Skill UI

This skill provides a comprehensive, structured workflow for diagnosing performance bottlenecks in SwiftUI applications. It guides users through a code-first review to detect common issues like invalidation storms, layout thrash, or heavy main-thread work. When code review is insufficient, it provides detailed guidance on collecting runtime evidence using Instruments, enabling precise root cause analysis and targeted remediation strategies.

Guides Python developers through architecture decisions, framework choices, async vs. sync trade-offs, type-hint strategies, and project structure best practices for 2025 projects.

This tool serves as a persistent memory and state manager for complex coding projects within the Claude environment. It allows users to initialize new projects with structured metadata, track detailed session progress (including summaries, decisions, and next steps), and save the entire context reliably. By ensuring project continuity across disconnected sessions, it drastically improves the flow and consistency of the development workflow.

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.

An automated verification system designed for critical healthcare application deployments (EMR/EHR). This harness enforces stringent patient safety standards by running multiple mandatory test gates, including Clinical Decision Support System (CDSS) accuracy, Protected Health Information (PHI) leakage prevention, and data integrity checks. A single failure in critical gates automatically blocks deployment, ensuring regulatory compliance and optimal patient safety in the CI/CD pipeline.

This tool facilitates comprehensive purple team exercises by executing Atomic Red Team tests mapped to MITRE ATT&CK techniques. It is primarily used for gap analysis, validating detection coverage (blue team visibility) against specific threat vectors, and building continuous, automated adversarial emulation pipelines in CI/CD environments.

Generates a precise, evidence-based installation plan for a project's component catalog (ECC). Instead of loading a full, noisy bundle, this skill analyzes the repository's actual code structure, dependencies, and CI/CD evidence to classify skills, rules, and commands into 'DAILY' (essential) and 'LIBRARY' (referenceable) buckets. It ensures only necessary components are active, optimizing the development environment.

This tool guides the creation of actionable monitoring dashboards for observability platforms like Grafana and SigNoz. Instead of merely displaying raw metrics, it focuses on structuring data to answer critical operational questions: Is the system healthy? Where are the bottlenecks? What changes occurred? And what action should be taken? It transforms raw metrics into decision-making tools.

This utility ensures the prevention of silent floating-point mismatch bugs across all EVM chains. It covers runtime querying of decimals, chain-aware caching, handling precision drift in bridged tokens, and safe normalization for bots, dashboards, and DeFi tools. It is crucial for accurate cross-chain balance comparison.

Implements an ultra-compressed communication mode by drastically reducing token count while maintaining high technical accuracy. This mode strips filler words, articles, and pleasantries, allowing users to communicate complex technical concepts in the most concise way possible. Ideal for debugging, generating logs, or highly efficient development conversations. Supports multiple intensity levels, including classical Chinese modes.

This guide provides best practices for technical documentation, emphasizing the capture of context (the *why*) rather than just code (the *what*). It details how to write Architecture Decision Records (ADRs) for significant architectural choices, such as database selection, API design, and framework choice. Utilizing ADRs ensures that future engineers and teams understand the original reasoning, constraints, and trade-offs made during development.

This comprehensive guide provides best practices for creating, reviewing, and auditing documentation for Go projects. It covers writing principles (e.g., Intent over paraphrase, Concision), structuring documentation (godoc comments, README, CHANGELOG), and workflow orchestration for both libraries and CLI applications. Ideal for technical writers and developers ensuring code is discoverable and maintainable.