Skill UI

This skill converts unstructured documents (PDF, EPUB, DOCX, etc.) into actionable, structured agent skills. Instead of providing simple summaries, it deep-dives to extract core frameworks, mental models, key principles, techniques, and anti-patterns from educational material. Use this when you need to transform academic knowledge into a reusable, machine-readable toolkit for agents like Copilot CLI, Amp, or Claude Code.

This tool restores project context, crucial after a session clear. It comprehensively analyzes Git history, summarizing recent commits, tracking uncommitted changes, scanning for outstanding TODOs/FIXMEs, and verifying the current project structure. It helps developers quickly re-establish the full working context to resume work efficiently.

This utility guides users in creating standardized and descriptive commit messages for staged changes using the Conventional Commits specification. It enforces structure, defining types (feat, fix, chore, etc.), scopes, and detailed body/footer requirements, ensuring a clear and maintainable project history.

A specialized downstream skill for CTF-sandbox workflows. Used to analyze complex security challenges driven by AI agent interactions, prompt injection, or infrastructure vulnerabilities. Focuses on tracing control chains involving untrusted input, tool side effects, deployment drift, container runtime issues, and CI/CD supply chain provenance. Ideal for rigorous vulnerability assessment in modern, distributed systems.

This skill specializes in analyzing built frontend artifacts, going beyond static source code. It reconstructs the dynamic JavaScript structure, tracks chunk loading paths, and interprets source maps and build manifests to recover hidden routes, APIs, and runtime behaviors. Essential for deep security analysis and understanding execution flow from deployed bundles.

This specialized skill is used to trace complex network requests through multi-layered infrastructure, such as reverse proxies, container boundaries, and various header manipulations. It is essential for determining which specific node, proxy rule, or derived header ultimately serves a live request within a sandboxed environment. Use it when analyzing VHost routing, path rewrites, or X-Forwarded headers to pinpoint the decisive backend.

This skill provides a structured, multi-step guide for automating interactions with desktop applications. It emphasizes obtaining a reliable context via observation, preferring UI Automation (using element IDs for buttons, menus, or editable fields) over raw coordinates. It covers best practices for keyboard input and ensuring state consistency across major operating systems (Windows/macOS), making it suitable for complex, reliable process automation.

This skill facilitates the computation of Electronic Clinical Quality Measures (eCQMs) by leveraging CQL (Clinical Quality Language) logic against the QDM (Quality Data Model). It is designed to extract crucial facts—such as counseling provided, symptoms, or documented exclusions—from unstructured clinical notes. By lifting these facts, it supplements structured EHR data, ensuring that numerators and valid exclusions are accurately captured for quality measure evaluation. Note that this tool augments data capture and does not replace certified measure engines.

This skill guides the creation of computable phenotypes and patient cohorts using the OHDSI/OMOP CDM standards. It allows users to define precise, executable criteria by combining standard concept sets (like PheKB) with advanced Natural Language Processing (NLP) features extracted from free clinical text. Use this when structured codes alone are insufficient to capture critical health information (e.g., symptoms, social context).

This tool de-identifies sensitive free-text columns (e.g., names, dates, identifiers) in local structured datasets (CSV, JSONL, Parquet) using advanced OpenMed models. It ensures strict privacy by generating a completely redacted dataset and a separate PHI-free aggregate summary, allowing for secure data sharing and analysis without compromising source privacy or logging sensitive values.

A specialized tool designed to detect, suggest, and evaluate Gang of Four (GoF) design patterns within TypeScript and JavaScript codebases. It analyzes code structure, identifies code smells, and provides stack-aware refactoring recommendations, supporting frameworks like React, Angular, NestJS, and Vue.

The Diagram Generator converts complex, unstructured inputs—such as natural language, code snippets, schemas, and notes—into professional, editable visual diagrams. It supports diverse standards including Mermaid, PlantUML, and Graphviz, enabling the creation of flowcharts, sequence diagrams, UML models, ER diagrams, architecture maps, and more. Outputs are designed to be reviewable, version-controllable, and highly precise.