Skill UI

Quantizes HuggingFace LLMs to 8-bit or 4-bit with bitsandbytes, cutting memory by up to 75% while keeping accuracy, and supports QLoRA fine-tuning plus 8-bit optimizers for faster, memory-efficient training.

This specialized skill generates comprehensive, best-practice SEO metadata packages, including optimized titles, compelling descriptions, and structured URLs. It ensures strict compliance with character limits (e.g., 150-160 characters) while strategically integrating primary and secondary keywords, emotional triggers, and clear CTAs. Use this when launching new content or needing A/B test variations to maximize search visibility and click-through rates across platforms.

Automates Spark SQL optimizer tasks in data pipelines by guiding best practices, producing production-ready code and configurations, and validating ETL, transformation, orchestration, and streaming workflows.

Automates SQL query optimization guidance for data analytics workflows, offering best practices, pattern advice, and production-ready code for queries, BI, and visualization tasks whenever the user invokes SQL query optimizer support.

Automates Tailwind class optimization for frontend developers, guiding users through best practices, validating CSS patterns, and generating React/Vue-friendly code with accessibility and performance assurances.

Optimizes large language model inference on NVIDIA GPUs with TensorRT, delivering 10-100× faster throughput, quantized precision (FP8/INT4), multi-GPU scaling, and serving-ready tooling for production deployments.

Analyzes tweet drafts through Twitter’s recommendation architecture to rewrite and edit content, explain what the algorithms favor, and surface engagement tactics that align with Real-graph, SimClusters, TwHIN, and Tweepcred signals for broader reach.

Python framework for multi-objective optimization, offering NSGA-II/III, MOEA/D, benchmark suites, customizable operators, and visualization to help engineering teams explore Pareto fronts and make multi-criteria decisions.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

Guides Odoo users through configuring FIFO/AVCO valuation, reordering rules, putaway strategies, warehouse routes and multi-step flows to ensure accurate stock levels and traceable receipts/shipments.

A comprehensive guide and methodology for identifying, measuring, and fixing performance bottlenecks across the entire stack. This skill covers optimizing database queries (e.g., N+1 problems, missing indexes), improving API efficiency (caching, parallel processing), and enhancing frontend performance (memoization, code splitting, image optimization). It emphasizes the critical 'Measure First' principle to ensure all optimizations are data-driven and effective.

Framework for auditing and optimizing GEO content for each AI search platform, scoring Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot with tailored checklists and action steps.