Claude Code Subagents Collection

A comprehensive collection of 33 specialized AI subagents for Claude Code, designed to enhance development workflows with domain-specific expertise and intelligent automation.

🚀 Overview

This repository contains a curated set of specialized subagents that extend Claude Code's capabilities across the entire software development lifecycle. Each subagent is an expert in a specific domain, automatically invoked based on context analysis or explicitly called when specialized expertise is needed.

Key Features

• 🤖 Intelligent Auto-Delegation: Claude Code automatically selects optimal agents based on task context
• 🔧 Domain Expertise: Each agent specializes in specific technologies, patterns, and best practices
• 🔄 Multi-Agent Orchestration: Seamless coordination between agents for complex workflows
• 📊 Quality Assurance: Built-in review and validation patterns across all domains
• ⚡ Performance Optimized: Agents designed for efficient task completion and resource utilization

Available Subagents

Agents are now organized into logical categories for easier navigation:

🏗️ Development

Frontend & UI Specialists

• frontend-developer - Build React components, implement responsive layouts, and handle client-side state management
• ui-designer - Creative UI design focused on user-friendly interfaces
• ux-designer - User experience design and interaction optimization
• react-pro - Expert React development with hooks, performance optimization, and best practices
• nextjs-pro - Next.js specialist for SSR, SSG, and full-stack React applications

Backend & Architecture

• backend-architect - Design RESTful APIs, microservice boundaries, and database schemas
• full-stack-developer - End-to-end web application development from UI to database with seamless integration

Language Specialists

• python-pro - Write idiomatic Python code with advanced features and optimizations
• golang-pro - Write idiomatic Go code with goroutines, channels, and interfaces
• typescript-pro - Advanced TypeScript development with type safety and modern patterns

Platform & Mobile

• mobile-developer - Develop React Native or Flutter apps with native integrations
• electron-pro - Electron desktop application development and cross-platform solutions

Developer Experience

• dx-optimizer - Developer Experience specialist that improves tooling, setup, and workflows
• legacy-modernizer - Refactor legacy codebases and implement gradual modernization

☁️ Infrastructure

• cloud-architect - Design AWS/Azure/GCP infrastructure and optimize cloud costs
• deployment-engineer - Configure CI/CD pipelines, Docker containers, and cloud deployments
• devops-incident-responder - Debug production issues, analyze logs, and fix deployment failures
• incident-responder - Handles production incidents with urgency and precision
• performance-engineer - Profile applications, optimize bottlenecks, and implement caching strategies

🔍 Quality & Testing

• code-reviewer - Expert code review for quality, security, and maintainability
• architect-reviewer - Reviews code changes for architectural consistency and design patterns
• qa-expert - Comprehensive QA processes and testing strategies for quality assurance
• test-automator - Create comprehensive test suites with unit, integration, and e2e tests
• debugger - Debugging specialist for errors, test failures, and unexpected behavior

📊 Data & AI

Data Engineering & Analytics

• data-engineer - Build ETL pipelines, data warehouses, and streaming architectures
• data-scientist - Data analysis expert for SQL queries, BigQuery operations, and data insights
• database-optimizer - Optimize SQL queries, design efficient indexes, and handle database migrations
• postgres-pro - PostgreSQL database expert for advanced queries and optimizations
• graphql-architect - Design GraphQL schemas, resolvers, and federation patterns

AI & Machine Learning

• ai-engineer - Build LLM applications, RAG systems, and prompt pipelines
• ml-engineer - Implement ML pipelines, model serving, and feature engineering
• prompt-engineer - Optimizes prompts for LLMs and AI systems

🛡️ Security

• security-auditor - Review code for vulnerabilities and ensure OWASP compliance

🎯 Specialization

• api-documenter - Create OpenAPI/Swagger specs and write developer documentation
• documentation-expert - Professional technical writing and comprehensive documentation systems

💼 Business

• product-manager - Strategic product management with roadmap planning and stakeholder alignment

🎭 Meta-Orchestration

• agent-organizer - Master orchestrator for complex, multi-agent tasks. Analyzes project requirements, assembles optimal agent teams, and manages collaborative workflows for comprehensive project execution.

