Skill UI

Create a focused workspace Skill from reusable decisions, knowledge, templates, or workflows in the current conversation. Supports planning, user approval, draft authoring, validation, and publication. Use for /make-skill with a focus argument to save workflows or convert them into reusable skills.

This advanced skill manages complex, large-scale development tasks by coordinating multiple, specialized subagents in parallel. It systematically decomposes the goal into isolated missions, manages resource quotas (token usage), and handles error recovery across multiple files and components. Ideal for large feature builds, system refactoring, or complex applications requiring multiple, concurrent roles (Planner, Builder, Tester).

A comprehensive toolkit for structuring prospective clinical study designs. It assists in selecting and classifying primary/secondary endpoints, estimating sample size and power for various designs (means, proportions, survival), and scoring overall study plan feasibility (GO/NO-GO). All outputs are estimates routed to specified human experts (clinicians, biostatisticians, regulatory owners), never presented as final facts or regulatory submissions.

This skill converts vague coding requests into a single, actionable, and detailed project plan. It guides the user through defining scope, establishing concrete, ordered action items (discovery, changes, tests, rollout), and identifying potential risks or open questions, ensuring a robust development workflow.

This skill automates the debugging and fixing of failing Continuous Integration (CI) checks on GitHub PRs. It uses the GitHub CLI (gh) to locate failures, fetch detailed logs from GitHub Actions, summarize the failure context, and propose a structured fix plan. After user approval, it can implement the necessary code changes and verify the fix, streamlining the development cycle.

This meta-skill scans a target codebase path, intelligently determines the most appropriate architectural diagram type (e.g., sequence, component, class), and concurrently generates the diagram in two standardized formats: PlantUML source code and draw.io XML. Finally, it composes these deliverables into a cohesive architecture document (DOCX). Ideal for creating comprehensive onboarding guides or technical RFCs that require both text-friendly and highly editable views of the system architecture.

This meta-skill provides a comprehensive, step-by-step plan for complex software migrations. It intelligently coordinates multiple steps, including gathering migration requirements, classifying the source and target stacks (e.g., CJS to ESM, Py2 to Py3), searching authoritative guides, and analyzing repository diffs to ensure a guided, reliable upgrade path for large-scale projects.

This comprehensive skill provides expert guidance for integrating with Google NotebookLM programmatically, covering both the `nlm` Command Line Interface (CLI) and Measurement/Control Plane (MCP) tools. Use it to automate workflows, manage notebooks, add diverse sources (URLs, YouTube, Drive), and generate various complex content types, such as reports, quizzes, mind maps, and videos. Essential for developers building upon NotebookLM's advanced research capabilities.

This tool extracts and standardizes comprehensive paper-writing preferences from a user's initial request. It determines whether the generation should proceed directly using safe academic defaults or if it needs to initiate a preference interview, ensuring all structured data required for robust academic drafting is captured.

This tool generates a complete, publication-quality LaTeX fragment for a specific section of a research paper (e.g., Introduction, Method, Results). It strictly adheres to provided writing plans, structural conventions, citation budgets, and overall paper narratives, ensuring the output is ready for direct compilation and seamless integration into a full document.

This Main Agent acts as a central orchestrator for a specialized squad of AI agents, managing complex software development lifecycles. It guides projects through defined phases—from requirements gathering and planning to architecture, implementation, testing, and deployment. It maintains strict context discipline by summarizing agent outputs and referencing full artifacts, ensuring efficient, structured, and non-overwhelming communication to the user.

Alex converts high-level requirements into a precise, dependency-aware, and actionable implementation checklist. It maps critical paths, breaks down features into atomic micro-tasks, and establishes clear Definitions of Done (DoD) across various layers. Ideal for bridging the gap between conceptual goals and step-by-step engineering execution.