJ Ace. Use when: **Démarrer une publicité vidéo IA** - Transformer une idée brute en script structuré; **Créer du contenu vidéo pour les réseaux sociaux** - TikTok, Reels, YouTube Shorts; **Développer un concept de campagne** - Avant de passer au storyboard; **Pitcher une idée vidéo** - Présenter un concept à un client ou une équipe; **Adapter un messag...
Use this skill whenever the user is working with AdonisJS v7 backend framework code: controllers, routes, middleware, services, VineJS validators, Transformers, Bouncer policies, events, listeners, mail, cache, queue, exceptions, Ace commands, request/response/session handling, or backend architecture and review. Trigger for "create a controller", "add validation", "create a service", "add a policy", "wire routes", "handle an exception", or AdonisJS backend review/debugging. For Lucid ORM, migrations, schema generation, models, relationships, query builders, transactions, factories, or seeders, use the lucid skill alongside or instead of this one. For Japa tests, use the japa skill. For Inertia frontend patterns, use inertia-react or inertia-vue alongside this one.
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent data leakage and ensure reproducible results. It addresses preprocessing outside pipelines, missing random_state parameters, improper cross-validation, and custom transformers not following sklearn API conventions. Implements proper Pipeline and ColumnTransformer patterns, systematic hyperparameter tuning, and appropriate evaluation metrics.
State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX. Provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. The industry standard for Large Language Models (LLMs) and foundation models in science.
Expert skill for using DeepSeek-OCR, a vision-language model for optical character recognition with context optical compression supporting documents, PDFs, and images.
Integrate TileGym kernels into Hugging Face `transformers` models by replacing the library's submodule(s) and certain class(es)' implementations, and patching certain class(es)' init/forward/load weight methods prior to instantiating models. Used when the user requires integrating TileGym kernels into `transformers` models.