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Build multi-step LLM reasoning chains in n8n using Groq, OpenAI, or Claude for structured data extraction, categorization, scoring, and analysis. Use this skill whenever the user wants to chain multiple LLM calls together in an n8n workflow — phrases like "extract entities then categorize", "multi-step LLM prompt", "chain_llm", "LLM pipeline", "classify and score", "entity extraction then enrichme

Orquestre múltiplos LLMs como um conselho, gerando inteligência coletiva através de revisão por pares e síntese do presidente.

Optimizes text and docs for LLM token efficiency. Use when - optimizing prompts, reducing tokens, compressing text, condensing verbose content, encoding for CLAUDE.md, compressing for context/prompt/LLM. Trigger keywords - optimize, reduce tokens, compress, condense, slim, tighten, too verbose, shrink, encode, deep compress, deep encode, super compress, maximum compress.

Automatize tarefas acionadas por interface web ou por humanos, convertendo-as em loops de CLI e saída de arquivo, permitindo que LLMs iterem de forma independente com logs estruturados e blocos de rascunho. Aplique uma abordagem de "armar ou abandonar": se uma etapa não for repetível, aprimore a automação em vez da supervisão manual. Ideal para trabalhos repetitivos, gatilhos de UI web ou fluxos de trabalho com loops internos dependentes de humanos.

Cheaper and faster than /compact. Restores previous session context by reading transcripts directly — no LLM calls, no token cost

Process CSV data files by cleaning, transforming, and analyzing them. Use this when users need to work with CSV files, clean data, or perform basic data analysis tasks.

Optimize content for AI search engines — ChatGPT, Perplexity, Claude, Gemini, and AI Overviews. Covers entity optimization, structured data, citation-worthy formatting, and platform-specific strategies. Use when someone wants visibility in AI-generated answers, says 'AI SEO', 'AI search', 'LLM optimization', 'ChatGPT ranking', 'Perplexity citations', 'AI Overviews', or wants their content cited by

Optimize content for AI Overviews (formerly SGE), ChatGPT web search, Perplexity, and other AI-powered search experiences. Generative Engine Optimization (GEO) analysis including brand mention signals, AI crawler accessibility, llms.txt compliance, passage-level citability scoring, and platform-specific optimization. Use when user says "AI Overviews", "SGE", "GEO", "AI search", "LLM optimization",

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

Crafting effective prompts for LLMs. Use when designing prompts, improving output quality, structuring complex instructions, or debugging poor model responses.

Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.

Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.