Skill UI

Generates and iterates high-performing ad creative—headlines, descriptions, primary text, and full variations—for any platform, using performance data to discover winning angles, stay within platform specs, and scale creative production.

Covers designing agent tools from schema creation through validation and error handling, emphasizing descriptions that keep LLMs accurate and reusable via MCP standards.

A comprehensive toolkit for App Store Optimization (ASO), designed to boost mobile app visibility and ranking on both Apple App Store and Google Play Store. This guide covers end-to-end strategies, including deep keyword research, competitor analysis, metadata optimization (title, subtitle, description), and conversion rate planning. Use this resource when planning app launches or aiming to improve app store SEO and discoverability.

A comprehensive toolkit for App Store Optimization (ASO), designed to boost mobile app visibility and ranking on both Apple App Store and Google Play Store. This guide covers end-to-end strategies, including deep keyword research, competitor analysis, metadata optimization (title, subtitle, description), and conversion rate planning. Use this resource when planning app launches or aiming to improve app store SEO and discoverability.

This skill automatically generates comprehensive and highly structured Pull Request (PR) descriptions directly from Git diffs. It goes beyond simple summaries by automatically adding detailed change categories (e.g., source, test, config), security risk assessments, and required review checklists based on the modified file types. Use this when submitting complex code changes or when the diff is large and requires explicit structure for thorough review.

This skill automates the creation of comprehensive documentation for relational databases (PostgreSQL/MySQL). By introspecting live schemas, it extracts detailed metadata including table structures, column descriptions, primary/foreign key relationships, indexes, views, and stored procedures. The output provides a data dictionary, entity-relationship map, and schema statistics, crucial for knowledge transfer and database lifecycle management.

This skill acts as a PR optimization expert, designed to help users create high-quality pull requests. It generates comprehensive descriptions, automates review processes, and ensures adherence to best practices regarding clarity, documentation, and test coverage. It streamlines the entire code review lifecycle.

Transforms messy invoices, receipts, and bank docs into structured folders by reading files, extracting vendors, dates, amounts, and descriptions, renaming them consistently, and sorting copies ready for tax prep or accountant handoff.

This architecture provides a scalable, production-ready framework for integrating AI candidate analysis with Juicebox. It manages automated data ingestion from job descriptions, ranks candidates using intelligent scoring, handles result caching, and facilitates seamless data export to major ATS platforms like Greenhouse and Lever. Designed for high-volume, complex hiring pipelines requiring data freshness, deduplication, and robust scaling.

This specialized skill generates comprehensive, best-practice SEO metadata packages, including optimized titles, compelling descriptions, and structured URLs. It ensures strict compliance with character limits (e.g., 150-160 characters) while strategically integrating primary and secondary keywords, emotional triggers, and clear CTAs. Use this when launching new content or needing A/B test variations to maximize search visibility and click-through rates across platforms.

Analyzes job descriptions and candidate data to reorganize experience, emphasize relevant skills, and output ATS-friendly resumes tailored for specific roles and industries, helping job seekers deliver targeted applications.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.