Key Capabilities:

• Intelligent Project Analysis: Technology stack detection, architecture pattern recognition, and requirement extraction
• Strategic Team Assembly: Selects optimal 1-3 agent teams based on project needs and complexity
• Workflow Orchestration: Manages multi-phase collaboration with quality gates and validation checkpoints
• Efficiency Optimization: Focused teams for common tasks (bug fixes, features, documentation) with comprehensive orchestration for complex projects

When to Use: Complex multi-step projects, cross-domain tasks, architecture decisions, comprehensive analysis, or any scenario requiring coordinated expertise from multiple specialized agents.

📦 Installation

Quick Setup

Manual Installation (Recommend)

Alternatively, you can manually copy individual agent files:

# Prevent replacing documents from other providers
mkdir ~/.claude/agents/lst97
# Copy specific agents to your Claude agents directory
cp /path/to/agents/*.md ~/.claude/agents/lst97

Verification

To verify agents are loaded correctly:

# List all available agents
ls ~/.claude/agents/lst97/*.md

# Check Claude Code recognizes the agents (run in Claude Code)
# "List all available subagents"

Quick Installation

These subagents are automatically available when placed in the ~/.claude/agents/ directory. Claude Code will automatically detect and load them on startup. This will enable the CLAUDE.md to be available in global scope, may also conflict with other repository.

# Clone the repository to your Claude agents directory
# Documents are base on the scaffold from https://github.com/wshobson/agents.git
cd ~/.claude
git clone https://github.com/lst97/claude-code-sub-agents.git

# Or if the directory already exists, pull the latest updates
cd ~/.claude
git pull origin main

🔧 MCP Server Configuration (Required for Full Performance)

To enable optimal performance with specialized MCP (Model Context Protocol) servers that enhance agent capabilities, add the following configuration to your global Claude settings file (~/.claude.json):

"mcpServers": {
  "sequential-thinking": {
    "type": "stdio",
    "command": "npx",
    "args": [
      "-y",
      "@modelcontextprotocol/server-sequential-thinking"
    ],
    "env": {}
  },
  "context7": {
    "type": "stdio",
    "command": "npx",
    "args": [
      "-y",
      "@upstash/context7-mcp"
    ],
    "env": {}
  },
  "magic": {
    "type": "stdio",
    "command": "npx",
    "args": [
      "-y",
      "@21st-dev/magic@latest",
      "API_KEY=\"api-key\"" // API key is required
    ],
    "env": {}
  },
  "playwright": {
    "type": "stdio",
    "command": "npx",
    "args": [
      "@playwright/mcp@latest"
    ],
    "env": {}
  },
  "filesystem": {
    "command": "npx",
    "args": [
      "-y",
      "@modelcontextprotocol/server-filesystem",
      "/your/allowed/path" // please add your path here
    ]
  },
  "puppeteer": {
    "command": "npx",
    "args": [
      "-y",
      "puppeteer-mcp-server"
    ],
    "env": {}
  }
}

MCP Server Benefits:

• sequential-thinking: Enhanced multi-step reasoning and complex analysis
• context7: Access to up-to-date documentation and framework patterns
• magic: Advanced UI component generation and design system integration
• playwright: Cross-browser testing and E2E automation capabilities

Note: These MCP servers significantly enhance agent capabilities but are not strictly required for basic functionality.

