Skill UI

This skill generates a comprehensive risk register for projects, programs, or products. It applies standard risk management practices, including likelihood/impact scoring, RAG status, and detailed mitigation/contingency plans. Ideal for preparing reports for project boards, steering committees, and programme management offices.

This skill provides a quantified design audit (0-100) for UI files based on StyleSeed's comprehensive design language. It breaks down the score into weighted categories (Color, Typography, Layout, etc.), identifies specific design violations, and generates a prioritized list of fixes, acting as a critical quality gate before deployment.

This methodology provides a rigorous framework for scoring competitors across nine weighted dimensions. By using explicit 1-5 rubrics and focusing intensely on the client's strategic tension, it moves beyond subjective gut-feel rankings to create defensible, comparable, and evidence-anchored scores. It is essential for developing a detailed, structured competitive report.

A utility for scaffolding and verifying fixed evaluation corpora (bench suites). It generates deterministic test suites from existing repositories, decoupling the evaluation process from the repo's natural test suite. This ensures consistent fitness scoring when running evolution pipelines across different commits or forks.

This skill runs an automated evolution process designed to systematically optimize the seven core policy surfaces of an AI agent harness (e.g., planner, contextBuilder, reviewer). By mutating these policies and scoring the variants in a sandbox, it identifies crucial performance improvements without requiring expensive foundation model retraining. It is ideal for empirical configuration tuning and establishing optimized baselines.

This library provides comprehensive patterns for building robust Clinical Decision Support Systems (CDSS) integrated into Electronic Medical Records (EMR). It ensures patient safety by implementing critical checks such as drug interaction screening, weight/age/renal-adjusted dose validation, and standardized clinical scoring (e.g., NEWS2). The engine uses pure functions, guaranteeing high testability and reliability for critical medical insights.

This pattern is designed to solve context limitations in complex multi-agent workflows. Instead of relying on a single, massive context dump, it implements a structured, multi-cycle loop (Dispatch -> Evaluate -> Refine -> Loop). This process iteratively refines the search query by scoring content relevance and identifying missing context gaps, ensuring sub-agents gather the most targeted and highly relevant information needed for tasks like debugging or feature implementation.

This skill generates sophisticated trading signals by employing the neural-trader anomaly detection engine. It utilizes Z-score scoring to classify market movements into distinct patterns, such as spikes (breakout), drifts (trend forming), and oscillations (range-bound). The process integrates neural prediction and historical pattern searching to provide highly ranked, actionable signals, making it invaluable for algorithmic and quantitative trading strategies.