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AI Skills

A portable, shareable collection of AI agent skills — structured instruction packs that teach coding agents how to perform specific tasks consistently.

What Are AI Skills?

AI skills are Markdown files (SKILL.md) that follow a standard format. Each skill contains:

  • Metadata (name, description, trigger phrases, allowed tools)
  • Structured instructions the agent follows step-by-step
  • Examples, anti-patterns, and best practices

When an AI agent loads a skill, it adopts that skill's workflow for the duration of the task. This means:

  • Consistent output — every agent produces the same quality changelog, README, or commit message
  • Portable knowledge — skills work across OpenWork, Claude Code, Codex CLI, and compatible agents
  • Version-controlled expertise — skills live in git, so they evolve with your team

Why Host Skills in a Repository?

1. Portability Across Agents and Machines

AI coding agents are becoming the new IDE. But each agent starts from zero — it doesn't know your team's conventions. A skill repository acts as a shared brain:

  • Clone the repo into any project's .opencode/skills/, .claude/skills/, or .agents/skills/ directory
  • Every agent on every machine immediately knows how to write your changelogs, READMEs, and commit messages
  • No more "wait, which format do we use for changelogs?"

2. Version Control for Expertise

Just like code, agent behavior should be reviewable and auditable:

  • Track changes to your conventions over time via git history
  • Review skill updates in pull requests before they affect agent behavior
  • Roll back if a skill change produces unexpected results
  • Tag releases so teams can pin to a known-good version of your skills

3. Team Alignment

Without skills, each developer's AI agent independently invents formatting, structure, and tone. With shared skills:

  • Changelogs follow Keep a Changelog v1.1.0 every time
  • Commit messages follow Conventional Commits v1.0.0 every time
  • READMEs follow a consistent structure every time
  • New team members get the same quality output as veterans

4. Separation of Concerns

Skills live outside your application code:

  • No dependency on a specific project's structure
  • Reusable across monorepos, microservices, libraries, and apps
  • Easy to update all projects at once by updating the shared skill repo

5. Community and Ecosystem

Open skill repos enable sharing:

  • Fork and customize skills for your team's needs
  • Contribute improvements back to the community
  • Discover patterns others have solved well
  • Build on each other's work instead of starting from scratch

Repository Structure

skills/
├── README.md                          # This file
├── bash-scripting/
│   └── SKILL.md                       # Bash scripting patterns and best practices
├── brew-update/
│   └── SKILL.md                       # Homebrew update/upgrade/cleanup workflow
├── conventional-changelog/
│   └── SKILL.md                       # Keep a Changelog v1.1.0 format
├── conventional-git/
│   └── SKILL.md                       # Conventional Commits v1.0.0 branch/commit naming
├── conventional-readme/
│   └── SKILL.md                       # README structure based on makeareadme.com
├── disk-cleanup/
│   └── SKILL.md                       # Remote disk cleanup for Debian/Ubuntu
└── linux-system-audit/
    └── SKILL.md                       # Read-only Linux/VPS/container-host audit and report

Skills Included

Skill Description Trigger Phrases
bash-scripting Robust Bash script patterns, error handling, and idioms "write a bash script", "shell script"
brew-update Homebrew update, upgrade, and cleanup workflow "brew update", "update packages"
conventional-changelog Keep a Changelog v1.1.0 format for CHANGELOG.md "update changelog", "release notes"
conventional-git Conventional Commits v1.0.0 for branches, worktrees, and messages "name a worktree", "commit message"
conventional-readme README structure and best practices from makeareadme.com "write a readme", "improve readme"
disk-cleanup Deploy and run disk cleanup on remote servers "clean disk", "free space"
linux-system-audit Read-only Linux/VPS/container-host audit with a prioritized Markdown report "audit this server", "VPS report"

How to Use

Each agent auto-detects skills placed in its skills directory — no registration step. Use the global path for skills you want available everywhere, or the per-project path to scope a skill to one repo (e.g. project-specific conventions).

OpenCode / OpenWork

# Global (all projects)
cp -r conventional-git/ ~/.config/opencode/skills/

# Per-project
cp -r conventional-git/ /path/to/project/.opencode/skills/

Claude Code

# Global (all projects)
cp -r conventional-git/ ~/.claude/skills/

# Per-project
cp -r conventional-git/ /path/to/project/.claude/skills/

Codex CLI

Codex discovers skills under .agents/skills directories (repo-level, checked from the current working directory up to the repo root, then user- and system-level):

# Global (all projects)
cp -r conventional-git/ ~/.agents/skills/

# Per-project (repo root)
cp -r conventional-git/ /path/to/project/.agents/skills/

Codex detects skill changes automatically — no restart needed.

Creating Your Own Skills

Each skill is a single directory with a SKILL.md file. The frontmatter defines metadata, and the body contains the instructions.

---
name: my-skill
description: What this skill does and when to trigger it.
user-invocable: true
allowed-tools: Read Edit Write Bash
---

# Skill Title

Instructions the agent follows...

See samber/cc-skills for a large collection of community skills.

Skill Format Specification

Field Required Description
name Yes Unique identifier (lowercase, hyphens)
description Yes What the skill does and when to trigger it
user-invocable No Whether the user can invoke it directly (default: false)
allowed-tools No Tools the skill is permitted to use
argument-hint No Hint for required arguments
metadata No Author, version, source URL, and other metadata

License

MIT

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A portable, shareable collection of AI agent skills — structured instruction packs that teach coding agents how to perform specific tasks consistently.

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