数据与 AI技能

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications.

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Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python

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Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.

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Expert guidance for LlamaIndex development including RAG applications, vector stores, document processing, query engines, and building production AI applications.

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Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

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Expert in JAX for high-performance numerical computing and machine learning

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Run Hugging Face models in JavaScript or TypeScript with Transformers.js in Node.js or the browser.

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Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.

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LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

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PyTorch deep learning development with transformers, diffusion models, and GPU optimization.

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MLflow, model versioning, experiment tracking, model registry, and production ML systems

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You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati

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Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

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Get reliable JSON, enums, and typed objects from LLMs using response_format, tool_use, and schema-constrained decoding across OpenAI, Anthropic, and Google APIs.

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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natur

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Large Language Model development, training, fine-tuning, and deployment best practices.

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LLMs, prompt engineering, RAG systems, LangChain, and AI application development

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This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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Design and implement a complete ML pipeline for: $ARGUMENTS

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Machine learning development with JAX, functional programming patterns, and high-performance computing.

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Supervised & unsupervised learning, scikit-learn, XGBoost, model evaluation, feature engineering for production ML

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分析健康目标数据、识别目标模式、评估目标进度,并提供个性化目标管理建议。支持与营养、运动、睡眠等健康数据的关联分析。

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Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.

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