AI Code Toolkit

This repo provides:

• project and feature scaffolding via templates
• file-level design guidance before edits
• rule-based review after edits
• design-system discovery for frontend work

Why This Exists

As projects scale, conventions in docs like CLAUDE.md, AGENTS.md, and style guides become hard to keep concise and consistently applied by AI agents. This toolkit moves those conventions into reusable template configs (scaffold.yaml, architect.yaml, RULES.yaml) so agents can discover only the relevant guidance when needed.

Purpose-based plugins

For toolkit users who prefer an agent plugin, install only the workflows you need: aicode-bootstrap (project creation), aicode-develop (features and patterns), aicode-review (rules-based review), aicode-admin (authoring reusable configuration), or aicode-design (frontend discovery). Self-contained plugin roots are under plugins/ and work with the Claude Code/Cowork marketplace, Codex marketplace, and local Gemini extension installation; Cursor/Grok and Cowork's local stdio runtime still need client acceptance tests. Read the installation and compatibility guide before use. These bundles group workflows rather than restrict tool access.

Initialize the consumer project before starting a scaffold-backed plugin with interactive npx -y @agiflowai/aicode-toolkit@2.0.0 init --skip-mcp; plugin installation does not supply templates. Keep MCP server cwd in the consumer workspace. Published style-system 0.2.0 currently ignores some discovery filters until the dispatch fix is released. See compatibility.

Quick Start

Requirements:

• Node.js >= 22.12 (Node 24 recommended)
• an MCP-compatible agent such as Claude Code, Cursor, or Gemini CLI

1. Initialize a Workspace

# Existing project
npx @agiflowai/aicode-toolkit init

# New project
npx @agiflowai/aicode-toolkit init --name my-app --project-type monolith

This creates templates/ and .toolkit/settings.yaml. Projects reference templates through sourceTemplate in project.json or .toolkit/settings.yaml.

2. Configure MCP

init can configure MCP automatically. For manual setup, add the servers you need to your agent config.

Example for Claude Code:

{
  "mcpServers": {
    "scaffold-mcp": {
      "command": "npx",
      "args": ["-y", "@agiflowai/scaffold-mcp", "mcp-serve", "--admin-enable"]
    },
    "architect-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@agiflowai/architect-mcp",
        "mcp-serve",
        "--admin-enable",
        "--design-pattern-tool",
        "codex",
        "--review-tool",
        "gemini-cli"
      ]
    },
    "style-system": {
      "command": "npx",
      "args": ["-y", "@agiflowai/style-system", "mcp-serve"]
    }
  }
}

Useful flags:

• --admin-enable: enable admin/template-authoring tools
• --design-pattern-tool <tool>: use an LLM to filter design patterns
• --review-tool <tool>: use an LLM for review output

3. Verify

Ask the agent:

What boilerplates are available?

It should call list-boilerplates. If not, restart the agent.

Repo Layout

AI agent
  ├─ scaffold-mcp
  ├─ architect-mcp
  ├─ style-system
  └─ one-mcp
        ↓
     templates/
       ├─ scaffold.yaml
       ├─ architect.yaml
       └─ RULES.yaml

scaffold-mcp

Generates projects and feature boilerplate from templates.