Skill UI

Automates updating Azure Verified Modules referenced in Bicep files to their latest released versions, guiding scan, version comparison, documentation checks, and validation workflows while limiting reports to non-breaking changes.

Maintains the repository's llms.txt guidance file by auditing current documentation, specs, and structure to ensure the navigation summary matches reality and stays compliant with the official specification.

Scans specified files or folders to infer existing syntax, writes a project coding standards or contribution doc using configurable templates, and optionally reports or fixes inconsistencies before saving to the first available standard filename.

This skill guides the systematic execution of complex development plans. It mandates a critical review of the plan for dependencies, ambiguous conditions, and environmental assumptions before execution. The process involves incremental task completion, rigorous testing, structured committing, and final reporting, ensuring adherence to established development best practices.

This skill guides developers through the structured completion of a feature branch. It enforces running comprehensive tests, detects the current Git environment (e.g., common repository vs. worktree), and presents clear options (merge, PR, retain, discard). It ensures safe integration, manages worktree cleanup, and enforces best practices for version control workflows.

This skill helps users maintain a structured and comprehensive record of their daily activities, progress, key decisions, and learning outcomes. It creates a dedicated file for each day using the ISO date format (yyyy-mm-dd), ensuring that all entries are organized chronologically. By adhering to concise, third-person reporting, it helps users build a valuable, reviewable knowledge base of their journey and accomplishments.

This guide outlines a mandatory, evidence-based protocol for declaring any work complete, fixing a bug, or submitting a pull request. It strictly mandates that all claims of success must be supported by fresh, verifiable evidence, such as passing test outputs, successful build logs, or comprehensive checklist verification. It eliminates reliance on assumptions, gut feelings, or trusting proxy reports.

A comprehensive coach designed to elevate all aspects of human communication. This skill covers public speaking techniques, persuasive presentation design, impactful storytelling, active listening, and professional written communication (emails, reports). It emphasizes theory combined with mandatory, structured practice and actionable feedback, helping users navigate various contexts, from board pitches to difficult conversations, while accounting for cultural communication norms.

A comprehensive guide for configuring ArgoCD within KubeSphere DevOps. It details implementing GitOps continuous deployment, covering application management, multi-cluster setup, SSO integration via Dex, and best practices for managing application state synchronization from Git repositories. Learn the differences between direct ArgoCD and KubeSphere's tenant-friendly GitOps application creation.

Generates professional, publication-ready clinical decision support (CDS) documents for pharmaceutical and clinical research settings. Capabilities include biomarker-stratified patient cohort analyses, evidence-based treatment recommendation reports (with GRADE grading), and advanced statistical analyses (e.g., hazard ratios, survival curves). Outputs are optimized for regulatory submissions and medical guidelines.

This skill provides comprehensive support for generating high-stakes clinical documentation, including case reports (CARE guidelines), diagnostic summaries (radiology/pathology), clinical trial reports (ICH-E3), and patient progress notes (SOAP/H&P). It mandates adherence to strict regulatory standards (HIPAA, FDA, ICH-GCP) and integrates AI-generated scientific schematics to ensure the final output is accurate, compliant, and publication-ready.

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.