🎭 Advanced: Agent-Organizer Auto-Dispatch Setup

For complex projects requiring multi-agent coordination, you can enable the dispatch protocol in your project root directory (not globally):

# Copy CLAUDE.md to your PROJECT root directory (recommended)
cp /path/to/agents/CLAUDE.md /path/to/your/project/CLAUDE.md

⚠️ Project-Scope Recommendation:

• ✅ Project-Specific: Place CLAUDE.md in individual project roots for targeted orchestration
• ❌ Global Scope: Avoid placing in ~/.claude/CLAUDE.md to prevent over-orchestration of simple tasks
• 🎯 Selective Usage: Enable only for projects requiring comprehensive multi-agent workflows

Trade-offs to Consider:

• Quality vs Speed: Multi-agent workflows provide expert results but take longer
• Token Efficiency: 2-5x token usage for comprehensive analysis and implementation
• Complexity Matching: Best for complex projects, may over-engineer simple tasks

🔧 Usage

Automatic Invocation (Recommended)

Claude Code intelligently analyzes your request and automatically delegates to the most appropriate subagent(s) based on:

• Context Analysis: Keywords, file types, and project structure
• Task Classification: Development, debugging, optimization, etc.
• Domain Expertise: Matching requirements to specialist knowledge
• Workflow Patterns: Common multi-agent coordination scenarios

Example: "Implement user authentication with secure password handling" → Automatically uses: backend-architect → security-auditor → test-automator

Explicit Invocation

For specific expertise or when you want control over agent selection:

# Direct agent requests
"Use the code-reviewer to check my recent changes"
"Have the security-auditor scan for vulnerabilities"
"Get the performance-engineer to optimize this bottleneck"

# Multi-agent requests
"Have backend-architect design the API, then security-auditor review it"
"Use data-scientist to analyze this dataset, then ai-engineer to build recommendations"

Hybrid Approach

Combine automatic and explicit invocation:

# Start explicit, let Claude coordinate the rest
"Use backend-architect to design a REST API for user management, then handle the implementation automatically"

# Explicit validation after automatic work
"Implement this feature automatically, then have security-auditor review the result"

💡 Usage Examples

Direct Agent Invocation

When not using agent-organizer, specify the exact agent needed for your task:

# Development Tasks
"Use backend-architect to design a REST API for user management"
"Have frontend-developer create a responsive login form component"
"Get python-pro to implement async data processing with proper error handling"
"Have react-pro optimize this component for performance and add proper TypeScript types"
"Use typescript-pro to refactor this module with advanced type safety"

# Code Quality & Review
"Use code-reviewer to analyze this pull request for best practices"
"Have architect-reviewer check if this change maintains architectural consistency"
"Get debugger to investigate why this test is failing intermittently"

# Security & Performance
"Have security-auditor scan this authentication module for vulnerabilities"
"Use performance-engineer to identify bottlenecks in this API endpoint"
"Get database-optimizer to improve these slow queries"

# Testing & QA
"Use test-automator to create comprehensive tests for this user service"
"Have qa-expert design a testing strategy for this new feature"

# Infrastructure & Deployment
"Get devops-incident-responder to investigate this production deployment failure"
"Use cloud-architect to design scalable infrastructure for this microservice"
"Have deployment-engineer set up CI/CD pipeline for this repository"

# Data & AI
"Use data-scientist to analyze user behavior patterns in this dataset"
"Have ai-engineer implement a RAG system for document search"
"Get ml-engineer to deploy this trained model to production"

# Documentation & Specialization
"Use documentation-expert to create comprehensive API documentation"
"Have api-documenter generate OpenAPI specs for these endpoints"

# Multi-Agent Coordination Examples
"Use backend-architect to design the API, then have security-auditor review it"
"Get frontend-developer to build the component, then use test-automator for coverage"
"Have database-optimizer improve queries, then performance-engineer validate results"

Agent Communication Protocol Examples

Each agent uses a standardized communication protocol with agent-specific context requests. Here are examples:

Frontend Development

{
  "requesting_agent": "frontend-developer",
  "request_type": "get_task_briefing",
  "payload": {
    "query": "Initial briefing required for UI component development. Provide overview of existing React project structure, design system, component library, and relevant frontend files."
  }
}

📋 Subagent Format

Each subagent follows a standardized structure for consistent behavior and optimal integration:

File Structure

---
name: subagent-name
description: When this subagent should be invoked
tools: tool1, tool2  # Optional - defaults to all tools
---

# Subagent Name

**Role**: Detailed role description and primary responsibilities

**Expertise**: Specific technologies, frameworks, and domain knowledge

**Key Capabilities**:
- Capability 1: Description
- Capability 2: Description
- Capability 3: Description

System prompt defining the subagent's specialized behavior, decision-making patterns, and interaction style with other agents.

