Skill UI

Guidance lets you wrap LLM calls with grammars, regex, and selection constraints to ensure valid JSON/XML/code, enforce formats, heal tokens, and orchestrate multi-step workflows via Pythonic context managers for production-ready automation.

Automates helm chart generation, guidance, and validation for advanced DevOps workflows, covering Kubernetes, Terraform, monitoring, and infrastructure-as-code best practices.

Automates guidance and code generation for managing Helm values within advanced DevOps workflows, including Kubernetes, Terraform, CI/CD, monitoring, and infrastructure-as-code best practices.

Automatically guides API developers through creating hypermedia link generators, covering REST, GraphQL, OpenAPI, authentication, and design patterns while producing validated code and configurations that follow best practices.

This comprehensive guide details how to set up robust CI/CD pipelines for projects integrating with Ideogram. It covers automated unit testing, optional integration tests using GitHub Secrets, and crucial prompt validation logic directly within GitHub Actions workflows, ensuring high code quality and reliable API interaction.

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.

Detects fail-open insecure defaults such as hardcoded secrets, weak auth, or permissive security that allow production apps to run unsafely, guiding auditors through discovery, verification, confirmation, and reporting steps during security reviews or deployment validation.

Automates guidance and code for identifying insecure deserialization flaws, following Security Fundamentals best practices to help with authentication, input validation, and secure coding checks when that phrase is mentioned.

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.