Skill UI

This skill provides a comprehensive guide on securely integrating and calling Firebase Cloud Functions from Flutter applications. It covers the proper use of `httpsCallable` to invoke structured server-side logic, handling data serialization, implementing robust error handling (including permission denial and timeouts), configuring regional deployments, and utilizing the Firebase Emulator Suite for reliable local testing.

Guides integrating Firebase Cloud Storage with Flutter apps, covering setup, uploads/downloads, metadata, error handling, and security best practices for file management workflows.

A comprehensive guide to creating production-ready, idiomatic Go applications. Covers critical patterns such as functional options, robust error handling, graceful shutdown, resource management, and architectural best practices. Ideal for refactoring existing Go code, choosing between architectural patterns, or designing scalable APIs and constructors.

A comprehensive guide to implementing robust, idiomatic error handling in Go. It covers best practices for error creation, context wrapping using `%w`, type-safe inspection with `errors.Is`/`errors.As`, adhering to the single handling rule, and utilizing structured logging with `slog`. Essential for building scalable, debuggable, and production-ready Go applications.

A comprehensive guide for building, reviewing, and debugging production-grade gRPC microservices using Golang. Covers critical patterns such as setting up interceptors for cross-cutting concerns (logging, auth), robust error handling with specific status codes, managing client deadlines, and implementing graceful service shutdowns.

samber/oops provides a structured, context-rich replacement for standard Go error handling. It allows engineers to attach critical metadata—such as domain, user ID, stack traces, error codes, and request details—directly to the error object. This ensures that variable data travels with the error through the call stack, enabling advanced APM tools (like Sentry or Datadog) to group and diagnose issues accurately, treating errors as structured, actionable data.

A comprehensive Go implementation of ReactiveX for building declarative, type-safe pipelines for asynchronous data streams. It manages complex event sources, handling automatic backpressure, error propagation, and context integration. Ideal for real-time data processing, WebSockets, and event-driven architectures where manual goroutine and channel wiring is complex.

This guide details best practices for using `lark-cli` to interact with Feishu resources. It covers initial configuration, switching between user and bot identities, and crucial authentication flows for AI agents. It provides detailed instructions on handling permission errors, implementing secure split-flow authentication, and managing high-risk write operations to ensure data integrity and user consent.

OpenViking Code Repository Search exposes `ov` terminal commands for scoped semantic, keyword, and glob searches across viking:// resources, lets you read directory summaries/files, and manage indexed repositories (add/remove) with documented error handling.

This generator provides a comprehensive, production-ready template for building a Model Context Protocol (MCP) server using Kotlin. It sets up the full Gradle structure, integrates essential dependencies (MCP SDK, Ktor, Coroutines), and includes robust placeholders for custom tools, error handling, and testing, accelerating the development of context-aware backend services.

This is an expert guide for writing, debugging, and validating complex expressions within n8n workflows. It covers core variables such as $json, $node, $now, and $env, detailing how to correctly access nested data from current nodes, previous nodes, and webhook payloads. Learn best practices for handling data structures, managing variable scopes, and avoiding common syntax errors to ensure reliable workflow automation.

Produces production-ready Dataverse SDK code for Python, showcasing advanced error handling and retries, batch operations, OData query tuning, metadata lifecycle management, configuration/timeouts, cache invalidation, chunked file uploads, and Pandas integrations with references.