Skill UI

A production-grade framework for executing high-performance, robust browser automation and web scraping using Playwright with the Go language. This skill specializes in handling complex Single Page Applications (SPAs), emulating human behavior (e.g., random typing delays, Bezier mouse movements), and implementing advanced stealth techniques to bypass anti-bot systems like Cloudflare. It emphasizes architectural best practices, such as using contexts for isolation and structured logging.

This skill simulates a senior Go developer, specializing in writing high-performance, production-grade Go applications. It covers advanced topics like concurrent programming using goroutines and channels, designing robust microservices with gRPC/REST, and optimizing code using pprof and benchmarks. It enforces idiomatic Go patterns, context propagation, and strict testing practices (table-driven, fuzzing, race detection).

Automates Grafana dashboard creation for DevOps workflows, giving guided steps, production-ready configurations, and validation when integrating Kubernetes, Terraform, Helm, and advanced CI/CD monitoring practices.

This skill guides you through designing and implementing production-grade Grafana dashboards for comprehensive system observability. Learn to visualize critical metrics, track key performance indicators (KPIs), monitor infrastructure health (using principles like RED/USE), and build actionable alerts using Prometheus data sources. Ideal for DevOps engineers and Site Reliability Engineers (SREs).

Guides API developers through building well-structured GraphQL mutations with best-practice patterns, production-ready code, and validation help whenever “graphql mutation builder” appears in the request.

Automates GraphQL resolver creation for backend teams, giving step-by-step guidance, industry best practices, and production-ready code/configs to validate resolvers across Node.js, Python, Go, database design, caching, messaging, and microservices.

Automates GraphQL schema generation within API development, guiding REST/GraphQL/OpenAPI design, producing production-ready code/configurations validated against standards.

Comprehensive guide for deploying applications powered by the Groq inference API. Covers best practices and specific setup instructions for multiple production environments, including Vercel Edge Functions, Google Cloud Run, Docker containers, and other containerized platforms. Essential for setting up robust, low-latency, production-ready AI services.

A comprehensive guide to configuring and managing Groq API access across development, staging, and production environments. This skill establishes environment-specific configurations, ensuring optimal model selection, rate limit handling, and secure secret management (using AWS, GCP, Vault) for robust and scalable LLM integrations.

A comprehensive guide to maximizing Groq API performance. This skill details advanced techniques including optimal model selection (balancing latency and quality), implementing semantic caching, efficient token management, streaming responses for perceived speed, and orchestrating parallel requests to ensure high throughput and low latency in production applications.

A comprehensive checklist for deploying Groq-powered applications to production. It covers critical areas including secure API key management, model fallback strategies, rate limit planning, implementing robust error handling (e.g., circuit breakers), setting up health endpoints, and configuring detailed monitoring and spending controls necessary for a successful, stable go-live.

A comprehensive guide to production-ready patterns for using the Groq SDK in TypeScript and Python. Covers best practices for client singleton setup, type-safe completions, streaming, robust error handling (rate limiting, API errors), and implementing exponential backoff retries to ensure stable, high-performance LLM integration.