Skill UI

This template simulates a hand-drawn, low-fidelity wireframe session on a whiteboard or sketchpad. It includes graph paper backgrounds, marker scribbles, sticky notes, and multiple tabs, making it ideal for early-stage UI/UX brainstorming and capturing initial conceptual ideas.

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 the comprehensive workflow for distributing a single AI skill across various local agent skill directories (Codex, Claude, Pi, Hermes). It addresses canonical locations, symlink management, and specific synchronization commands to ensure that all agents detect the latest skill version while correctly navigating common development pitfalls.

This skill guides the creation of comprehensive, structured strategic documents (SSOT, life vision, principles) using a strict, iterative interview-style methodology. Instead of generating content, the AI asks one focused question at a time. The user provides the answer, and the AI patches the document incrementally, ensuring the user remains the primary author. It is ideal for deep reflection, strategic planning, and building authoritative knowledge bases.

A simulated board of legendary marketing advisors (Seth Godin, David Ogilvy, Alex Hormozi, etc.). This tool analyzes a marketing problem by providing multiple, often conflicting, expert perspectives. Its value lies in surfacing trade-offs, identifying blind spots, and forcing the user to confront real strategic dilemmas before choosing a path for execution.

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

This skill guides the design, implementation, and auditing of inclusive digital products based on WCAG 2.2 standards. It covers the POUR principles and provides technical guidance for generating semantic ARIA (Web) and native accessibility traits (iOS/Android). Use it when defining UI components, auditing compliance gaps, or ensuring robust keyboard navigation and focus management.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

A comprehensive tool for querying, storing, and managing complex knowledge across multiple agent sessions. AgentDB utilizes advanced techniques including semantic routing, hierarchical recall, causal graph construction, and context synthesis. Use this skill when your application requires robust, multi-faceted knowledge retrieval and reasoning based on structured and unstructured data. It provides a structured workflow for checking system health, starting sessions, storing knowledge in tiers (working, episodic, semantic), and searching learned patterns.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

A comprehensive guide to implementing Clean Architecture patterns for both Android and Kotlin Multiplatform (KMP) projects. It covers robust module structuring, dependency inversion rules, defining UseCases and Repositories, and designing the data layer using technologies like Room, SQLDelight, and Ktor. Learn how to separate concerns, maintain platform independence, and manage complex data flows.