Required Components

• Name: Kebab-case filename matching the agent name
• Description: Clear trigger conditions for automatic invocation
• Role Definition: Specific responsibilities and boundaries
• Expertise Areas: Technologies, patterns, and domain knowledge
• System Prompt: Detailed instructions for specialized behavior

Optional Components

• Tools: Specific Claude Code tools (defaults to all available tools)
• Dependencies: Other agents this one commonly works with
• Patterns: Common workflow patterns and coordination scenarios

🔄 Agent Orchestration Patterns

Claude Code automatically coordinates agents using these patterns:

• Sequential: architect → implement → test → review for dependent tasks
• Parallel: performance-engineer + database-optimizer for independent analysis
• Validation: primary-agent → security-auditor for critical components
• Iterative: review → refine → validate for optimization tasks

🎯 When to Use Which Agent

🏗️ Planning & Architecture

Agent	Best For	Example Use Cases
backend-architect	API design, system architecture	RESTful APIs, microservices, database schemas
frontend-developer	UI/UX planning, component design	React components, responsive layouts, state management
cloud-architect	Infrastructure design, scalability	AWS/Azure/GCP architecture, cost optimization
graphql-architect	GraphQL system design	Schema design, resolvers, federation

💻 Implementation & Development

Agent	Best For	Example Use Cases
python-pro	Python development	Django/FastAPI apps, data processing, async programming
golang-pro	Go development	Microservices, concurrent systems, CLI tools
typescript-pro	TypeScript development	Type-safe applications, advanced TS features
react-pro	React expertise	Hooks, performance optimization, advanced patterns
nextjs-pro	Next.js applications	SSR/SSG, full-stack React, routing

☁️ Operations & Maintenance

Agent	Best For	Example Use Cases
devops-incident-responder	Production issues, deployments	Log analysis, deployment failures, system debugging
incident-responder	Critical outages	Immediate response, crisis management, escalation
deployment-engineer	CI/CD, containerization	Docker, Kubernetes, pipeline configuration
database-optimizer	Database performance	Query optimization, indexing, migration strategies

📊 Analysis & Optimization

Agent	Best For	Example Use Cases
performance-engineer	Application performance	Bottleneck analysis, caching strategies, optimization
security-auditor	Security assessment	Vulnerability scanning, OWASP compliance, threat modeling
data-scientist	Data analysis	SQL queries, BigQuery, insights and reporting
code-reviewer	Code quality	Best practices, maintainability, architectural review

🧪 Quality Assurance

Agent	Best For	Example Use Cases
test-automator	Testing strategy	Unit tests, integration tests, E2E test suites
debugger	Bug investigation	Error analysis, test failures, troubleshooting
architect-reviewer	Design validation	Architectural consistency, pattern compliance

📚 Best Practices

• Trust Auto-Delegation: Claude Code excels at context analysis and optimal agent selection
• Provide Rich Context: Include tech stack, constraints, and project background
• Use Explicit Control: Override automatic selection when you need specific expertise
• Establish Quality Gates: Build review and validation into standard workflows
• Match Task Complexity: Don't over-engineer simple tasks or under-resource complex ones

🤝 Contributing

Adding New Agents

To contribute a new subagent to the collection:

1. Follow Naming Convention

   • Use lowercase, hyphen-separated names (e.g., backend-architect.md)
   • Name should clearly indicate the agent's domain and role

2. Use Standard Format

   • Include proper frontmatter with name, description, and optional tools
   • Follow the structured format outlined in the Subagent Format section

3. Write Clear Descriptions

   • Description should clearly indicate when the agent should be automatically invoked
   • Include specific keywords and contexts that trigger the agent

4. Define Specialized Behavior

   • Include detailed system prompt with role, expertise, and capabilities
   • Define interaction patterns with other agents
   • Specify decision-making frameworks and priorities

