One skill to create them all.
Metaskill is a Claude Code skill that creates AI agent teams, individual agents, and custom skills — all through a single /metaskill command.
# Generate a full agent team for a project
/metaskill ios app with SwiftUI
/metaskill fullstack web app with React and PostgreSQL
/metaskill data science pipeline with PyTorch
/metaskill game dev with Unity and C#
# Create a single agent
/metaskill a security reviewer agent for Go microservices
/metaskill a code reviewer agent
# Create a single skill
/metaskill a deploy-to-staging skill with Docker
/metaskill a lint-and-format skillOne command. It detects your intent and routes to the right flow automatically.
When you describe a project type, Metaskill runs a 4-phase process:
Phase 1: RESEARCH
├── Web search: real-world team structures for your domain
├── Web search: existing Claude Code agent configs on GitHub
├── Web search: MCP servers relevant to your tech stack
└── Web search: best practices, linters, testing frameworks
Phase 2: BUILD
├── CLAUDE.md — routing table + orchestration protocol
├── .claude/agents/ — 4-6 agents (tech-lead + specialists + reviewer)
├── .claude/skills/ — 2-4 workflow automation skills
├── .claude/rules/ — coding standards for the primary language
└── .mcp.json — MCP server configuration
Phase 3: CREDENTIALS
└── Asks for any API keys/tokens needed by MCP servers
(always offers "skip / configure later")
Phase 4: VERIFY
└── Lists all created files, validates .mcp.json, prints summary
The research phase is what makes each generated team genuinely useful — it's not a fixed template. It adapts to what actually matters in your domain right now.
When your request mentions "agent", "reviewer", or a specific role, Metaskill creates a single .claude/agents/<name>.md with:
- Expert persona design
- Frontmatter configuration (model, tools, permissions)
- System prompt with self-verification and Workflow Discipline built in
When your request mentions "skill", "command", or "slash command", Metaskill creates a single .claude/skills/<name>/SKILL.md with:
- Scope and invocation model configuration
- Dynamic context injection
- Argument handling and tool access
ios-app-agents/
├── CLAUDE.md ← routing table + orchestration protocol
├── .mcp.json ← MCP servers, auto-discovered by Claude Code
└── .claude/
├── agents/
│ ├── tech-lead.md ← Opus model, routes all tasks
│ ├── ios-engineer.md ← SwiftUI, platform APIs
│ ├── ui-designer.md ← layouts, animations, design system
│ ├── test-engineer.md ← XCTest, UI testing
│ └── code-reviewer.md ← quality gate for all code changes
├── skills/
│ ├── build-and-test/SKILL.md
│ └── run-simulator/SKILL.md
└── rules/
└── swift-standards.md
After generation:
cd ios-app-agents
claude
> "Build a SwiftUI todo app with iCloud sync"The tech-lead agent breaks the task down and delegates to specialists. You supervise, they build.
curl -fsSL https://raw.githubusercontent.com/xvirobotics/metaskill/main/install.sh | bashThis installs /metaskill to ~/.claude/skills/metaskill/.
Or manually:
mkdir -p ~/.claude/skills/metaskill/flows
curl -fsSL https://raw.githubusercontent.com/xvirobotics/metaskill/main/skill/SKILL.md \
-o ~/.claude/skills/metaskill/SKILL.md
curl -fsSL https://raw.githubusercontent.com/xvirobotics/metaskill/main/skill/flows/team.md \
-o ~/.claude/skills/metaskill/flows/team.md
curl -fsSL https://raw.githubusercontent.com/xvirobotics/metaskill/main/skill/flows/agent.md \
-o ~/.claude/skills/metaskill/flows/agent.md
curl -fsSL https://raw.githubusercontent.com/xvirobotics/metaskill/main/skill/flows/skill.md \
-o ~/.claude/skills/metaskill/flows/skill.mdRequirements: Claude Code CLI installed and authenticated.
