Total 50,611 skills, AI & Machine Learning has 8484 skills
Showing 12 of 8484 skills
Create event-driven hooks for Claude Code automation. Use when the user wants to create hooks, automate tool validation, add pre/post processing, enforce security policies, or configure settings.json hooks. Triggers: create hook, build hook, PreToolUse, PostToolUse, event automation, tool validation, security hook
Guidance for implementing tensor parallelism in PyTorch, including ColumnParallelLinear and RowParallelLinear layers. This skill should be used when implementing distributed tensor parallel operations, sharding linear layers across multiple GPUs, or simulating collective operations like all-gather and all-reduce for parallel computation.
Diagnose thinking failures and maintain process-sovereignty by auditing cognitive orientation and operation balance.
Use this skill for ANY question about CREATING evaluators. Covers creating custom metrics, LLM as Judge evaluators, code-based evaluators, and uploading evaluation logic to LangSmith. Includes basic usage of evaluators to run evaluations.
Xiaohongshu Copy Optimization Agent System. Specialized in optimizing copy for eyewear products on Xiaohongshu, it supports reading content to be optimized and reference materials, and outputs high-conversion notes that comply with platform specifications. Usage scenarios: When users request to optimize Xiaohongshu eyewear copy, generate Xiaohongshu eyewear notes, or need to refer to platform hot words and writing specifications.
Debug and trace C/C++/Rust programs with the GNU Debugger (GDB) without blocking the agent. Use when you need to set tracepoints, inspect variables, or monitor a running process while staying responsive to the user.
Discover and install skills from multiple marketplaces for AI coding agents
A 'skill for creating skills'. This tool automates the entire process of converting any GitHub repository into a standardized Trae skill, and is a core tool for expanding AI Agent capabilities.
This skill provides comprehensive guidance for using the Replicate CLI to run AI models, create predictions, manage deployments, and fine-tune models. Use this skill when the user wants to interact with Replicate's AI model platform via command line, including running image generation models, language models, or any ML model hosted on Replicate. This skill should be used when users ask about running models on Replicate, creating predictions, managing deployments, fine-tuning models, or working with the Replicate API through the CLI.
Interactive wizard for creating Claude Code skills. Use when scaffolding new skills, validating skill structure, managing dependencies, or editing skill configuration files.
Paper reviewer that evaluates machine learning research projects following official ICML reviewer guidelines. Provides comprehensive reviews with actionable feedback across all key dimensions: claims/evidence, relation to prior work, originality, significance, clarity, and reproducibility. Also provides formative feedback on incomplete drafts, proposals, and research code repositories. MANDATORY TRIGGERS: review paper, ICML review, paper review, evaluate paper, research paper feedback, ML paper review, conference review, academic review, paper critique, NeurIPS review, ICLR review, project proposal, research proposal, paper draft, early feedback, incomplete paper, work in progress, WIP review, review repo, review codebase, research project review
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.