5. Test Integration

   • Verify the agent can be automatically invoked based on description
   • Test explicit invocation with clear requests
   • Ensure compatibility with existing agent coordination patterns

Quality Standards

• Domain Expertise: Agents should demonstrate deep knowledge in their specialization
• Clear Boundaries: Define what the agent does and doesn't handle
• Integration Ready: Design for seamless coordination with other agents
• Consistent Voice: Maintain professional, helpful, and expert tone

Submission Process

1. Create the agent file following all standards
2. Test the agent with various invocation patterns
3. Submit a pull request with example use cases
4. Include documentation of the agent's unique value and integration patterns

🛠️ Troubleshooting

Common Issues:

• Agent not selected: Use domain-specific keywords or explicit invocation
• Unexpected selection: Provide more context about tech stack and requirements
• Generic responses: Request specific depth and include detailed constraints
• Conflicting advice: Request reconciliation between different specialists

Resources:

• Claude Code Documentation - Official guide
• Subagents Documentation - Agent system reference

📊 Quick Reference

Most Commonly Used Agents

1. code-reviewer - Quality assurance and best practices
2. backend-architect - API and system design
3. frontend-developer - UI/UX implementation
4. security-auditor - Security validation and compliance
5. performance-engineer - Optimization and bottleneck analysis

Essential Coordination Patterns

• Development: architect → implement → test → review
• Debugging: debugger → specialist → validator
• Optimization: performance-engineer + database-optimizer → validation
• Security: primary-agent → security-auditor → approval

Key Success Factors

• ✅ Trust automatic delegation for optimal results
• ✅ Provide rich context and specific requirements
• ✅ Use explicit invocation strategically
• ✅ Establish quality gates and validation patterns
• ✅ Learn from agent coordination patterns

🎬 Examples

These examples demonstrate real-world multi-agent coordination scenarios with detailed resource metrics to help you understand the token usage, execution time, and expected deliverables for different project complexities:

• Example 1: Simple feature implementation (~300K tokens, ~17 minutes) - Shows efficient 4-agent coordination for focused component development
• Example 2: Complex system implementation (~850K tokens, ~45 minutes) - Demonstrates enterprise-scale 7-agent orchestration with error recovery

Both examples include actual token counts, execution times, and deliverable quality to help you plan and budget for multi-agent workflows in your projects.

Example 1: ExportStep Component Implementation

User Request: /sc:implement use agent-organizer to design and implement the ExportStep.tsx function, improve the UI/UX as well

Agent Orchestration Flow

Step 1: agent-organizer Analysis (56.7K tokens, 1m 20s)

• Analyzed existing project structure and Zustand stores
• Created comprehensive 7-task implementation plan
• Assembled 3-agent specialist team for coordinated execution

Step 2: backend-architect Implementation (99.1K tokens, 7m 31s)

• Designed complete export store architecture with state management
• Implemented format conversion utilities for SRT, VTT, ASS, JSON formats
• Integrated Electron IPC for seamless file operations

Step 3: frontend-developer Enhancement (84.3K tokens, 5m 29s)

• Created fully interactive ExportStep component with real event handlers
• Implemented real-time preview generation based on subtitle data
• Added accessibility compliance (WCAG 2.1 AA) and responsive design

Step 4: test-automator Quality Assurance (61.4K tokens, 2m 46s)

• Developed comprehensive test coverage for format converters
• Set up Jest and React Testing Library framework
• Created accessibility and interaction testing strategies

Implementation Results

Complete Feature Delivery:

• 🏗️ Backend: Export store with state management, format conversion utilities, Electron IPC integration
• 🎨 Frontend: Interactive UI with real-time preview, accessibility compliance, keyboard navigation
• ✅ Testing: Comprehensive test coverage with framework setup and validation

Project Metrics

Resource Usage:

• Total Tokens: ~301K tokens (agent-organizer: 56K, backend-architect: 99K, frontend-developer: 84K, test-automator: 61K)
• Total Time: ~30 minutes execution time
• Team Size: 4 agents (1 orchestrator + 3 specialists)
• Files Created/Modified: 4 major files (stores, components, utilities, tests)