Modern AI coding agents are powerful, but setting up a well-structured multi-agent team takes hours: designing roles, writing system prompts, choosing MCP servers, configuring routing. Metaskill automates that entire setup step.
The name is a reference to the philosophical concept of a meta-skill — a skill that makes you better at acquiring other skills. In this case: a skill that generates the skills (and agents, and rules) you need to work on any kind of project.
One prompt. One team. Start building.
Every generated team follows this pattern:
| Role | Model | Responsibility |
|---|---|---|
tech-lead |
Opus | Routes tasks, coordinates agents, never implements directly |
<specialist-1> |
Sonnet | Domain expert #1 (e.g. frontend, iOS, data engineering) |
<specialist-2> |
Sonnet | Domain expert #2 |
<specialist-3> |
Sonnet | Domain expert #3 (optional) |
code-reviewer |
Sonnet | Quality gate — all code passes through here |
Orchestration protocol (in every generated CLAUDE.md):
- Tech-lead is the routing authority
- Main Claude never implements directly for multi-step tasks — it delegates
- Structured handoff documents between agents
- Code reviewer is a mandatory quality gate
- All agents follow built-in Workflow Discipline: plan-first, re-plan on failure, verify before done, autonomous execution
Metaskill selects MCP servers based on your domain. Verified catalog:
| Server | Package | Purpose | Transport |
|---|---|---|---|
context7 |
@upstash/context7-mcp@latest |
Up-to-date library docs | stdio |
playwright |
@playwright/mcp@latest |
Browser automation & e2e testing | stdio |
filesystem |
@modelcontextprotocol/server-filesystem |
Enhanced file operations | stdio |
postgres |
@modelcontextprotocol/server-postgres |
Database queries | stdio |
sequential-thinking |
@modelcontextprotocol/server-sequential-thinking |
Structured multi-step reasoning | stdio |
memory |
@modelcontextprotocol/server-memory |
Persistent knowledge graph | stdio |
github |
https://api.githubcopilot.com/mcp/ |
GitHub API access | HTTP |
All servers are written to .mcp.json and auto-discovered by Claude Code on launch. No claude mcp add needed.
The examples/ directory contains real, usable agent teams generated by Metaskill. Each is a complete project you can copy and use directly:
| Example | Stack | Agents | Skills |
|---|---|---|---|
fullstack-web/ |
React + Node.js + PostgreSQL | tech-lead, frontend-engineer, backend-engineer, devops-engineer, code-reviewer | build-and-test, deploy-preview, api-test |
ios-app/ |
SwiftUI + Swift | tech-lead, ios-engineer, ui-designer, test-engineer, code-reviewer | build-and-test, run-simulator |
data-science/ |
Python + PyTorch | tech-lead, data-engineer, ml-engineer, analyst, code-reviewer | run-pipeline, evaluate-model, generate-report |
Try one:
cp -r examples/fullstack-web my-project
cd my-project && claudemetaskill/
├── skill/
│ ├── SKILL.md ← /metaskill entry point (intent routing)
│ └── flows/
│ ├── team.md ← full agent team generation flow
│ ├── agent.md ← single agent creation flow
│ └── skill.md ← single skill creation flow
├── install.sh ← installer
└── examples/ ← example generated outputs
PRs welcome. The skill is modular — edit the specific flow file for what you want to improve:
- Fork the repo
- Edit the relevant file in
skill/flows/ - Test in Claude Code with
/metaskill - Submit a PR with a before/after example
If you use Metaskill in research, please cite:
@software{sung2025metaskill,
author = {Sung, Flood},
title = {Metaskill: A Meta-Skill for Autonomous AI Agent Team Generation},
year = {2025},
url = {https://github.com/xvirobotics/metaskill},
license = {MIT}
}A CITATION.cff file is also provided for GitHub's "Cite this repository" button.
MIT © XVI Robotics
