Skill UI

This guide outlines a rigorous, technical framework for receiving and responding to code review feedback. It emphasizes that code review is a technical assessment, not an emotional exchange. Users learn how to verify assumptions, ask clarifying questions, professionally refute incorrect suggestions, and implement changes iteratively, ensuring code quality and architectural integrity.

Generate production-quality React components and UIs using the specialized Rayden UI component library and Tailwind CSS. This skill ensures that all generated code strictly adheres to a defined design system, including correct component usage, design tokens, prop validation, and established layout patterns. Ideal for scaffolding complex pages like dashboards, login forms, and data tables while maintaining premium aesthetics and consistency.

This specialized skill automates the process of converting visual designs (from Stitch) into high-quality, modular React and Vite components. It adheres to modern frontend best practices, enforcing logic isolation via custom hooks, data decoupling using mock data files, and ensuring type safety with TypeScript interfaces. The workflow includes automated asset fetching, style mapping (Tailwind), and rigorous validation cycles to produce production-ready, maintainable code.

Seaborn is a high-level Python visualization library built on Matplotlib, designed specifically for creating publication-quality statistical graphics. It excels at dataset-oriented plotting, enabling users to perform complex multivariate analyses, visualize data distributions (like box plots, violin plots), and explore relationships with minimal code. It simplifies the process of generating multi-panel figures and statistical plots directly from pandas DataFrames.

This guide provides comprehensive troubleshooting for common issues encountered when converting Figma designs to code using the Anima SDK. It covers authentication failures (Figma/Anima tokens), specific node/file lookup errors, code rate limits, and output quality problems, helping developers ensure accurate and robust code generation.

A comprehensive tool designed to help developers gather all necessary evidence when facing persistent issues or API errors with Anthropic's Claude models. It collects vital data points such as the request ID, full error payloads, status codes, and provides reproducible cURL commands, ensuring developers can file high-quality support tickets.

This comprehensive guide details how to set up robust Continuous Integration/Continuous Deployment (CI/CD) pipelines for HubSpot integrations using GitHub Actions. It covers unit tests, deep integration tests against a developer test account, and secure secret management. Ideal for automating HubSpot API testing, ensuring consistent code quality across branches, and implementing reliable deployment workflows for CRM features.

A comprehensive checklist for validating the production readiness of design-to-code pipelines, specifically for Anima's service converting Figma designs into React, Vue, and HTML. Use this guide when deploying automated code generation services, setting up CI/CD for design handoffs, or validating output quality to ensure component integrity and prevent production failures.

This guide sets up robust CI/CD pipelines for validating Attio integrations. It combines mocked unit tests (using MSW) for speed and gated live API integration tests for pre-release validation. Use this to ensure complete code reliability and automated quality checks across all development pushes.

A comprehensive guide to setting up CI/CD pipelines for Adobe API integrations. It covers secure OAuth credential management, unit testing, secret pattern scanning, and rigorous integration testing within GitHub Actions. Essential for maintaining high code quality in Adobe-integrated SaaS products.

This guide provides production-ready, high-quality patterns for integrating with Algolia Search v5. It covers critical architectural aspects such as implementing singleton client management, performing strongly typed searches, robust error handling (including specific API error codes), efficient batch indexing, and handling multi-tenant environments. Use these patterns to refactor existing code and establish reliable coding standards.

This comprehensive checklist guides the secure and systematic deployment of web scraping integrations using Bright Data. It covers critical stages including production zone verification, code quality checks (e.g., rate limiting, error logging), infrastructure setup, robust monitoring configuration, and safe gradual rollout procedures, ensuring reliable, mission-critical data collection processes into production.