Skill UI

This tool automates the creation of new, sequentially numbered database migration files, including both 'up' (forward) and 'down' (rollback) SQL scripts. It intelligently selects the appropriate SQL template based on the migration name (e.g., create_, add_, drop_) to ensure correct schema evolution and includes steps for metadata storage and pattern searching.

Implements the DISTILL and CONSOLIDATE phases of the intelligence pipeline. This tool enables the training and consolidation of learned neural patterns. It supports various adaptation methods, including single-domain SONA for real-time learning and multi-domain MicroLoRA for robust, long-term knowledge storage, preventing catastrophic forgetting.

This guide addresses the critical difference between standard Node.js SHA3-256 and the Keccak-256 hash function required by Ethereum. Using the wrong hash function can silently corrupt calculated function selectors, event topics, storage slots, and derived addresses. It provides best practices and code examples for using dedicated libraries (like ethers, viem, web3.js) to ensure correct, Ethereum-compliant hashing in JavaScript and TypeScript environments.

This pattern demonstrates how to build a robust, thread-safe data persistence layer in Swift using the 'actor' model. It combines fast in-memory caching with reliable file-backed storage, automatically eliminating data races and the need for manual locks. Ideal for offline-first applications and managing shared mutable state in modern Swift concurrency.

This skill provides a comprehensive workflow for training advanced neural network models, including LSTM, Transformer, and N-BEATS, using historical market data. It facilitates model training, prediction generation with confidence intervals, performance comparison across different model types, and the structured storage of results for further analysis and SONA training.