Skill UI

This guide outlines Dynamic Workflow Mode, enabling coding agents to generate structured, task-local harnesses for complex, multi-step processes. It establishes a disciplined system for temporary workarounds, extracting reusable shared skills, and implementing control pane checkpoints. Use this when tasks require custom loops, multi-agent collaboration, or reliable handoff artifacts.

This comprehensive guide helps elevate a UI from merely functional to polished, purposeful, and aesthetically coherent with its domain. It dictates pre-coding design choices, such as defining the audience, tone (e.g., minimal, industrial), and purpose. It provides rigorous guidelines on establishing visual hierarchy, typography, color palettes, and interactions, ensuring the final product feels intentional and professional, rather than generic or templated.

GateGuard is a mandatory pre-tool-use hook designed to significantly enhance the reliability and safety of LLM-generated code and actions. Instead of relying on self-evaluation, it forces the model to perform concrete, verifiable investigations—such as listing all imported files, checking data schemas, or detailing rollback procedures—before any write or destructive command execution. It establishes a three-stage gate (Deny, Force, Allow) to prevent hallucination and ensure context awareness in complex coding environments.

A powerful tool designed to scaffold sophisticated, custom AI agent harnesses using the metaharness CLI. It supports various templates (e.g., coding, devops, marketing) and AI hosts (e.g., Claude, Codex). Crucially, it operates with maximum safety, defaulting to a dry-run mode and refusing to write to the calling repository root, ensuring controlled and predictable development workflows.

Hookify rules enable developers to define custom, event-driven workflows using Markdown files with YAML frontmatter. These rules monitor specific operational events (such as bash commands, file edits, or user prompts) and can trigger warnings or block operations when predefined regex patterns are matched. They are essential for enforcing coding standards, preventing dangerous operations, and guiding development adherence in complex software environments.

This skill analyzes existing legacy codebases to detect implicit coding conventions across four meta-architectural dimensions (File Anatomy, State & Control Flow, Infrastructure, Error Handling). It resolves conflicts one by one with the user, generating a structured rules file (.ai-style-rules.md) and optionally installing persistent hooks to prevent AI-generated code style drift in future development.

A comprehensive guide detailing best practices for writing clean, maintainable, and type-safe Java code, specifically tailored for Spring Boot services. It covers naming conventions, prioritizing immutability, proper use of Optional, stream API best practices, and modern project structuring.

This comprehensive guide covers essential security patterns for Perl development, addressing common vulnerabilities like SQL Injection, XSS, Path Traversal, and Shell Injection. It details best practices including using Perl's Taint Mode, implementing strict allow-list validation, performing safe file operations, and executing external commands securely to ensure robust and resilient applications.

This skill is intended to analyze and improve the quality of codebases, ensuring adherence to best practices and coding standards. Note: This specific file is a translation placeholder, and the core functionality description should refer to the original skill file for details.

A comprehensive guide detailing the architectural standards, technology stack, file structure, and coding patterns for building modern, AI-driven full-stack applications. It covers best practices for Next.js, FastAPI, database interaction (Supabase), and structured AI integration (Claude), ensuring consistency and scalability across the development lifecycle.

A comprehensive guide to writing robust, efficient, and maintainable Python applications. Covers core principles like readability, explicit coding, and EAFP (Easier to Ask Forgiveness than Permission), advanced features like modern type hinting, context managers, and sophisticated exception handling techniques.

A structured workflow designed to prevent 'reinventing the wheel.' Before writing custom code, this skill guides the user through analyzing needs, conducting parallel searches across multiple channels (npm, PyPI, GitHub, MCP), evaluating candidate solutions based on maintainability and community support, and making a decision (Adopt, Extend, or Custom Build). It ensures that solutions leverage existing, proven libraries and patterns.