Skill UI

A powerful tool designed to monitor blockchain mempools, analyzing pending transactions, gas price fluctuations, detecting DEX swaps, and identifying potential MEV opportunities. It is essential for advanced crypto traders, MEV researchers, and protocol developers who need to optimize transaction timing and gas costs across multiple EVM chains (Ethereum, Polygon, Arbitrum, etc.).

A powerful tool designed to monitor blockchain mempools, analyzing pending transactions, gas price fluctuations, detecting DEX swaps, and identifying potential MEV opportunities. It is essential for advanced crypto traders, MEV researchers, and protocol developers who need to optimize transaction timing and gas costs across multiple EVM chains (Ethereum, Polygon, Arbitrum, etc.).

A comprehensive toolkit for App Store Optimization (ASO), designed to boost mobile app visibility and ranking on both Apple App Store and Google Play Store. This guide covers end-to-end strategies, including deep keyword research, competitor analysis, metadata optimization (title, subtitle, description), and conversion rate planning. Use this resource when planning app launches or aiming to improve app store SEO and discoverability.

A comprehensive toolkit for App Store Optimization (ASO), designed to boost mobile app visibility and ranking on both Apple App Store and Google Play Store. This guide covers end-to-end strategies, including deep keyword research, competitor analysis, metadata optimization (title, subtitle, description), and conversion rate planning. Use this resource when planning app launches or aiming to improve app store SEO and discoverability.

Guides teams through discovery, research, moodboards, palettes, typography, and Tailwind-ready code so they can ship distinctive, accessible front-end experiences with process validation.

A comprehensive guide to implementing Exa neural search at various scales. This resource details three distinct architectures—Direct Search, Cached Search, and full RAG Pipeline—allowing developers to choose the optimal blueprint based on traffic volume, required latency, and complexity. Use this when designing scalable Exa integrations, from simple UI search to advanced AI-powered research agents.

This advanced skill is a multi-agent research framework designed for executing deep, extensive research in parallel. It orchestrates a team of 10 specialized agents to gather, synthesize, and analyze information simultaneously. It is ideal for complex topics that require rapid, broad, and battle-tested investigation across multiple dimensions, significantly accelerating the knowledge discovery process.

Create structured moodboards that synthesize inspiration from trend research or site analysis, refining colors, typography, and patterns through iterative user feedback before implementation.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

This guide outlines the comprehensive reference architecture for building advanced, AI-powered search and research applications, modeled after Perplexity. It covers three core tiers: a searchable service with model routing and caching, a citation pipeline for validation and rendering, and an orchestrator for multi-query research synthesis. Use this when designing complex, production-grade, search-augmented LLM integrations or reviewing advanced system patterns.

Guides SaaS teams through value-based pricing, tier design, research techniques (Van Westendorp, MaxDiff) and pricing-page best practices to capture customer willingness to pay and support monetization decisions.

Pyvene is a declarative framework designed for performing causal interventions on PyTorch neural networks. It allows researchers to conduct advanced experiments such as activation patching, causal tracing (ROME-style), and interchange intervention training (IIT). Use this library when you need to test causal hypotheses about model behavior, deeply interpret model components, or ensure reproducibility in advanced AI research.