Skill UI

This tool analyzes your entire codebase, assessing project identity, team size, test maturity, and existing architectural signals. It then recommends the most appropriate, AI-assisted development methodology stack (e.g., Microservices, Enterprise-Gov) and asks targeted questions to refine the plan, providing a concrete quick start guide.

This skill programmatically searches and fetches biomedical literature from PubMed and PMC using NCBI E-utilities (ESearch, EFetch, ESummary). It allows users to gather large corpora of abstracts, full texts, and metadata related to specific conditions, drugs, or genes. Ideal for building evidence bases, performing biomedical Named Entity Recognition (NER), or summarizing scientific findings.

This mode defines an autonomous, senior-level AI agent capable of resolving complex tasks from start to finish with minimal human supervision. It operates by strictly verifying every claim against real evidence (live web search, codebase, available tools). The agent prioritizes action over questioning, only interrupting the user for irreversible actions, genuine dead-ends, or critical ambiguities, ensuring maximum efficiency in hands-on engineering and operational workflows.

A comprehensive tool designed to analyze and suggest improvements for performance bottlenecks across various domains, including application code, database queries, and system architecture. It identifies issues like memory leaks, inefficient algorithms (e.g., O(n²)), and N+1 database queries, providing actionable steps and best practices to enhance system stability and speed.

Guides users to select a local, on-device Personally Identifiable Information (PII) detection model from a committed registry. It filters models based on language, required runtime environment (e.g., PyTorch, MLX), and size constraints. The process emphasizes building an offline shortlist and mandates rigorous pre-deployment recall validation and performance benchmarking to ensure secure, privacy-preserving deployment.

Performs comprehensive, two-layer validation on generated plans. Layer 1 executes instant structural checks (format, dependencies, compliance) while Layer 2 triggers specialist agents (e.g., security, DB, performance) based on plan content. The system forces resolution of all identified issues before allowing execution, supporting auto-fixes and guided human input.

This utility analyzes code changes within a branch compared to the base branch. It calculates a complexity score, detects scope coherence issues (warning if changes are too large or unrelated), and generates a fully structured Pull Request (PR) draft. It automates the creation of titles, comprehensive descriptions (including checklists and technical details), and even suggests splitting the PR into smaller, manageable units, significantly improving code review quality and efficiency.

This skill provides a comprehensive, 4-phase workflow for managing and reviewing a pull request backlog. It automatically audits open PRs, classifying them by size, staleness, overlap, and CI status. Users can then initiate deep code reviews, generate comment drafts, and set up local worktrees for systematic maintenance and quality assurance.

A comprehensive assistant designed to analyze existing codebases for adherence to SOLID principles, common code smells, and complexity metrics. It identifies anti-patterns like tight coupling, excessive responsibility, and duplication, providing prioritized, actionable refactoring suggestions to improve code maintainability and quality.

Streamlines the complex process of documenting software releases. This tool scans Git history, categorizes changes (features, fixes, performance), and generates multiple outputs—including a structured CHANGELOG, PR body, and user-friendly communication announcements—while also detecting necessary database migrations. Ideal for professional software teams preparing for production deployments.

This skill enhances raw clinical entity recognition by assigning three critical axes: Negation, Temporality, and Uncertainty (ConText). It determines the clinical status of a mention (e.g., affirmed, negated, historical, or uncertain), ensuring that basic NER results are transformed into clinically faithful assertions before grounding to standards like FHIR. Essential for accurate problem list generation and differential diagnosis support.

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.