Skill UI

Master secure Electron desktop app development: multi-process architecture, contextIsolation, IPC communication, preload scripts, electron-builder packaging, code signing, and auto-update strategies.

This skill enables the makepad-skills library to continuously self-improve during development. It uses Claude Code hooks for automatic triggering, routing relevant skills based on context, and facilitating self-correction and validation. It guides developers on when to evolve skills, how to classify new knowledge, and the process for contributing patterns or solutions back to the library.

Automate software development workflows directly within GitHub repositories. Configure CI/CD pipelines, build, test, and deploy applications efficiently. Manage secrets, caching, artifacts, and reusable workflows to streamline DevOps processes. Supports self-hosted runners and debugging for robust deployment strategies.

This skill provides patterns for automating GitHub workflows with AI assistance. It helps in automating PR reviews, setting up issue triage, creating GitHub Actions workflows, and integrating AI into CI/CD pipelines. It also covers automating Git operations like rebases and cherry-picks. Inspired by modern DevOps practices and tools like Gemini CLI, this skill streamlines development processes and reduces manual effort in repository management.

This system treats ideas like living species on an 'evolution island.' Instead of simple storage, it runs structured, multi-round iterations simulating biological processes: competition, crossbreeding, and mutation. Users input initial concepts, and the tool systematically evaluates them using a 6-dimensional scoring framework (Novelty, Feasibility, Value, etc.). It helps users deepen viability, discover unexpected connections between disparate ideas, and prioritize the most robust concepts for development and investment.

This skill bootstraps a new Rails project with a predefined tech stack including Rails 8, PostgreSQL, Inertia.js, React, Vite, Tailwind, Sidekiq, and Redis. It configures the environment for SPA-like experiences, background job processing, and comprehensive testing using minitest. The setup includes security scanning, UUID primary keys, and TSX for frontend components, ensuring development matches production settings without Docker or Kamal.

This skill generates Newman CLI commands, configuration files, Jenkins pipeline scripts, and shell automation for running Postman collections in CI/CD or local environments. It helps automate API testing workflows by creating reusable scripts and pipeline configurations that integrate seamlessly with development and deployment processes, ensuring consistent test execution.

This skill covers principles for identifying tasks suited to LLM processing, designing effective project architectures, and iterating rapidly using agent-assisted development. It applies to batch pipelines, multi-agent systems, and interactive applications. Key concepts include task-model fit recognition, manual prototyping, pipeline architecture, file system state management, and structured output design for reliable parsing.

This skill focuses on designing Software Development Kits (SDKs) that developers enjoy using. It emphasizes creating APIs that feel native, providing helpful error messages, and reducing friction in the development process. The goal is to drive adoption through exceptional developer experience rather than aggressive marketing strategies.

This comprehensive, zero-trust security auditor scans third-party AI agent skills before installation. It performs a rigorous 6-phase review process, analyzing surface patterns, script integrity, permissions, and supply chain risks. It is designed to detect malicious payloads, credential theft, instruction overrides, and prompt injection attacks, ensuring secure AI development and deployment.

This tool audits the activation metadata of coding skills used by AI agents. It grades skills (A-F) based on description clarity, simulates which skill a prompt will trigger, and identifies metadata collisions where one skill might silently overshadow another. It is essential for ensuring robust, predictable, and reliable AI agent behavior during development and CI processes.