Agent Skills Hub

manage-worktree

Manage git worktrees: create, move branches into, or remove worktrees. Simplifies parallel development, context switching, and cleanup for Apartment-based Rails projects.

dynamic-programming

Master DP patterns with complete implementations for memoization, tabulation, and state design for production-ready solutions.

data-visualization

Create professional data visualizations with Python using matplotlib, seaborn, and plotly. Includes chart selection guidance, design principles, accessibility standards, and code patterns for publication-quality figures.

feature-prioritization

A structured decision-making tool that applies RICE, MoSCoW, Kano, and value-effort frameworks to prioritize software features, roadmap items, and build-vs-defer decisions with data-driven objectivity.

pr-template

Generate professional pull request descriptions using Grey Haven Studio standards, ensuring clear summaries, motivation, technical implementation details, and testing strategies.

context-fundamentals

Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.

flow-nexus-platform

Comprehensive management for the Flow Nexus platform, covering user authentication, sandbox execution, app deployment, credit management, and gamified challenges.

vibesafe

Manage Vibesafe units to scan, generate, test, and verify AI-written code with cryptographically-secure hash-locked checkpoints.

qa

Browser-based QA automation for web applications. Performs automated site audits, visual regression testing, user flow verification, and issue tracking with real-time browser snapshots.

wycheproof

Validates cryptographic implementations using the Google Wycheproof test vector suite to detect security edge cases and known vulnerabilities.

agents-md-generator

Generate hierarchical, token-efficient AGENTS.md files for AI coding agents to provide repository-wide context and project-specific guidelines.

strategic-compact

Enhance workflow efficiency by performing manual context compaction at logical task boundaries instead of relying on unpredictable auto-compaction.