Skill UI

This skill provides comprehensive guidance for setting up production-grade Continuous Integration/Continuous Deployment (CI/CD) pipelines for Go projects using GitHub Actions. It covers all critical stages, including rigorous unit testing with race detection, advanced linting, static application security testing (SAST) via tools like CodeQL and govulncheck, automated dependency management (Dependabot/Renovate), container image building, and automated binary release using GoReleaser. Use this when building a robust quality gate for a Go codebase.

This guide provides comprehensive best practices for Go code quality using the industry-standard `golangci-lint` tool. It covers the full development workflow, including initial setup and configuration of `.golangci.yml`, running linters (like govet, staticcheck), automatically fixing issues, correctly suppressing warnings using `//nolint` directives, and interpreting complex linter output to ensure high code maintainability and security.

This skill outlines the principle of mandatory, evidence-based verification before declaring any work (such as a feature, bug fix, or task) complete or successful. It mandates that assertions of success must be backed by repeatable evidence, such as passing test outputs, clean build logs, or successful execution reports. It is critical for PR reviews, task closure, and maintaining high engineering rigor.

A comprehensive guide to the three primary architecture patterns for integrating with Figma: CLI scripts, Webhook services, and dedicated plugins. This resource helps developers select the optimal pattern (REST vs. Plugin API) based on whether the use case requires real-time event handling, local automation, or in-editor component interaction. Ideal for planning robust Figma-connected applications like design token synchronization or linting.

Comprehensive guide for setting up Continuous Integration/Continuous Deployment (CI/CD) pipelines for Palantir Foundry integrations using GitHub Actions. This workflow covers running PySpark unit tests, performing SDK integration tests with mocked APIs, and implementing custom linting rules to enforce Foundry-specific best practices. Use this when ensuring code quality and validating complex business logic changes in an automated build process.

This guide details how to scaffold complete, structured exercise directory setups, including mandatory sections for problems, reference solutions, and conceptual explainers. It outlines precise naming conventions, required files (like readme.md and main.ts), and a robust workflow that ensures the setup passes linting checks. It is ideal for creating new course modules, generating exercise stubs, or structuring large-scale educational curricula.

This wrapper integrates Shopify's official AI Toolkit MCP server with Claude Code. It provides essential developer capabilities, including real-time GraphQL schema validation, Liquid template linting (checking for deprecated filters and anti-patterns), and instant search across Shopify's extensive documentation. Use this skill when setting up complex Shopify MCP integrations, ensuring code quality, or debugging API calls.

This guide provides a comprehensive setup for Continuous Integration/Continuous Deployment (CI/CD) pipelines for Shopify apps. It utilizes GitHub Actions to automate linting, unit testing, and critical integration tests. Key features include API version compatibility checking, secure secret management, and streamlined deployment via the Shopify CLI, ensuring stable and reliable e-commerce functionality. Ideal for setting up automated release cycles or monitoring API changes.

This skill provides comprehensive capabilities for creating, editing, and managing professional presentations within the Lark (Feishu) ecosystem. It supports advanced content generation workflows, utilizing XML protocols, template searching, and layout linting to ensure high-quality, structured visual output. Ideal for automating report generation, slide decks, and structured corporate communications.

A comprehensive skill for building, auditing, and linting custom Kubernetes Operators. Focuses specifically on the Operator pattern, ensuring controllers correctly implement declarative reconcile loops. Tools validate Custom Resource Definitions (CRD) against best practices, catch common reconciliation bugs (like status drift or blocking calls), and score operators against industry capability levels.