Skill UI

A comprehensive methodology for pinpointing the root cause of complex bugs, regressions, or unexpected behavior in large codebases. Techniques include binary search debugging, Git bisect for tracking regressions across commits, strategic data/control flow logging, component isolation, and advanced breakpoint strategies (conditional breakpoints, watch expressions). Ideal for scenarios where the bug source is unclear or difficult to reproduce.

This skill guides the implementation of essential user feedback states—Loading (Skeleton), Empty (Zero Data), Error, and Success—for data-dependent components or entire pages. It ensures a robust and polished user experience by providing structured, best-practice patterns for handling various data scenarios, preventing visual gaps or dead ends in the UI flow.

This skill is designed for scenarios requiring complex, multi-step task decomposition. Instead of asking an Agent to perform the task directly, it guides the process of requesting a clear, actionable, sequential execution plan from a stronger Agent. The core function emphasizes that the user must execute the generated plan themselves, ensuring the output is a roadmap, not a final answer.

This utility provides a structured way to manage and separate notes across multiple accounts and environments within Apple Notes. By utilizing distinct accounts (e.g., iCloud for Production, 'On My Mac' for Development), users can ensure absolute data isolation between development, staging, and live production data. It is crucial for maintaining clean separation in complex development workflows and QA testing scenarios.

A comprehensive diagnostic guide for common issues encountered with CAST AI agents, Kubernetes autoscaling, API connectivity, and resource provisioning. Covers scenarios like agent crashes, 401 errors, and scaling failures, providing specific commands (kubectl, curl, helm) and detailed fixes for robust troubleshooting.

A comprehensive, step-by-step runbook designed for diagnosing and resolving critical operational issues in ClickHouse databases. Covers tiered severity levels (P1-P3) for scenarios ranging from server outages and OOM kills to performance degradation and stuck queries. Provides essential system tables queries and commands for service recovery and system health checks.

This guide provides production-ready patterns for using the @clickhouse/client library in Node.js. It covers critical, real-world scenarios such as safe typed queries, streaming large data inserts (with backpressure handling), robust batch operations with retries, and streaming SELECT queries for low memory consumption. It also includes best practices for comprehensive error handling and connection lifecycle management, ensuring stable and scalable ClickHouse integrations for data pipelines.

A comprehensive runbook for responding to critical incidents involving Flexport API integrations. It covers various failure scenarios, including complete API outages, webhook delivery loss, and complex supply chain data synchronization issues. The guide provides severity classification (P1-P4), diagnostic commands, and step-by-step playbooks for rapid triage and mitigation.

A comprehensive guide detailing the process of migrating bookkeeping and accounting services to Fondo. It covers multiple scenarios, including transfers from DIY solutions, other firms (like QuickBooks, Pilot, Bench), and managing changes in accountants. The guide outlines necessary data preparation (GL exports, bank statements, tax records) and the phased onboarding process to ensure seamless financial continuity for growing businesses.

Provides a comprehensive checklist for deploying Hex data analytics integrations into production. It covers critical operational best practices, including API key management, rate limit handling, error resilience (circuit breakers, exponential backoff), and robust monitoring strategies. This guide ensures that data pipelines are deployed reliably, minimizing downtime and guaranteeing consistent, high-quality data reporting for go-live scenarios.

This benchmark evaluates Vision-Language Models (VLMs) on detecting potential safety hazards within indoor environments. It uses static camera frames, simulating real-world fixed security camera monitoring. The test covers 40 scenarios across 5 critical categories, including fire/smoke, electrical risks, trips/falls, child safety, and falling objects, providing a rigorous measure of VLM's practical safety inspection capability.

A comprehensive set of structured patterns for integrating with the Lucidchart REST API. These patterns handle complex requirements such as OAuth 2.0 token lifecycle management, mandatory API version negotiation, deep shape tree structures, and robust error handling for concurrency issues (e.g., document locking, rate limiting). Includes utilities for building type-safe queries and mocking test scenarios.