Agent Skills Hub

Project-Management-Interactive-copilot-test

A universal skill for automating GitHub Project V2 Kanban boards, supporting status transitions, sprint management, and interactive workflows via CLI.

cc-stack-analysis

Expert tool for auditing and validating the structural integrity, naming conventions, and best practices of Claude Code configurations, including skills, hooks, and commands.

git-commit-formatter

Standardize git commits with conventional formatting, signatures, and automated context gathering for better project history.

pix

An autonomous UI implementation agent that converts Figma designs into pixel-perfect code using Figma MCP and browser-based refinement.

proofread

Automated, non-destructive proofreading for LaTeX and Quarto lecture files, generating quality reports for grammar, typos, and academic style.

goal-tracking

Cascading goal tracking system connecting 3-year vision to daily tasks. Automates progress calculation, stalled goal detection, and project-to-goal alignment for Obsidian vaults.

github-navigator

GitHub operations via gh CLI. Use for repository inspection, issues, PRs, releases, and deep codebase analysis including cloning for architectural insights.

agent-system

Implementation patterns for MERIDIAN autonomous AI agents using Claude API, including BaseAgent lifecycle, structured tool use, token budget enforcement, and cron scheduling.

code-review-specialist

A specialized code review agent that performs multi-dimensional analysis covering security vulnerabilities, performance optimization, code quality, and maintainability standards.

project-guidelines-example

A project-specific template skill providing standardized architecture, file structures, coding patterns, and deployment workflows for production-grade AI applications.

Create New Skills

A framework for creating reusable Claude Code agent skills, following best practices for directory structure, progressive disclosure, and multi-file patterns.

inno-prepare-resources

Automates research resource preparation by loading instances, searching GitHub for codebases, building dataset descriptions, and downloading arXiv papers.