Skip to main content 홈 크리에이터 yanacuti1121 yana-ai agent-factory
agent-factory Claude Code agent generation system that creates custom agents and sub-agents with enhanced YAML frontmatter, tool access patterns, and MCP integration support following proven production patterns
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Agent Factory
A comprehensive system for generating production-ready Claude Code agents and sub-agents. This skill provides templates, standards, and generation tools to create custom agents that seamlessly integrate with Claude Code's agent system.
What This Skill Does
This skill helps you create custom Claude Code agents for any domain or workflow. It generates properly formatted agent files that Claude Code can automatically discover and invoke when relevant.
Capabilities
Generate Custom Agents - Create specialized agents for any domain (frontend, backend, testing, product, etc.)
Enhanced YAML Frontmatter - Rich metadata including color coding, field categorization, expertise levels
Tool Access Guidance - Recommends optimal tool configurations based on agent type
MCP Integration - Suggests relevant MCP server tools for enhanced capabilities
Execution Pattern Assignment - Ensures proper parallel/sequential execution for safety
Validation - Checks agent configuration against best practices
Agent Types Supported
Strategic Agents (Lightweight, Parallel-Safe)
Purpose : Planning, research, analysis
Tools : Read, Write, Grep only
Execution : 4-5 agents can run in parallel
Color : Blue
Examples : product-planner, market-researcher, architect
Implementation Agents (Full Tools, Coordinated)
Purpose : Code writing, feature building
Tools : Read, Write, Edit, Bash, Grep, Glob
Execution : 2-3 agents coordinated
Color : Green
Examples : frontend-developer, backend-developer, api-builder
Quality Agents (Heavy Bash, Sequential Only)
Purpose : Testing, validation, review
Tools : Read, Write, Edit, Bash, Grep, Glob
Execution : 1 agent at a time (NEVER parallel)
Color : Red
Examples : test-runner, code-reviewer, security-auditor
Coordination Agents (Lightweight, Orchestration)
Purpose : Manages other agents, validates integration
Tools : Read, Write, Grep
Execution : Orchestrates others
Color : Purple
Examples : fullstack-coordinator, workflow-manager
Enhanced YAML Frontmatter Every generated agent includes rich metadata:
---
name: agent-name-kebab-case
description: When to invoke this agent
tools: Read, Write, Edit
model: sonnet
color: green
field: frontend
expertise: expert
mcp_tools: mcp__playwright
---
Field Categories Development : frontend, backend, fullstack, mobile, devops
Quality : testing, security, performance
Strategic : product, architecture, research, design
Domain : data, ai, content, finance, infrastructure
Color Coding
Blue : Strategic/planning agents
Green : Implementation/development agents
Red : Quality/testing agents
Purple : Coordination/orchestration agents
Orange : Domain-specific specialists
Expertise Levels
Beginner : Simple, focused tasks
Intermediate : Moderate complexity workflows
Expert : Advanced, complex operations
How to Use
Quick Start
Open the prompt template : documentation/templates/AGENTS_FACTORY_PROMPT.md
Scroll to bottom - Find template variables
Fill in your details :
AGENT_NAME: my-custom-agent
DESCRIPTION: What this agent does and when to invoke it
DOMAIN_FIELD: frontend
TOOLS_NEEDED: Read, Write, Edit, Bash
Copy entire prompt - Include filled variables
Paste into Claude - Claude.ai, Claude Code, or API
Receive agent file - Complete .md file ready to use
Install agent - Copy to .claude/agents/ or ~/.claude/agents/
Example Invocation @agent-factory
Create a custom agent:
Name: api-integration-specialist
Type: Implementation
Domain: backend
Description: API integration expert for third-party services
Capabilities: OAuth, REST clients, error handling
Tools: Read, Write, Edit, Bash
MCP: mcp__github
Output : Complete .claude/agents/api-integration-specialist.md file
Generated Agent Structure Each generated agent is a single Markdown file:
---
name: custom-agent
description: Triggers auto-invocation
tools: Read, Write, Edit
model: sonnet
color: green
field: backend
expertise: expert
mcp_tools: mcp__github
---
You are a [role] specializing in [domain].
When invoked:
1. [Step 1]
2. [Step 2]
3. [Step 3]
[Detailed instructions]
[Checklists]
[Best practices]
[Output format]
Integration Workflows
Workflow 1: Feature Development 1. product-planner → Creates requirements
2. frontend-developer + backend-developer → Build (parallel)
3. test-runner → Validates (sequential)
4. code-reviewer → Reviews (sequential)
Workflow 2: Bug Fix 1. debugger → Analyzes issue
2. [appropriate-dev-agent] → Fixes
3. test-runner → Validates fix
Workflow 3: Code Review 1. code-reviewer → Quality review (can run solo)
2. security-auditor → Security scan (can run solo)
MCP Tool Integration Common MCP servers to integrate:
mcp__github : PR reviews, issues, repo operations
mcp__playwright : E2E testing, screenshots, browser automation
mcp__context7 : Documentation search, knowledge queries
mcp__filesystem : Advanced file operations
Custom MCP servers : Any user-configured MCP tools
Agents automatically reference MCP tools in their capabilities when configured.
Safety & Performance
Process Monitoring Agents consume system resources. Monitor with:
ps aux | grep -E "mcp|npm|claude" | wc -l
15-20: Strategic agents (parallel)
20-30: Implementation agents (coordinated)
12-18: Quality agents (sequential)
30: Reduce parallelization
60: Critical - restart system
Execution Rules ✅ Safe : 4-5 strategic agents in parallel
✅ Safe : 2-3 implementation agents coordinated
❌ Unsafe : Quality agents in parallel (crashes system)
Best Practices
Keep agents focused - One clear responsibility per agent
Use descriptive descriptions - Enables auto-invocation
Follow tool access patterns - Match tools to agent type
Specify execution pattern - Prevents performance issues
Leverage MCP tools - Enhance agent capabilities
Test agents incrementally - Start simple, add complexity
Version control agents - Check project agents into git
Limitations
Agents are templates - customize for your specific needs
Tool suggestions are guidelines, not requirements
MCP tools require servers to be configured
Performance depends on system resources
Generated agents need testing in your environment
Installation Place in one of these locations:
Project agents (shared with team):
.claude/agents/custom-agent.md
Personal agents (available everywhere):
~/.claude/agents/custom-agent.md
When to Use This Skill Create custom agents for:
Domain-specific workflows (data science, ML, finance)
Team-specific conventions (your code style, testing approach)
Specialized tools or frameworks (Shopify, AWS, Kubernetes)
Custom MCP server integrations
Rapid prototyping of agent ideas
Use the AGENTS_FACTORY_PROMPT.md template when:
You need multiple related agents
You want consistent agent patterns
You're building an agentic framework
You want to test agent concepts quickly