Skill UI

This skill reconstructs the detailed workflow behind a specific coding artifact (like a feature or PR). By analyzing local `ax` agent traces, it connects commits, sessions, skills, and tool calls into a concise, evidence-grounded narrative. It is used when needing to understand the step-by-step process and key decisions that led to a result.

Detailed methods for red-teaming and penetration testing of LLM applications. This skill extracts sensitive system prompts, secrets, and embedded logic using advanced techniques like jailbreaking, encoding tricks, and automated scanners (Garak, Promptfoo), ensuring compliance with OWASP LLM07:2025.

A comprehensive skill guide for defining the core intent, tone, and strategic direction of any frontend design. It applies rigorous cross-domain principles drawn from architecture, cinema, marketing, and automotive design. Use this before coding to establish purpose, visual hierarchy, and intentional structure, ensuring every design element is deliberate and highly crafted.

This skill helps developers achieve maximum AI readiness by automatically detecting the project stack and seeding context using a single, precise goal sentence. It systematically fills the essential project context (the 6 Ws), requiring the user only to answer the few remaining knowledge gaps (like the 'why' or 'when'). This creates a portable, structured context file, ensuring AI models have a complete understanding of the project before coding, minimizing guesswork and maximizing efficiency.

This professional tool helps developers and researchers identify the optimal AI model for a specific task, such as coding, reasoning, or image recognition. It queries official HuggingFace leaderboards, filters models based on hardware constraints (e.g., available VRAM or RAM), and provides a detailed comparison table including benchmark scores, parameter size, and licensing information.

Before You Build acts as a crucial checkpoint in the AI coding workflow. It prevents premature implementation by rigorously assessing product risk—analyzing market demand, existing user workflows, switching costs, and distribution channels. Instead of writing code, it guides the user to define core assumptions and propose the smallest, most effective validation test (e.g., landing page test, user interview) before committing resources.

Context Kit is an external framework designed to organize and manage personal, durable context data (like identity details, memories, and domain knowledge) into secure Markdown artifacts. It helps users set up robust knowledge bases for coding agents (e.g., Claude Code), guiding them on best practices for data privacy, local storage, and safe integration, while preventing sensitive data leaks.

A specialized presentation deck template designed for internal engineering talks, technical conferences, and deep-dive knowledge sharing. It features a professional GitHub-dark theme, JetBrains Mono coding font, and a structured layout including Cover, Agenda, main content slides (with code blocks), demo slides (terminal screenshots), and Q&A sections.

A comprehensive utility for managing complex cmux topologies, including workspaces, panes, and surfaces. This tool is essential for inspecting, controlling, and monitoring multiple AI coding agents running in parallel within a macOS terminal environment. It supports advanced functionalities such as browser automation and real-time status notifications, optimizing developer workflows.

This guide details how to effectively delegate complex, multi-step tasks to various specialized AI agents (Codex, Pi, Claude, Hermes). It covers best practices for maintaining conversation context, handling TUI prompts (e.g., avoiding newlines, using quotes), techniques for reliable polling, and choosing the optimal agent for coding, design, or general purpose work. Essential for advanced AI workflow automation.

This skill enforces highly efficient coding practices, guiding the AI to write the simplest, most direct code possible. It strictly follows an 'Efficiency Ladder' (YAGNI, reusing existing code, standard library, etc.) to prevent over-engineering, unnecessary abstractions, and boilerplate. Use this when the goal is maximum efficiency with minimal code.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.