Skill UI

This skill helps reduce LLM API and infrastructure costs by 50-90% using strategies like model selection, prompt caching, batching, quantization, and self-hosting. It covers cost tracking, right-sizing models for tasks, provider-side caching, and semantic caching to avoid redundant calls. Suitable for developers managing AI spend and optimizing cloud infrastructure.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

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.

This skill provides a comprehensive audit framework and optimization strategy for local SEO, specifically tailored for law firms, attorneys, and legal/professional service providers. It focuses on maximizing local visibility by optimizing Google Business Profiles (GBP), establishing critical E-E-A-T signals, and ensuring directory consistency. Use this when aiming to improve ranking in local Google packs, maps, and specialized legal directories.

An expert guide for deploying and optimizing Large Language Models (LLMs) entirely on local hardware. Mastering the entire local AI ecosystem, including inference engines like Ollama, vLLM, and llama.cpp. Provides deep knowledge on quantization formats (GGUF, EXL2, AWQ), VRAM calculation, and ensuring privacy-first, offline deployment for state-of-the-art models.

This skill provides quantitative analysis frameworks and CLI indicator scripting via Longbridge. It supports pairs trading, volatility strategies, multi-factor models, and statistical methods like ADF/GARCH. Users can run scripts against K-line data for strategy optimization, hedging, and ML-based prediction without requiring login. Ideal for quantitative finance workflows.

This skill provides comprehensive guidance for building and debugging user interfaces using Makepad 2.0 widgets. It covers the complete widget catalog including containers, text, buttons, inputs, media, and navigation components. It also includes critical coding rules for layout, styling, and performance optimization to ensure correct UI rendering.

Mason is a specialized coding agent responsible for implementing production-ready code based on architectural blueprints and checklists. He ensures clean, functional code that adheres to security standards, linting rules, and defined folder structures. Mason writes testable code, avoids premature optimization, and follows strict layering rules. Use this skill when you need to build software features that match existing designs and are ready for code review and QA testing.

A specialized skill for achieving asymptotic performance gains when classical algorithms are already near optimal. It leverages advanced mathematical techniques and probabilistic data structures (e.g., Bloom Filters, HyperLogLog, FFT, MinHash/LSH) to handle massive datasets (n ≥ 10⁶) by accepting bounded approximations or exploiting deep structural properties. Requires strict adherence to error parameter and complexity justification protocols.

Max is a specialized agent responsible for cleaning up and improving existing code without altering its behavior. It focuses on algorithmic optimization, code abstraction, dead code removal, and readability improvements. Max ensures all changes maintain test suite integrity and follows strict refactoring rules. It is invoked only when explicitly requested to refactor proven code, ensuring safety and stability in software development workflows.

Master Minecraft server plugin development using Bukkit, Spigot, and Paper APIs. Covers event-driven architecture, performance optimization, internal mechanics, and ecosystem integration for Java-based server plugins.

Expert in building production machine learning systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Covers model serving, feature engineering, training optimization, MLOps, monitoring, and deployment in production environments.