Skill UI

SkillOpt-Sleep provides a mechanism for autonomous agent self-improvement. By simulating a 'sleep' or 'dream' cycle, it harvests past interaction sessions, mines recurring tasks, and replays them through the designated backend. It consolidates learned knowledge into persistent memory and skills, ensuring that only strictly validated improvements are adopted via a held-out gate, simulating long-term competence gain from short-term experience. Ideal for scheduled, background optimization.

An internal utility designed for advanced debugging of Rust applications. It helps developers analyze panics or backtraces by checking proper handling of Option and Result types, generating minimal reproducible `cargo test` commands, and identifying precise patch targets for safe code improvements.

This skill automates the migration of existing frontend code (including React, Vue, Angular, and Next.js components) into a structured Stitch Design. It orchestrates the extraction of a self-contained HTML file, analyzes the source code to build a comprehensive design system document, and finally uploads both assets into your Stitch project for iteration and improvement.

Max is a specialized agent designed for safe, non-behavioral code improvement. It systematically refactors existing, working code by optimizing time complexity (Big-O), abstracting duplicated logic, removing dead code, and enhancing readability. It operates under a strict constraint: no change may alter the original functionality, requiring all refactored code to pass existing test suites flawlessly.

This skill generates a comprehensive customer journey map, detailing the entire customer experience from initial awareness through to advocacy. It identifies key stages, customer actions, emotional highs and lows, critical pain points, and actionable opportunities for product improvement or service enhancement. Ideal for UX research, product discovery, and cross-functional alignment.

A comprehensive framework for assessing the operational and cultural health of a team. It covers key dimensions such as delivering value, morale, psychological safety, and release capability. Use this guide for structured retrospectives, leadership reviews, or post-incident analyses to identify root causes and generate actionable improvement plans.

This skill analyzes raw user research data—including transcripts, survey results, and feedback—and synthesizes it into a structured, actionable report. It identifies key themes, quantifies prevalence, pinpoints critical pain points, prioritizes feature requests, and provides strategic, evidence-backed recommendations for product improvement and development.

A comprehensive guide on creating developer-centric changelogs and release notes. Learn how to structure updates using Semantic Versioning and the Keep a Changelog format. This skill helps communicate new features, breaking changes, deprecations, and performance improvements in a way that developers genuinely care about, building trust and demonstrating product momentum.

A comprehensive, four-phase methodology for debugging complex software issues. It guides users through root cause investigation, pattern analysis, hypothesis testing, and controlled implementation. The core principle emphasizes finding the underlying cause rather than applying quick, symptomatic fixes, ensuring robust and reliable code improvements across various failure scenarios.

A comprehensive dashboard designed for tracking dating and community engagement metrics. It features real-time signal tickers, key performance indicators (KPIs), 30-day mutual match charts, and match rate trend analysis. Ideal for personal self-improvement or tracking consumer/social trends.

This skill analyzes raw WordPress Site Health reports (text, screenshots, or debug info). It converts diagnostic data into a prioritized, risk-tiered fix plan, identifying critical issues and recommended improvements. It provides exact, safe commands (WP-CLI/PHP snippets) necessary to address the site and strictly enforces safety protocols like backups, lint checks, and dry-runs.

This framework guides AI agents to critically assess their own output across five key axes: Accuracy, Completeness, Clarity, Actionability, and Conciseness. It provides a structured, evidence-based scorecard and concrete suggestions for improvement, turning every task completion into a deep learning opportunity.