Agent Setup

AI DevKit provides two commands for agent setup: ai-devkit init creates your project configuration (.ai-devkit.json), and ai-devkit install applies it to your workspace. Together they give you repeatable setup, easy onboarding, and consistent agent files after configuration changes.

For teams adopting AI coding agents across multiple tools, this setup becomes the shared foundation of the control plane: one config that generates workflow docs, skills, MCP server files, and environment-specific instructions.

Before running these commands:

• Install AI DevKit (npm install -g ai-devkit) or use npx ai-devkit@latest ...
• Run commands from your project root directory
• Make sure you have permission to create or update agent-related files in the repository

Key concepts:

• Environment: An AI coding tool you work with, such as Cursor, Claude Code, or Codex. AI DevKit generates the configuration files each environment expects.
• Phase: A stage of the software development lifecycle, such as requirements, design, or testing. AI DevKit provides document templates for each phase.

Use ai-devkit init when:

• You are setting up AI DevKit in a project for the first time
• You want interactive prompts to choose environments and phases
• You want non-interactive bootstrap from a template file (ai-devkit init --template)
• You want to install AI DevKit built-in skills (prompted interactively, or pass --built-in for CI)

Use ai-devkit install when:

• .ai-devkit.json already exists
• You want deterministic setup without re-answering prompts
• You want to restore missing agent files or command folders to match your config

The simplest way to get started is the interactive setup. This walks you through choosing environments, phases, and built-in skills:

ai-devkit init

Once .ai-devkit.json exists in your project, apply or re-apply the setup with:

ai-devkit install

After install completes successfully, you should usually see:

• .ai-devkit.json in your project root
• Environment-specific skill folders such as .cursor/skills/, .claude/skills/, .github/skills/, or .agents/skills/
• MCP config files such as .mcp.json or .codex/config.toml if your config includes MCP servers for supported environments

After running, you will see a summary like:

✔ Install Summary
  ✔ 3 environment(s) installed
  ✔ 5 phase template(s) installed
  ✔ 2 skill(s) installed

Use a non-default config file if your project stores AI DevKit config elsewhere:

ai-devkit install --config ./.ai-devkit.team.json

Overwrite existing install artifacts without extra prompts:

ai-devkit install --overwrite

Based on your configured environments, AI DevKit installs or updates files such as:

• Agent skill files (for example .cursor/skills/, .claude/skills/, .github/skills/ for GitHub Copilot, .devin/skills/, and .agents/skills/ for Codex) refer Skills for more on skills
• MCP server configuration files (.mcp.json for Claude Code and GitHub Copilot, .codex/config.toml for Codex, opencode.json for opencode, .junie/mcp/mcp.json for Junie, .devin/config.json for Devin, .roo/mcp.json for Roo Code, .kilo/kilo.jsonc for Kilo Code)
• Other environment-specific templates defined by AI DevKit

The exact artifacts depend on the environments configured in .ai-devkit.json. ai-devkit install only manages project-local files generated from that config.

Once .ai-devkit.json is committed to your repository, teammates and CI pipelines can reproduce the same setup with a single command:

ai-devkit install

Each teammate still needs the AI DevKit CLI available locally, either from a global install (npm install -g ai-devkit) or by using npx ai-devkit@latest install.

For repeatable, non-interactive setup, create a template file. This is useful for sharing a standard configuration across teams or running in CI.

Create a file named fullstack-engineer.yaml in your project root with this content:

environments:
  - cursor
  - claude
  - codex
phases:
  - requirements
  - design
  - planning
  - implementation
  - testing
paths:
  docs: docs/ai
skills:
  - registry: codeaholicguy/ai-devkit
    skill: structured-debug
  - registry: codeaholicguy/ai-devkit
    skill: dev-lifecycle

Initialize from that template:

ai-devkit init --template ./fullstack-engineer.yaml

Use a custom directory for AI documentation in either interactive or template mode (default is docs/ai):

ai-devkit init --docs-dir ./ai-docs

Templates can include MCP server definitions. Add a mcpServers section to your template:

Note: mcpServers support requires AI DevKit 0.23.0 or later.

mcpServers:
  memory:
    transport: stdio
    command: npx
    args:
      - -y
      - "@ai-devkit/memory"

The -y flag lets npx run non-interactively, which is recommended for repeatable setup and CI.

