Skill UI

This skill guides a thorough, adversarial review of development plans across four key dimensions: Architecture, Code Quality, Testing, and Performance. It forces explicit consideration of trade-offs, risks, and user approval before any code is written, ensuring robust and well-engineered solutions for complex systems.

This tool analyzes a given codebase to automatically identify high-value, actionable automation routines. It surfaces use cases across three primary trigger types: scheduled cron jobs, external API calls, and GitHub repository events. By mapping manual pain points to autonomous workflows, it helps teams implement robust CI/CD and DevOps practices.

This skill guides users on deploying OpenMed models completely on-device for maximum privacy and edge computing. It details running workflows using three backends: MLX (Apple Silicon), CoreML (iOS/macOS), and ONNX/WebGPU (cross-platform/browser). The process involves converting, optionally quantizing, and running models locally, ensuring PHI never leaves the host device.

This protocol provides a systematic, multi-step diagnostic approach to determine if a command failure (e.g., permission denied, network issues) is genuinely due to sandbox restrictions or is caused by environmental or measurement errors. It includes eight critical checks—such as verifying shell scope and reading real exit codes—to eliminate false positives before filing a blocker report.

This tool simulates a senior application security engineer, performing a comprehensive, multi-phase audit. It checks for exposed secrets, prompt injection vectors, dependency vulnerabilities (supporting multiple languages), and critical configuration misconfigurations. It provides a scored, prioritized remediation report, ensuring a robust security posture before deployment.

This technique segments unstructured clinical documents (such as HPI, PMH, and A&P sections) into contextually rich, canonical chunks. This is crucial for improving the precision of downstream NLP tasks like Named Entity Recognition (NER) and de-identification. By identifying and normalizing headers and mapping them to LOINC codes, the system ensures that the source context (e.g., historical vs. active) is maintained, leading to significantly more accurate results.

This skill applies consistent date shifting to clinical text, ensuring that absolute dates are obscured while critically preserving the temporal intervals between medical events. It is essential for longitudinal research or survival analysis where time-to-event data must be preserved under HIPAA Safe Harbor rules. It maintains the integrity of time differences (e.g., length of stay, visit gaps) by applying a stable, per-patient offset.

This tool leverages an LLM-as-a-Judge agent to thoroughly evaluate staged Git changes before they are committed. It checks for correctness, completeness, and safety issues, providing a detailed verdict (APPROVE, NEEDS_REVIEW, REJECT) and actionable feedback. It is essential for maintaining high code quality, especially in complex or security-sensitive code sections.

This utility is essential for developers needing to ensure that recent code modifications function as intended. By running the application within a verification environment, users can quickly validate that the changes meet all expected behaviors, maintaining system stability and quality assurance before deployment or merging.

A comprehensive, structured guide for reverse engineering and analyzing managed .NET/C# binaries. This skill covers the full workflow, including initial identification (CLR check), deobfuscation (using tools like de4dot), static analysis via IL view (dnSpyEx), and dynamic debugging. It is essential for analyzing malware, red team tools, and patching compiled C# applications.