Skill UI

A collection of production-ready GitHub Actions workflow patterns designed for automating the entire software development lifecycle (SDLC). Use these templates to streamline continuous integration (CI), build Docker images, deploy to Kubernetes clusters, perform security scans, and execute matrix testing across various tech stacks.

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).

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).

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.

This guide details a production-ready reference architecture for applications built on Groq's LPU inference API. It covers sophisticated model routing based on latency, quality, or cost needs, implementing streaming pipelines, and establishing robust multi-provider fallback chains (e.g., Groq primary to OpenAI backup). Ideal for designing and reviewing complex, resilient LLM systems.

This skill provides a comprehensive deep dive into migrating from legacy or self-managed insurance systems to Guidewire Cloud. It covers the entire migration lifecycle, including initial system assessment, product configuration, complex data migration (policies, claims, accounts via ETL), API integration cutover, and final UAT to go-live stages. It is designed for technical teams planning a major platform upgrade.

This skill automatically gathers and structures comprehensive context for Apple product development. It analyzes existing project files (like README, Info.plist, and code) and prompts the user for details across product overview, target platforms, technology stack, design system, and accessibility requirements. The output is a structured context document used by other specialized skills to ensure consistent, informed design and development decisions.

Auto-activating skill provides guidance for HTTP method helper tasks, covering REST, GraphQL, OpenAPI, authentication, and API design patterns while producing production-ready code and validations.

A comprehensive skill designed to manage, track, and persist image assets generated by Ideogram's API. This tool addresses the critical issue of expiring image URLs by implementing a complete lifecycle pipeline. It handles metadata tracking (prompt, seed, model, style, etc.), immediate downloading, and robust storage to both local filesystems and cloud solutions like S3. Features include asset persistence, generation history logging, and image reproduction using stored seed values.

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.

This architecture provides a scalable, production-ready framework for integrating AI candidate analysis with Juicebox. It manages automated data ingestion from job descriptions, ranks candidates using intelligent scoring, handles result caching, and facilitates seamless data export to major ATS platforms like Greenhouse and Lever. Designed for high-volume, complex hiring pipelines requiring data freshness, deduplication, and robust scaling.