Total 30,584 skills, AI & Machine Learning has 4942 skills
Showing 12 of 4942 skills
Supervised & unsupervised learning, scikit-learn, XGBoost, model evaluation, feature engineering for production ML
Interactive wizard for creating Claude Code skills. Use when scaffolding new skills, validating skill structure, managing dependencies, or editing skill configuration files.
Guidelines for creating high-quality datasets for LLM post-training (SFT/DPO/RLHF). Use when preparing data for fine-tuning, evaluating data quality, or designing data collection strategies.
Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.
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...
Transformez une image 2D en modèle 3D animé prêt pour le web ou le jeu en moins de 30 minutes, en utilisant le workflow Dilum Sanjaya (Hunyuan3D + Mixamo). Use when: **Créer un personnage 3D pour un site web** - Mascotte, avatar, illustration interactive; **Prototyper un asset de jeu** - Character design, props, environnements; **Produire du contenu marketing 3D** - Produits rotatifs, personnages animés; **Convertir des illustrations existantes** - Logo, mascotte, character design → 3D; **Tes...
Use when the user says /bye, "wrap up", "end session", or similar. Reconstructs full session history including compacted context, creates a sessionlog, commits changes, and summarizes next steps.
Use when "training LLM", "finetuning", "RLHF", "distributed training", "DeepSpeed", "Accelerate", "PyTorch Lightning", "Ray Train", "TRL", "Unsloth", "LoRA training", "flash attention", "gradient checkpointing"
Evaluates agent skills against Anthropic's best practices. Use when asked to review, evaluate, assess, or audit a skill for quality. Analyzes SKILL.md structure, naming conventions, description quality, content organization, and identifies anti-patterns. Produces actionable improvement recommendations.
Build single-agent and multi-agent systems using Google's Agent Development Kit (ADK) in Python, Java, Go, or TypeScript. Use when creating AI agents with ADK, designing multi-agent architectures, implementing agent tools, configuring agent callbacks, managing agent state, orchestrating sequential/parallel/loop agent workflows, or when the user mentions ADK, google-adk, google agent development kit, agentic AI with Gemini, or agent orchestration with Google tools. Also use when setting up ADK projects, writing agent tests, deploying agents, or integrating MCP tools with ADK.
TasteRay API integration for personalized recommendations across verticals (movies, restaurants, products, travel, jobs). Use when you need to: (1) recommend movies, restaurants, products, travel, or jobs, (2) answer "what would I like" questions, (3) provide personalized recommendations based on preferences, (4) rank or score items for a user, (5) explain why something matches a user's taste, (6) build recommendation context from conversation, (7) integrate psychological profiles with recommendation systems.
Analyzes errors, searches past solutions in memory, provides immediate fixes with code examples, and saves solutions for future reference. Use when user says "debug this", "fix this error", "why is this failing", or when error messages appear like TypeError, ECONNREFUSED, CORS, 404, 500, etc.