design-review
Designer's eye QA: detects and automates fixes for visual inconsistencies, spacing, hierarchy, and UI polish issues. Iteratively verifies with before/after screenshots.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
271 skills found
Designer's eye QA: detects and automates fixes for visual inconsistencies, spacing, hierarchy, and UI polish issues. Iteratively verifies with before/after screenshots.
Implement an AI agent delegation architecture to keep your main context clean, reduce token costs, and isolate specialized infrastructure or API tasks.
Automate your daily Milan news digest with this Python-based briefing tool. Supports weather, strikes, world/AI/Italian news, and event scraping, featuring deduplication, RSS/API pipeline management, and AI-agent ready scheduling.
Create and test AI-ready MCP tools for any web application. Inject code, automate browser interactions, and turn websites into intelligent agents.
Dynamic meta-router for managing and orchestrating multi-domain AI coding agent skills across plugins and projects.
Prevents AI hallucination and ensures evidence-based, verifiable outputs when analyzing code, reviewing technical documents, or providing recommendations.
Autonomous pattern detection and skill recommendation engine that monitors project memory, logs, and task lists to evolve your AI agent's capabilities automatically.
Comprehensive guide and implementation framework for building, configuring, and deploying NexAU agents from scratch, including tools, prompts, and skills.
Framework for orchestrating long-running agentic tasks, evidence-based delivery, and automated QA gates following Simon Willison's iterative loop.
Orchestrates multi-agent development workflows, managing task decomposition, requirement analysis, and quality assurance for complex software projects.
Review, audit, and build production-grade frontend interfaces with high design quality, accessibility standards, and design system compliance.
Transform AI agents into proactive partners using WAL Protocol, persistent memory buffers, and autonomous cron scheduling to anticipate needs and improve performance.