Skill UI

This skill facilitates the crucial process of sharing learned intelligent patterns across isolated projects or machines using the InterPlanetary File System (IPFS). It allows users to publish a project's accumulated knowledge base to a decentralized network and later fetch, apply, and merge these patterns into new or existing projects. This is invaluable for bootstrapping new applications with prior knowledge or consolidating learnings across a large monorepo.

This skill provides robust automation capabilities for interacting with network devices via SSH using the industry-standard Netmiko library. It is designed to simplify complex network operations, such as configuration backups, status checks, and bulk management across heterogeneous devices, significantly improving operational efficiency.

This tool provides comprehensive diagnostics for Border Gateway Protocol (BGP) within network environments. It helps engineers troubleshoot connectivity issues, verify routing paths, and diagnose complex peering problems across large-scale networks. Ideal for network operations and advanced troubleshooting.

This tool is designed to validate the syntax, completeness, and correctness of network configuration files. It helps ensure that network devices are configured according to established standards and best practices, preventing deployment errors and ensuring reliable network operation. Ideal for network engineers and DevOps teams.

A comprehensive guide for diagnosing physical and logical network failures, such as CRC errors, packet drops, duplex mismatches, and flapping. It teaches users how to analyze interface counters and trends across routers, switches, and Linux hosts to pinpoint the root cause of connectivity issues.

Pilot Protocol is an open-source overlay network providing AI agents with first-class network citizenship. It offers permanent virtual addresses, encrypted P2P communication, NAT traversal, and a structured app store. Use this skill when agents require stable networking across cloud changes, need direct, secure messaging without shared accounts, or must integrate structured external data services (e.g., live crypto prices) via local, typed capabilities.

This skill provides guidance on implementing common architectural patterns and best practices within the PyTorch framework. It is designed for developers building sophisticated deep learning models, helping to improve structure, efficiency, and code maintainability when working with neural networks.

This guide provides comprehensive patterns for behavior-focused testing of React components, custom hooks, and pages. It covers best practices using React Testing Library (RTL), including accessibility assertions, simulating user interactions (userEvent), and network mocking with MSW. It also helps developers define clear test boundaries between unit component tests and full E2E flows.

An advanced tool for network intelligence that calculates weighted graph scores to map the optimal path to a target. It identifies potential introducers, measures 'bridge value' across connections, and helps determine whether a connection requires a warm introduction or direct outreach on platforms like X and LinkedIn. Ideal for users needing the raw ranking methodology.

This comprehensive guide demonstrates how to achieve highly testable Swift code using protocol-based dependency injection. By abstracting external concerns—such as file systems, networking, and external APIs—into small, focused protocols, developers can write deterministic unit tests that mock side effects and error paths, ensuring robust architecture across different environments (App, Test, Preview).

This tool generates sophisticated trading signals by leveraging an advanced anomaly detection engine. It scans financial assets for various market anomalies—such as spikes, drifts, and oscillations—and classifies them. It then integrates neural network prediction and searches historical pattern databases to output highly ranked, actionable trading signals, complete with confidence scores and target parameters.

A comprehensive tool for training and evaluating advanced neural network models (including LSTM, Transformer, and N-BEATS) on time-series market data. It facilitates the prediction of financial trends, generating results with confidence intervals, and comparing performance across multiple architectures. Ideal for quantitative trading research and financial forecasting.