Skill UI

A comprehensive guide and toolset for Go code quality assurance using `golangci-lint`. It enables developers to run multiple linters in parallel, configure best practices via `.golangci.yml`, automate fixes, and interpret complex lint output. Ideal for maintaining code standards, setting up CI/CD pipelines, and cleaning up legacy codebases.

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 meta-skill is designed exclusively to orchestrate and formalize complex workflows composed of multiple existing skills. It acts as a comprehensive governance layer, guiding the user through intent clarification, trigger collision checks, linting, and proposal persistence. Use it when proposing a multi-step, integrated solution, but do not use it for simple standalone skill requests.

An internal tool designed to validate candidate meta-skills (SKILL.md) before registration. It performs comprehensive structural linting (G1 rules) and a scheduler dry-run (G2) to ensure the workflow is deterministic, references are correct, and templates resolve properly. It outputs detailed JSON diagnostics.

An internal utility designed to manage the entire lifecycle of meta-skill proposals within the opensquilla ecosystem. It allows developers to atomically write new skill proposals, list existing candidates, and formally accept them after passing rigorous gate checks (linting, smoke testing, E2E). This tool ensures structured and controlled deployment of new skills.

Dep handles the entire journey from local code to production deployment. This skill automates containerization using Docker and docker-compose, establishes robust CI/CD pipelines (linting, testing, security scanning, deployment), manages environment variables and secrets, and generates Infrastructure as Code (Terraform/Pulumi) configurations. It ensures observability and provides rollback procedures.

This skill designs and audits the core engineering harness required for reliable AI agent collaboration within a codebase. It establishes critical infrastructure elements such as change tracking, linting rules, CI/CD gates, and standardized documentation, ensuring the repository remains the single source of truth for all intelligent agents. Ideal for setting up complex development workflows.

This skill generates a complete, actionable CI/CD playbook, thoroughly documenting the entire software deployment lifecycle. It covers essential stages like build & linting, unit/integration testing, security scanning, and phased deployment to staging and production. The output provides clear guidelines on required inputs, automated gates, manual approvals, and necessary rollback procedures, which is crucial for engineering documentation and team onboarding.