ANTIGRAVITY LABJP
Articles/Agents & Manager
Agents & Manager/2026-05-06Intermediate

gh skill: Sharing AI Agent Knowledge Across Claude Code, Copilot, Cursor, and Gemini CLI

The gh skill GitHub CLI extension lets you package SKILL.md definitions and distribute them across 30+ AI coding agents. Here's how it works and how to get started.

gh skill2AI agents23SKILL.md2Claude Code8GitHub Copilot4Cursor19multi-agent48

The argument about which AI coding tool a team should standardize on misses something important: standardizing the tool is less valuable than standardizing the knowledge the tool operates on. That's what gh skill enables.

gh skill is a GitHub CLI extension that packages SKILL.md skill definitions and distributes them across Claude Code, GitHub Copilot, Cursor, Codex CLI, Gemini CLI, and 30+ other AI agents — from a single public repository.

The Problem It Solves

AI coding agent diversity is accelerating. Individual developers switch between Claude Code and Copilot depending on context. Teams have members who prefer different tools. This is fine for autonomy, but it creates inconsistency: the code style rules encoded in one agent's config don't reach the others.

gh skill makes skill definitions portable. Instead of maintaining parallel configurations for each tool, you maintain one source of truth and distribute it.

How It Works

When you install a skill package, gh skill places the definitions where each agent looks for them:

  • Claude Code: .claude/skills/
  • GitHub Copilot: VS Code settings
  • Cursor: .cursor/skills/
  • Gemini CLI: ~/.gemini/skills/

One install command handles all target agents.

Basic Usage

# Install gh CLI if needed
brew install gh
 
# Add the gh skill extension
gh extension install github-actions/gh-skills
 
# Install a skill package from a public repo
gh skill install owner/repository-name
 
# List installed skills
gh skill list
 
# Install for a specific agent only
gh skill install owner/repo --agent claude-code
 
# Update installed skills
gh skill update skill-name
 
# Remove a skill
gh skill uninstall skill-name

Skill Package Structure

A skill package needs a skill.yaml metadata file alongside the Markdown skill files:

# skill.yaml
name: team-coding-skills
version: 1.0.0
description: "Shared coding standards for the team"
agents:
  - claude-code
  - copilot
  - cursor
skills:
  - path: skills/code-review.md
  - path: skills/commit-conventions.md
  - path: skills/testing-standards.md

The Markdown files follow the same conventions as existing SKILL.md files. Here's an example:

<!-- skills/code-review.md -->
# Code Review Guidelines
 
When asked to review code, check in this order:
 
1. Type safety: nullable handling, safe casts
2. Error handling: exceptions caught and logged appropriately
3. Performance: unnecessary object creation, potential memory leaks
4. Testability: dependencies injectable, no tight coupling
5. Readability: names accurately reflect intent
 
Format feedback as:
- The problematic code
- A specific explanation of the issue
- A corrected code example

What Different Agents Actually Understand

Not all agents interpret skill definitions equally:

Claude Code handles the richest instruction set — conditional behavior, tool restrictions, multi-step workflows. GitHub Copilot reads Markdown instructions but applies them more loosely; complex conditional logic is often ignored. Cursor sits in the middle.

Practical recommendation: write shared skills in plain, direct language so they transfer across agents. Put Claude Code–specific configurations (hooks, tool restrictions) in separate files under agent-overrides/ and target those files to Claude Code only.

Migrating Existing SKILL.md Files

If you're a Claude Code user with existing SKILL.md content, you can make it distributable without rewriting anything:

my-skills-repo/
├── skill.yaml        ← Add this
├── README.md
└── skills/
    ├── existing-skill.md   ← Your existing content
    └── another-skill.md

The skill.yaml references your existing files. The content is distributed as-is, and other agents pick up what they can interpret.

Start Small

One skill file is enough to get started — your commit message conventions, or a short list of code style rules. Introduce the mechanics to your team, then grow the skill library incrementally.

The premium article covers production-grade multi-agent skill management: versioning strategy, role-based profiles, CI validation, and ensuring consistency across agents as the skill library grows.

Share

Thank You for Reading

Antigravity Lab is ad-free, supported entirely by members like you. We publish practical guides daily with implementation code, benchmarks, and production-ready patterns. If you've found it useful, we'd love to have you on board.

  • Copy-paste ready implementation code
  • New advanced guides published daily
  • $5/mo or $10 for lifetime access
View Membership →

If you found this article helpful, a small tip ($1.50) would mean a lot to us. Your support helps keep this site ad-free and covers server and hosting costs.

Related Articles

Agents & Manager2026-05-06
Enterprise Multi-Agent Skill Management with gh skill — Profiles, CI Validation, and Cross-Agent Consistency
A complete architecture for managing SKILL.md across multi-agent teams. Covers role-based skill profiles, three-layer agent compatibility design, GitHub Actions CI validation, and automated release notifications.
Agents & Manager2026-05-05
2026 AI Coding Agents Compared: How to Use Claude Code, OpenCode, Cursor, and Gemini CLI Together
The 2026 AI coding agent landscape is fragmented on purpose. Claude Code, OpenCode, Cursor, and Gemini CLI each have distinct strengths. This guide builds a practical framework for choosing and combining them based on project needs.
Agents & Manager2026-05-04
AI Agent Orchestration Design Patterns — Task Decomposition, Handoffs, and Loop Control
A practical look at the design challenges you encounter when actually running AI agents: task decomposition granularity, sub-agent handoff structures, and reliable loop termination.
📚RECOMMENDED BOOKS
Build a Large Language Model (From Scratch)
Sebastian Raschka
LLM Dev
Prompt Engineering for LLMs
Berryman & Ziegler
Prompting
AI Engineering
Chip Huyen
AI Eng
* Contains affiliate links
See all →