Model training and fine-tuning, RAG and embedding pipelines, evaluation harnesses, agent scaffolding, and inference optimization. Skills for the engineers building with models, not just calling them.
Related searches: claude code skills for ML engineers, AI engineer skills claude, claude RAG pipeline skills, claude code machine learning skills.
Senior ML engineer for machine learning model selection, training, evaluation, feature engineering, LLM integration, retrieval systems, data pipelines, and deploying AI features at Rihal scale. Activa
engineering
Build data pipelines, AI systems, and machine learning models with Python. USE THIS for data processing, model training, LLM integration, RAG systems, NLP, vector databases, prompt engineering, knowle
engineering
Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Run
engineering
Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis,
engineering
This skill should be used when the user asks to "build with DSPy", "create a DSPy module", "optimize prompts", "build a RAG system", "create an AI agent with DSPy", "use declarative LM programming", "
general
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotli
engineering
Comprehensive reference for the LangChain ecosystem including LangChain, LangGraph, and Deep Agents for Python 3.10+. Use when the user asks to build AI agents, implement RAG pipelines, configure chat
engineering
Enterprise Machine Learning specialist with TensorFlow 2.20.0, PyTorch 2.9.0, Scikit-learn 1.7.2 expertise. Master AutoML, neural architecture search, MLOps automation, and production ML deployment. B
science
Build multimodal AI applications with Pixeltable — declarative tables replace LangChain + pandas + vector DB with one system. Automates chunking, embedding, retrieval, tool-calling agents, and 25+ AI
engineering
Use when a phase involves LLMs, AI agents, RAG, ML inference, or prompt/tool integration design
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Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache;
security
Comprehensive data science, machine learning, and AI guide covering Python, deep learning, NLP, LLMs, prompt engineering, and MLOps. Use when building AI models, data pipelines, or machine learning sy
engineering
Automatically discover machine learning and AI skills when working with machine learning, PyTorch, training, inference, RAG, embeddings, fine-tuning, LLM, DSPy, HuggingFa — from majiayu000/claude-skil
science
ML Engineer role: LLM APIs (OpenAI, Claude, Gemini), embeddings, RAG pipelines, fine-tuning, LangChain, LlamaIndex, vector databases (Pinecone, Chroma, Weaviate), prompt engineering, model evaluation,
engineering
Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI ChatClient, prompt templates, embeddings, vector stores, and structured output. Use
engineering
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use when building LLM features, chatbot — from majiayu000/claude-skil
engineering
TRIGGER when working with ai-sdk which is Laravel official first-party AI SDK. Activate when building, editing AI agents, chatbots, text generation, image generation, audio/TTS, transcription/STT, emb
tools
Pattern AI/LLM: API Anthropic/OpenAI, streaming, RAG, prompt engineering, Vercel AI SDK. Trigger: "ai", "llm", "claude api", "openai", "rag", "embedding", "streaming chat"
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Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / mat
engineering
Griptape is a modular Python framework for building AI agents and workflows with chain-of-thought reasoning, tools, and memory. It provides Agents, Pipelines, and Workflows as core structures, with pl
engineering
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitti — from majiayu000/claude-skil
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Guides instrumentation of GenAI/LLM applications with OpenTelemetry for Honeycomb, including content capture and agent failure detection. Trigger phrases: "instrument my GenAI app", "add tracing to LL
general
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, c
engineering
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R — from ricardonevesbraga/flowgr
science
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
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Comprehensive practitioner reference for the 2025–2026 AI/ML landscape covering frontier model selection and routing, open-weight vs hosted API trade-offs, reasoning models and test-time compute, RAG
engineering
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimiz
general
Cloudflare platform knowledge — Workers, Pages, R2, D1, KV, Durable Objects, AI, and Zero Trust. PROACTIVELY activate for: (1) Cloudflare Workers (handlers, bindings, wrangler), (2) Cloudflare Pages a
engineering
Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, traini
security
Use whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots, agents), computer vis
general
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