Total 31,376 skills, AI & Machine Learning has 5079 skills
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Complete configuration reference for GrepAI. Use this skill when you need to understand all available configuration options.
Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
Configure GOB local file storage for GrepAI. Use this skill for simple, single-machine setups.
Build AI agents with Pydantic AI (Python) and Claude SDK (Node.js)
Design and coordinate multi-agent systems where specialized agents work together to solve complex problems. Covers agent communication, task delegation, workflow orchestration, and result aggregation. Use when building coordinated agent teams, complex workflows, or systems requiring specialized expertise across domains.
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
Integrate GrepAI with Claude Code via MCP. Use this skill to enable semantic code search in Claude Code.
Initialize GrepAI in a project. Use this skill when setting up GrepAI for the first time in a codebase.
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
Upstash Vector DB setup, semantic search, namespaces, and embedding models (MixBread preferred). Use when building vector search features on Vercel.
This skill should be used whenever users need help planning trips, creating travel itineraries, managing travel budgets, or seeking destination advice. On first use, collects comprehensive travel preferences including budget level, travel style, interests, and dietary restrictions. Generates detailed travel plans with day-by-day itineraries, budget breakdowns, packing checklists, cultural do's and don'ts, and region-specific schedules. Maintains database of preferences and past trips for personalized recommendations.