Skill UI

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).

A comprehensive guide to optimizing Granola's output quality across three dimensions: audio transcription accuracy, AI enhancement quality, and overall processing speed. Learn best practices for hardware setup, ideal meeting habits, and structuring your templates to maximize AI output reliability and efficiency.

This skill guides the design and implementation of complex GraphQL APIs. It specializes in schema design, utilizing Apollo Federation for distributed microservices, optimizing resolvers with DataLoader for N+1 prevention, and implementing real-time subscriptions. It covers security, complexity analysis, and adherence to best practices for building scalable, high-performance graph architectures.

A comprehensive guide to using Groq's high-speed LPU for chat completion. This resource provides code examples in TypeScript and Python, covering basic chat interactions, streaming responses, and advanced multimodal features. Ideal for developers building high-performance, low-latency AI applications using the Groq API.

This skill provides comprehensive observability tools for Groq API integrations. It instruments the client to track critical performance metrics such as Time to First Token (TTFT), total latency, token throughput, and estimated cost. Furthermore, it sets up Prometheus exporters and alert rules to monitor rate limit status and detect performance degradation in real-time, ensuring robust production monitoring.

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 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.

GroqCloud Automation orchestrates high-performance GroqCloud APIs through Composio, covering inference, chat completions, audio translation, and TTS voice selection for production workflows.

This bundle is designed to collect comprehensive diagnostic data from the Guidewire ecosystem. It captures critical information including Cloud API responses, Gosu stack traces, server logs, OAuth token status, and batch process statuses. Use it to aid in debugging, troubleshooting, and performance analysis within Guidewire applications.

This runbook provides a structured guide for responding to critical Guidewire production incidents. It covers the entire lifecycle of incident management, including initial triage (checking monitoring, API errors, and batch logs), diagnosing performance degradation (JVM metrics and query performance), implementing mitigation steps, and proper escalation procedures to Guidewire Support.

A comprehensive toolkit designed to monitor the health and performance of Guidewire InsuranceSuite. It tracks critical operational metrics including claim processing times, policy synchronization rates, billing accuracy, API latency, and batch job statuses. The system provides real-time alerting, structured logging, and health checks to maintain high service level agreements (SLAs) for complex insurance workflows.

This skill provides comprehensive guidance on optimizing Guidewire application performance. It covers advanced development techniques such as optimizing Gosu queries using the Query API, implementing efficient batch processing with BatchProcessBase, implementing caching strategies for frequently accessed reference data, and performing JVM heap tuning via the Guidewire Cloud Console (GCC). Use this when system performance bottlenecks are detected in Guidewire insurance or core business platforms.