Skill UI

This guide outlines the process of migrating codebase dependencies and logic when upgrading the Ideogram API from legacy versions (V1/V2) to the robust V3 endpoints. It addresses critical breaking changes, including the shift from JSON body to multipart/form-data, changes in model naming, aspect ratio syntax, and the integration of new V3 features like rendering speed and style presets. Provides structured code adapters for smooth transition.

This skill outlines step-by-step methods for discovering, confirming, and exploiting Insecure Direct Object Reference flaws via parameter manipulation, HTTP method switching, enumeration, and Burp Suite workflows, enabling authorized testers to document findings and suggest fixes.

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.

Automated guidance for generating infographic outlines, applying best practices across diagrams, charts, presentations, and visual documentation, with checks to keep outputs production-ready.

This comprehensive guide outlines essential, production-grade patterns for building robust LangChain applications. Learn best practices for implementing type-safe structured output using Zod, handling provider fallbacks, processing inputs concurrently with batching, optimizing user experience with streaming, and ensuring reliability through caching and retries.

This guide outlines essential security practices for deploying production-grade LLM applications using LangChain. It covers secure secrets management, defending against prompt injection, implementing sandboxed tool execution, validating model outputs for sensitive data, and setting up comprehensive audit logging, ensuring robust and secure AI deployment.

This comprehensive guide outlines essential security best practices for implementing and using Lindy AI agents. It covers crucial topics such as robust API key management, webhook signature verification, strict agent permission scoping, and secure data handling protocols. Use this resource when auditing agent activity, configuring enterprise security features (SSO/SCIM), or ensuring compliance and minimizing the attack surface area of autonomous AI systems.

This comprehensive guide outlines a multi-step process for safely upgrading the Linear SDK across major versions. It details how to check for breaking changes (like renamed fields or changed return types), implement compatibility layers using patterns like the Bridge pattern in TypeScript, run thorough unit and integration tests, and establish a robust deployment and rollback plan, ensuring zero downtime migration.

This skill provides a comprehensive guide for integrating Lokalise into CI/CD pipelines. It outlines how to automate the download of the latest, reviewed translations during the build stage, ensuring that production environments always ship the most current localized content. Ideal for setting up deployment workflows on platforms like Vercel, Netlify, and GitHub Actions.

This skill outlines a comprehensive, low-downtime process for migrating APIs between different versions, frameworks, or platforms. It guides the implementation of advanced architectural patterns like the Strangler Fig pattern, traffic shadowing, and canary deployments. Use this when upgrading a monolithic API to microservices or transitioning between incompatible API versions.

This comprehensive guide outlines best practices for developing modern, scalable applications using the Next.js App Router. It covers critical architectural patterns, including the decision-making process between Server and Client Components, advanced data fetching strategies (caching, revalidation), file-system routing principles, API route handling, and performance optimizations like image and bundle splitting. Essential knowledge for building robust full-stack React applications.

This comprehensive guide defines the secure data lifecycle management for OpenEvidence, an AI-powered platform for clinical evidence synthesis. It outlines data classification (e.g., clinical queries, patient responses), strict adherence to global regulations (HIPAA, GDPR, FDA), and technical requirements for handling Protected Health Information (PHI). Key processes include PHI de-identification, data validation, secure API integration, and compliant data export, ensuring the system maintains the highest level of patient privacy and regulatory compliance.