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.
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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
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,
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Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotli
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Comprehensive Azure AI skill for building, configuring, troubleshooting, and managing all Azure AI services. Covers Azure AI Foundry, Azure OpenAI Service, Azure AI Search, Azure AI Agents, Document I
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
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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;
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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
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
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
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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
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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
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GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants
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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
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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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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
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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
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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
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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
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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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Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user ment
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Automate self-hosting of open-source apps on cloud infrastructure the user owns. Use when the user asks to "self-host", "deploy to my own cloud", "install X on AWS / Lightsail / EC2 / Azure / Hetzner
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Curated open-source AI catalog reference. Auto-activates when llm-architect, content-marketer, data-analyst, mcp-developer, backend-developer, or any agent needs to RECOMMEND an open-source AI tool, m
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Build production document ingestion pipelines with chunking, embedding, and vector DB storage. Activate on: document ingestion, chunking strategy, embedding pipeline, vector DB ingestion, RAG indexing
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Use when designing or fixing the retrieval side of a RAG system, choosing chunking strategy (fixed-size / recursive / semantic), implementing hybrid search (BM25 + dense) with RRF fusion, adding a cro
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Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models, building orchestration workflows with templating/filtering
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Use when the user is doing AI/ML work in a scientific domain — biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, energy, conservation, engineering, mathe
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LLM-OPS -- IA de Producao workflow skill. Use this skill when the user needs LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qual
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