Efficiency Highlights:

• Sequential Coordination: Each agent built upon previous work seamlessly
• Quality Integration: Production-ready export system with comprehensive functionality
• Zero Breaking Changes: Enhanced existing architecture without disruption

Example 2: Complex Workspace Management System

User Request: /sc:design implement complex workspace management with user config persistence, multiple workspaces, workspace groups, Discord-like UI with drag-and-drop functionality

Phase 1: Comprehensive Design & Multi-Agent Assessment

5-Agent Team Assembly: backend-architect, frontend-developer, electron-pro, ux-designer, test-automator

Design Deliverables:

• Complete TypeScript interfaces for Workspace, WorkspaceGroup, and configurations
• IndexedDB storage strategy with migration from localStorage
• Discord-inspired UI specifications with drag-and-drop functionality
• Auto-save mechanisms with conflict resolution and backup strategy
• 5-phase implementation plan with quality gates

Phase 1 Assessment Results:

Comprehensive Team Assessment (5 agents, ~400K tokens total):

• 🏗️ Backend Architecture: IndexedDB schema, <200ms startup, migration framework, auto-save strategy
• 🎨 Frontend Components: Discord-inspired design, Material-UI integration, progressive enhancement
• ⚡ Electron Integration: IPC architecture, security model, performance optimization
• 🎭 UX Design: A+ UX Score (92/100), zero disruption, user journey validation
• ✅ Testing Strategy: 99.5% migration success, 4-layer testing pyramid, quality gates

Complete Implementation Results

Full 5-Phase Implementation:

• Phase 1: Assessment & Current State Analysis ✅
• Phase 2: Architecture Finalization & Infrastructure ✅
• Phase 3: Core Implementation ✅
• Phase 4: Integration & Migration ✅
• Phase 5: Quality Assurance & Finalization ✅

Final Deliverables:

• Complete workspace management system with IndexedDB persistence
• Discord-inspired UI with drag-and-drop workspace organization
• Multi-workspace support with workspace groups
• Seamless migration from existing localStorage system
• Comprehensive test coverage and error recovery mechanisms

Resource Metrics & Performance

Total Project Metrics:

• Tokens Used: ~900K tokens across all phases and error resolution
• Time Spent: ~120 minutes total execution time
• Agents Involved: 7 specialized agents (5 primary + 2 error resolution)
• Lines of Code: ~2,400 lines across 15+ files
• Test Coverage: 99.5% with comprehensive edge case handling (Should be hallucination)

Build Error Resolution with Nested Agent Coordination

Second User Prompt: @agent-code-reviewer-pro the application have build error please find all the build errors and ask the related sub agent to fix it. @agent-agent-organizer

Error Resolution Flow:

1. code-reviewer-pro (68.5K tokens, 5m 26s): Identified critical TypeScript syntax errors
2. agent-organizer coordination: Systematic build error fixes with typescript-pro
3. Nested delegation: Specialized agents called within agent workflows for targeted fixes

Error Resolution Efficiency:

• Detection: ~5m with code-reviewer-pro
• Coordination: Instant agent-organizer response
• Fix Implementation: ~30m minutes with nested typescript-pro agent
• Build Success: Zero remaining errors after systematic fixes
• Challenging Runtime ERROR Runtime error occur and it require manuel debugging and instruction

Key Multi-Agent Benefits

• 🧠 Intelligent Orchestration: agent-organizer coordinated 5+ agents across complex 5-phase implementation
• 🔧 Nested Agent Support: Error resolution through coordinated sub-agent delegation within workflows
• 📊 Enterprise-Scale Quality: 850K tokens of comprehensive analysis, design, and implementation
• ⚡ Rapid Error Recovery: Build errors resolved in <8 minutes through specialized agent coordination
• 🎯 Domain Expertise: Each agent contributed specialized knowledge (storage architecture, UX design, TypeScript fixes)

---

Happy coding with your AI specialist team! 🚀