After running ai-devkit init --template, MCP server definitions are saved to .ai-devkit.json. Run ai-devkit install to generate the agent-specific MCP config files. For the full mcpServers field reference, see Configuration File.

Field	Required	Description
environments	Yes*	List of AI environments to configure (cursor, claude, codex, etc.)
phases	Yes*	List of SDLC phases (requirements, design, planning, implementation, testing)
paths.docs	No	Custom directory for phase documents (default: docs/ai)
skills	No	List of skills to install, each with registry and skill fields
mcpServers	No	MCP server definitions (see MCP Servers for field details)

*If omitted, ai-devkit init will prompt you to select them interactively. Required for fully non-interactive runs.

When running ai-devkit init interactively (without a template), you are prompted to install AI DevKit's built-in skills. In non-interactive environments such as CI, pass --built-in to install them automatically:

ai-devkit init --environment cursor,claude --all --built-in

The --all flag selects all available phases. Combined with --environment and --built-in, this gives a fully non-interactive setup.

When using a template with a skills section, skills from the template are installed from that configuration instead of using the interactive built-in skills prompt. In that case, avoid combining the template with --built-in unless you intentionally want built-in skills added separately.

MCP (Model Context Protocol) servers give AI agents extended capabilities such as persistent memory, database access, or external tool integrations. Define MCP servers in .ai-devkit.json and AI DevKit generates the environment-specific config files for you.

Note: mcpServers generation and install flow require AI DevKit 0.23.0 or later.

MCP configuration is currently generated for Claude Code (.mcp.json), GitHub Copilot (.mcp.json), Codex (.codex/config.toml), opencode (opencode.json), Junie (.junie/mcp/mcp.json), Devin (.devin/config.json), Roo Code (.roo/mcp.json), and Kilo Code (.kilo/kilo.jsonc).

If your project uses only environments that do not currently support MCP generation, AI DevKit still saves the mcpServers definitions in .ai-devkit.json, but no environment-specific MCP config files are created.

You can define the same mcpServers configuration either in a template file used with ai-devkit init --template or directly in .ai-devkit.json. Both approaches work. After initialization, ai-devkit install always reads the final mcpServers values from .ai-devkit.json.

For the full mcpServers field reference, supported keys, and transport-specific examples, see Configuration File.

Add an mcpServers object to .ai-devkit.json like this:

{
  "mcpServers": {
    "memory": {
      "transport": "stdio",
      "command": "npx",
      "args": ["-y", "@ai-devkit/memory"],
      "env": { "DB": "./db" }
    },
    "notion": {
      "transport": "http",
      "url": "https://mcp.notion.com/mcp",
      "headers": { "Authorization": "Bearer token" }
    }
  }
}

Then run ai-devkit install to generate the MCP config files for each supported environment.

When ai-devkit install finds an existing MCP server with the same name but different configuration:

• Interactive mode: You are prompted to skip, overwrite all, or choose per server.
• Non-interactive mode (CI): Conflicts are skipped by default. Pass --overwrite to force replacement.

Servers not managed by AI DevKit are preserved and never modified.

Run:

ai-devkit init

This creates the configuration file used by install.

If you prefer non-interactive setup, use the template command shown in Template-based Setup.

If you want to force replacement of install-managed artifacts, run:

ai-devkit install --overwrite

Re-run:

ai-devkit install

This re-applies setup using the current .ai-devkit.json content.

• Configuration File
• Supported AI Agents & Environments
• Getting Started
• Development with AI DevKit