Skill UI

This guide provides a comprehensive technical deep dive for developers migrating image generation workflows from services like DALL-E, Midjourney, and Stable Diffusion to Ideogram. It covers API parameter mapping, architectural patterns (like the Adapter pattern), and provides code examples to ensure a stable, gradual, and feature-rich transition to Ideogram's platform.

Automated assistant for frontend image optimization walks you through best practices, generates production-ready code and config, and validates results whenever you ask about image optimization patterns or helper workflows.

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.

Automates integration test setup guidance, offering step-by-step best practices, production-ready code, and validation for automation workflows when integration testing is mentioned.

Automates Istio service mesh configuration guidance for DevOps Advanced users, offering best-practice patterns, production-ready code, validation, and infrastructure-as-code support across Kubernetes, Terraform, and monitoring workflows.

This sophisticated skill automates the entire Pull Request (PR) quality assurance lifecycle. It systematically fetches and categorizes CI check failures and detailed review feedback using the LOGAF priority scale. The workflow guides users through root cause analysis, proposing minimal, targeted fixes, replying to reviewers, and committing updates iteratively until all checks pass, ensuring high code quality before merging.

This skill specializes in writing, debugging, and refactoring modern JavaScript code (ES2023+). It covers advanced patterns like async/await, Promise handling, ESM modules, and optimal performance for both browser and Node.js environments. Use it for vanilla applications, complex async flows, and performance optimization.

This comprehensive guide addresses common technical and business logic errors encountered when integrating with the Juicebox API. It covers HTTP status codes (e.g., 401, 403, 429), including authentication failures, quota exhaustion, rate limiting, data validation issues, and analysis timeouts. The resource provides detailed causes, specific fixes, and best practices for building reliable and robust integrations into recruiting and research workflows.

Automated backend guidance for Kafka producer and consumer workflows, offering best practices, code samples, and configuration help for Node.js, Python, Go, databases, caching, messaging, and microservices scenarios.

Automates Kubernetes ConfigMap handler tasks by providing step-by-step guidance, best practice patterns, and production-ready code as part of an advanced DevOps workflow focused on Terraform, Helm, and infrastructure-as-code.

Automated guidance for Kubernetes ingress configuration within advanced DevOps workflows, covering best practices, production-ready setups, and validation for infrastructure-as-code delivery.

Automated guidance for Kubernetes service management in the DevOps Advanced domain, offering best-practice advice, code snippets, and config checks for services, Terraform, Helm, and monitoring workflows.