Skill UI

This comprehensive skill covers the full scope of Firebase services, including Authentication, Firestore, Cloud Functions, and Hosting. It goes beyond basic implementation, focusing on critical architectural principles: adopting a query-first, denormalized NoSQL mindset. Key insights include mandatory security rule design, optimal data modeling for performance, and correctly utilizing modular SDKs to build scalable and secure backends.

A comprehensive pipeline for processing, validating, and optimizing scraped web content from Firecrawl. This tool handles markdown cleaning, structured data extraction using Zod validation, content deduplication, and chunking tailored for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It ensures crawled data is standardized, clean, and ready for downstream consumption or knowledge base indexing.

Firecrawl provides a comprehensive API for web data harvesting. It enables users to perform single-page scraping, multi-page crawling, site mapping (URL discovery), and advanced structured data extraction using Large Language Models (LLMs). Ideal for integrating data pipelines, building web monitoring tools, or conducting market research.

Enables Claude to analyze historical time series patterns, select the best forecasting model, and predict future values with confidence intervals for sales, traffic, or other time-dependent metrics.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

This skill guide is essential for building advanced applications using Gemini and Gemma 4 hosted models. It covers working with multimodal content (text, images, audio, video), implementing complex features like function calling and structured output. It provides up-to-date SDK usage examples for Python, JavaScript, Go, and Java, ensuring developers use the latest, recommended API standards.

Generates ORM models and database schemas for frameworks like TypeORM, Prisma, Sequelize, SQLAlchemy, Django ORM, Entity Framework, and Hibernate, translating user requests into ready-to-use model code or schema definitions to accelerate backend development.

Generate complete, production-ready REST API implementations from existing OpenAPI specifications or database schemas. This skill scaffolds all necessary CRUD endpoints (GET, POST, PUT, DELETE) across multiple frameworks (Express, FastAPI, Spring Boot, etc.), automatically incorporating features like pagination, filtering, validation, and robust authentication middleware. Ideal for rapidly bootstrapping complex backend services from defined data models.

A comprehensive guide to Generative Engine Optimization (GEO), shifting content strategy from traditional SEO (Google ranking) to maximizing citation rates within AI search platforms like ChatGPT, Claude, and Perplexity. Learn how to structure content, build authority, and incorporate schema to be the preferred source for large language models.

This guide details the use of the GGUF format and quantization techniques for running large language models (LLMs) efficiently. GGUF enables optimal inference on consumer hardware, Apple Silicon, and systems requiring CPU-only operation. By compressing models using various K-quant methods (like Q4_K_M), developers can significantly reduce memory footprint and hardware requirements while maintaining high performance.

A comprehensive collection of patterns and best practices for automating GitHub workflows using advanced AI models. This skill enables automated PR reviews, sophisticated issue triage (labeling and commenting), and seamless integration of AI into CI/CD pipelines, significantly enhancing DevOps efficiency and code quality assurance.

Automates GPU resource optimization for ML deployments by guiding best practices, generating production-ready configurations, and validating setups for model serving, MLOps pipelines, and monitoring.