Skill UI

This skill revises legal prose to match the high standards of major academic journals, ensuring clarity and readability for a generalist legal audience (e.g., non-specialist scholars, judges, and practitioners). It focuses on making the argument visible in the main text, maintaining a confident, plain voice, and properly balancing supporting material in footnotes with core reasoning in the body. Ideal for final polish passes before submission.

This skill replaces detected Protected Health Information (PHI) in clinical notes with realistic, type-matched synthetic surrogates. Unlike simple redaction, surrogates maintain the natural readability and structure of the text, ensuring the data remains parseable for downstream NLP tasks, QA, or human review. It supports consistent, reproducible fake identities and allows custom identifier generation for proprietary formats.

This tool automatically formats Python code using the industry-standard 'black' formatter. It ensures your codebase adheres to consistent style guidelines, improving readability and maintainability across the entire team. Ideal for development workflows and code reviews.

This skill automatically cleans up and prettifies Markdown documents. It fixes structural inconsistencies such as incorrect heading levels and normalizes various list styles. It is useful for ensuring consistent formatting across large markdown files, optimizing readability, and preparing content for documentation or publishing.

An advanced tool that audits legal documents (such as contracts, NDAs, or policies) against established legal design principles. It scores the text across six pillars: language patterns, readability metrics (Flesch, LIX, etc.), structure, hidden clauses, statutory duplication, and visual hierarchy. It provides a non-rewriting assessment report to help users understand the clarity, complexity, and compliance risk of their legal drafting.

Apply Robert C. Martin-inspired clean code rules to improve code readability, naming conventions, function design, and responsibilities. This skill ensures everyday implementation quality, readable control flow, and testable code by following established best practices for software development.

This skill converts dense text, articles, or complex notes into clean, highly structured, and hierarchical bullet points. It is ideal for summarization, academic studying, or creating organized notes where strict readability and clear parent-child relationships are required. It eliminates prose and paragraphs, enforcing a pure bullet format for maximum structural clarity and memorization.

This expert AI code reviewer evaluates code across correctness, security, performance, design, and readability. It identifies bugs, vulnerabilities, and anti-patterns with actionable fix suggestions. Supports Python, JavaScript, Go, and Rust. Provides structured feedback with metrics and positive notes to improve code quality.

This skill simplifies and refines code to enhance clarity, consistency, and maintainability without altering functionality. It applies project-specific best practices to improve readability, reduce complexity, and align with coding standards. Ideal for cleaning up recently modified code or optimizing existing logic for better structure.

This skill evaluates content for AI citability based on research from Princeton and Georgia Tech. It scores content on answer block quality, self-containment, and structural readability to optimize visibility in AI-generated responses. Ideal for SEO and GEO strategies to ensure AI models extract and cite your content effectively.

Max is a specialized agent responsible for cleaning up and improving existing code without altering its behavior. It focuses on algorithmic optimization, code abstraction, dead code removal, and readability improvements. Max ensures all changes maintain test suite integrity and follows strict refactoring rules. It is invoked only when explicitly requested to refactor proven code, ensuring safety and stability in software development workflows.

This skill provides language-specific super-code guidelines for Python, covering idioms, efficiency, and best practices. It includes recommendations on comprehensions, unpacking, built-ins, functions, classes, error handling, type hints, and common anti-patterns to improve code quality and readability.