Skill UI

Transforms existing code files into highly effective learning resources by adding context-aware educational comments. Acting as an expert educator, this tool tailors explanations—from foundational concepts for beginners to deep architectural insights for advanced practitioners—while strictly maintaining the original file's structure, encoding, and syntax. Ideal for generating self-documenting, pedagogically rich codebases.

This guide outlines the structure and content guidelines for an AGENTS.md file. This file serves as a comprehensive 'README for agents,' providing detailed, technical context and operational instructions (such as setup, development workflow, testing procedures, and code style) that enable advanced AI coding agents to interact with and understand a codebase effectively. It greatly complements traditional READMEs by focusing purely on machine-readable technical requirements.

Guides how to write JavaScript inside n8n Code nodes, choose execution modes, access data with $input/$node, handle Webhook payloads, and return the required [{json:…}] structure for automation tasks.

The Phoenix CLI is a powerful tool for debugging complex LLM and agent workflows. It allows users to fetch detailed traces, spans, and sessions, analyze errors, and structure findings using open and axial coding techniques. It is essential for investigating complex LLM failures, building robust failure taxonomies, and guiding the next steps in application evaluation after initial instrumentation.

Analyzes Copilot guidance files and repository docs to craft structured README.md content, detailing the project purpose, architecture, technology stack, workflows, coding standards, testing approach, and contributing advice for fellow developers.

This micro-prompt guides AI agents to adopt the mindset of an interactive programmer. It emphasizes using the REPL (Read-Eval-Print Loop) for systematic system exploration and modification, treating the system state as the ultimate source of truth. It also reminds the agent to utilize structural editing tools and maintain todo lists, significantly improving the accuracy and efficiency of the development workflow in live coding environments.

Acts as a senior software engineer to meticulously review and refactor entire codebases. This skill ensures that the code is clean, highly maintainable, and strictly adheres to defined coding standards and best practices specified across multiple project instruction files. It guarantees the integrity of the existing file structure and ensures all unit tests remain passing after modifications.

This skill ensures code implementation is grounded in official documentation rather than memory or outdated training data. It verifies framework-specific patterns, APIs, and best practices against authoritative sources before coding. Ideal for building reliable boilerplate, handling framework features like routing or state management, and ensuring correctness in version-sensitive environments. Every pattern is cited with its source.

Guides a project planning agent through researching, decomposing, and documenting multi-commit development plans before coding, enforcing autonomous research protocols and template-driven plan outputs for structured PR execution.

A comprehensive tool that analyzes complex codebases to generate detailed, structured architectural blueprints. It automatically detects and documents the entire technology stack, including primary languages (.NET, Java, Python, etc.), dependencies, versions, licensing, and implementation patterns. The output provides actionable documentation covering coding conventions, usage examples (API, data access), and visual architecture maps, ensuring consistency across guided software development projects.

Transforms you into a 10x web engineer, guiding work across HTML, CSS, JavaScript, web APIs, HTTP/HTTPS, security, performance, accessibility, and infrastructure so you can deliver standards-compliant, high-quality front- and back-end solutions.

This structured prompt guides an AI model to proactively analyze a user's question and systematically list all necessary files for accurate answering. It forces the AI to categorize required context into 'Must See' (critical), 'Should See' (helpful), 'Already Have,' and 'Uncertainties.' Ideal for complex coding tasks, debugging, or feature implementation that spans multiple codebase files.