Skill UI

This skill generates a complete, opinionated, modern full-stack Rails application boilerplate. It sets up a robust tech stack including Rails 8, PostgreSQL, React, Inertia.js, Vite, Tailwind CSS, Sidekiq, and Redis. Designed for developers who need a starting point that adheres to best practices for database structure (UUIDs, JSONB), background job processing, comprehensive testing, and modern frontend development (TSX). Ideal for bootstrapping complex, production-grade web services.

This skill outlines a comprehensive methodology for designing, building, and deploying robust LLM-powered applications. It covers assessing task-model fit, designing modular pipelines (acquire -> prepare -> process -> parse -> render), and managing state using file systems to ensure scalability and reliability in AI systems.

Guides building secure production-grade Electron desktop apps with isolated renderer processes, hardened IPC via preload bridges, packaging through electron-builder, code signing, and full auto-update workflows.

This tool provides a structured methodology for triaging raw vulnerability findings generated by DAST and SAST scanners. It utilizes the OWASP risk rating methodology to validate true positives, filter out false positives, and calculate a comprehensive risk score (Risk = Likelihood x Impact). This process significantly reduces alert fatigue and ensures development teams prioritize remediation efforts on the most critical and exploitable vulnerabilities.

A comprehensive workflow for discovering, integrating, and utilizing third-party APIs and SDKs. This skill guides the process by first detecting the project language, ensuring necessary guidelines and skills are present, systematically fetching API details, and providing structured guidance on authentication, model usage, and endpoint implementation. It tracks integration progress by recording specific development milestones (e.g., SDK setup, first call made).

The GitHub Copilot SDK empowers developers to embed advanced, agentic AI workflows directly into their proprietary applications. It provides a programmatic interface to Copilot's production-tested agent runtime, allowing you to define complex agent behaviors, manage tool invocation, process file edits, and implement real-time streaming responses. Ideal for building custom, highly intelligent tools and automating complex tasks across multiple languages (Python, TypeScript, Go, .NET).

This skill guides the end-to-end process of building automated evaluation pipelines for Python applications powered by Large Language Models (LLMs). It teaches how to define evaluation criteria, instrument the application, build golden datasets, and run real-world evaluations using tools like pixie, ensuring the app's logic (not just the LLM) is rigorously tested. Ideal for QA, benchmarking, and improving LLM-based services.

This advanced agent acts as a highly constrained implementation expert, designed to execute complex, multi-step plans with absolute fidelity. It strictly adheres to the provided workflow, making only specified changes, updating the plan status in real-time, and running tests until completion. Ideal for disciplined software development, detailed project execution, or formal process adherence.

This skill guides the systematic execution of a detailed, written development plan. It mandates a critical review phase to identify dependencies, vague requirements, and environment assumptions before starting. The process involves tackling tasks sequentially, performing rigorous verification (testing/checking), committing changes after each milestone, and systematically handling common exceptions like test failures or dependency gaps. It ensures a disciplined, traceable, and reportable development cycle.

This skill guides developers through the final stages of a development branch workflow. It enforces critical best practices by requiring a test verification step before presenting structured options. Users can choose between local merging, creating a Pull Request (PR), retaining the branch state, or safely discarding the work, ensuring a clean and repeatable integration process.

This guide outlines the structured methodology for initiating code reviews using a dedicated sub-agent. It dictates when (after completing tasks, implementing critical features, or before merging) and how to provide the necessary context (SHA hashes, implemented code, requirements) to ensure the work product is thoroughly evaluated. By adopting this process, developers can catch architectural flaws and bugs early in the development lifecycle, improving code quality and maintainability.

This guide provides a comprehensive, reproducible workflow for setting up an Algolia local development environment. It covers creating separate development indices, seeding mock data, and implementing client mocking for unit tests. This process ensures fast, safe iteration cycles, allowing developers to build and test search features without ever touching production data.