Skill UI

Master advanced error capture and context enrichment using Sentry SDKs for both TypeScript and Python. This skill teaches how to reliably capture exceptions, track non-exception events (messages), and enrich reports with structured data like user context, tags, breadcrumbs, and custom fingerprints. It is essential for dramatically improving the ability to diagnose complex production issues quickly.

This skill provides a comprehensive walkthrough for setting up and testing Sentry error monitoring using both Node.js and Python SDKs. It demonstrates how to capture various event types, including simple messages and full exceptions, and crucially, how to enrich these events with user context, searchable tags, and detailed breadcrumbs. This is essential for verifying robust error logging and monitoring in a production environment.

This skill guides you through advanced integration patterns to connect Sentry with your full observability stack. Learn how to correlate error events from structured loggers (like Winston/Pino) using Event IDs, track business metrics (counters, gauges) within Sentry, and link performance data across multiple tools (Grafana, Datadog). Achieve end-to-end visibility, ensuring every error is tied back to its root cause and business context.

A comprehensive guide to production-ready error tracking using Sentry SDK in TypeScript and Python. Learn how to implement structured error context via scopes, manage user journeys with breadcrumbs, filter out noise and PII using beforeSend/beforeBreadcrumb hooks, and use custom fingerprinting for accurate issue grouping. Essential for robust observability.

This comprehensive guide outlines the best practices and structured methodology for developing specialized skills for AI coding agents. It provides detailed instructions on integrating Azure SDKs and Microsoft Foundry services, covering crucial topics like secure authentication patterns (DefaultAzureCredential), modular workflow design, and maintaining concise, effective knowledge context for optimal agent performance.

A workflow for executing implementation plans by spinning up a fresh subagent for each task and enforcing consecutive spec compliance and code quality reviews so the session stays focused and high-quality without manual context switches.

This guide provides production-ready patterns for implementing the TwinMind AI memory and meeting intelligence REST API. It covers best practices for authentication, storing contextual memories, searching knowledge bases, and processing meeting transcripts to extract actionable insights. Use this when building robust, scalable AI-powered applications or standardizing team coding practices.

Configures and manages isolated Git worktrees by selecting safe directories, verifying ignore rules, running project setup, and validating a clean test baseline so teams can work on multiple branches without switching contexts.

A collection of production-ready Python patterns for interacting with the Vast.ai cloud API. This includes typed search query builders, context-managed instance lifecycle (guaranteeing auto-cleanup), offer scoring algorithms (cost, reliability, performance), robust retry mechanisms with backoff, and secure SSH command execution. Essential for building reliable, complex cloud workflow applications.

This guide provides advanced, deep-dive debugging techniques for complex issues within the Windsurf integrated development environment. It addresses persistent problems such as context corruption in Cascade, indexing failures, extension conflicts, and deep configuration bugs that standard troubleshooting cannot resolve. Use this when the AI suggestions or IDE behavior is consistently incorrect or unstable.

This skill provides comprehensive guidance on selecting the optimal workspace architecture for Windsurf, adapting to various project scales. Whether managing small solo projects, large monorepos, or complex multi-service, polyglot systems, it outlines strategies for structuring file systems and configuring rules. The goal is to ensure that AI tools receive accurate and focused project context, maximizing developer efficiency.

This skill provides advanced context management for AI in large-scale, complex codebases. It optimizes context windows, ensures memory persistence across multiple sessions, and helps prioritize critical information. Use it when working with massive code repositories to maintain context and improve developer workflow.