Skill UI

This expert guide provides comprehensive knowledge for interpreting and systematically resolving validation errors within n8n workflows. It covers various error types (e.g., missing required fields, type mismatches, invalid expressions) and outlines best practices for iterative validation, including understanding different validation profiles like 'runtime' and 'strict' for various deployment stages. Ideal for debugging complex automation logic.

This tool automates the entire process of creating a complete, production-ready application structure solely from an OpenAPI (Swagger) specification. It analyzes the spec to design the application architecture, generating all necessary layers including controllers, service logic, data models (DTOs), and repositories, adhering to best practices, framework conventions, and separation of concerns. The output includes unit tests, configuration files, and setup instructions, providing a robust, ready-to-use codebase.

PDFtk Server is a powerful command-line tool designed for comprehensive manipulation and processing of PDF documents. It allows users to programmatically merge, split, rotate, secure (encrypt/decrypt), fill forms, apply watermarks, extract metadata, repair corrupted files, and much more. Ideal for automation scripts, batch processing, and any scenario requiring robust, scripted PDF handling from the terminal.

A persistent system designed to guide complex, multi-step projects, research, and long-running tasks. Instead of relying on limited volatile context, this skill uses structured Markdown files (task_plan.md, findings.md, progress.md) as a 'disk working memory.' It enforces a rigorous workflow, ensuring key decisions, findings, and progress updates are always logged persistently, thereby preventing information loss and maintaining continuity across sessions.

A powerful tool that converts complex Unified Modeling Language (UML) diagrams—such as Sequence, Class, Activity, and State diagrams—into plain text ASCII art. This is crucial for documentation in environments that do not support image embedding, such as terminal sessions, README files, or Git commit messages. Supports both standard ASCII and enhanced Unicode ASCII formats for better visual fidelity.

This comprehensive guide provides a detailed framework for authoring effective and structured SKILL.md files for AI coding agents. It covers best practices for structuring content, including frontmatter fields, defining natural language triggers, establishing core principles, and incorporating advanced elements like decision trees and verification checklists, ensuring agents receive clear, actionable, and consistent technical guidance.

This skill provides comprehensive command-line interface (CLI) functionality for managing the entire lifecycle of experiments within the Arize platform. Users can list, retrieve metadata, create new experiments from various data files (CSV, JSON, Parquet), and export detailed run results for deep analysis. It supports linking experiments to specific dataset versions, making it essential for structured ML model evaluation and performance comparison workflows.

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.

Analyzes IaC files and live Azure resources to validate SKUs, usage, and costs, then generates GitHub issues (one per recommendation plus an EPIC) so teams can track implementation of prioritized savings.

Guides teams through creating a complete GitHub Copilot configuration by outlining required instructions, reusable skills, and agent/workflow files tailored to the chosen tech stack and workflow preferences.

Reads `.agent.md` or `.prompt.md` files, assesses task complexity, tooling, and requirements, then recommends the most cost-efficient GitHub Copilot model(s) (e.g., GPT-5, Claude Sonnet) with rationale for users across different subscription tiers and priorities.

This guide provides comprehensive best practices for creating optimized, secure, and efficient multi-stage Dockerfiles. Learn how to separate build environments (builder stage) from minimal runtime environments, significantly reducing image size and attack surface. Topics covered include layer caching optimization, security hardening (non-root users), and using minimal base images for reproducible builds.