Claude Code Subagents Collection
A comprehensive collection of 82 specialized AI subagents for Claude Code , providing domain-specific expertise across software development, infrastructure, and business operations.
This repository provides production-ready subagents that extend Claude Code's capabilities with specialized knowledge. Each subagent incorporates:
Current industry best practices and standards (2024/2025)
Production-ready patterns and enterprise architectures
Deep domain expertise with 8-12 capability areas per agent
Modern technology stacks and frameworks
Optimized model selection based on task complexity
Architecture & System Design
Agent
Model
Description
c-pro
sonnet
System programming with memory management and OS interfaces
cpp-pro
sonnet
Modern C++ with RAII, smart pointers, STL algorithms
rust-pro
sonnet
Memory-safe systems programming with ownership patterns
golang-pro
sonnet
Concurrent programming with goroutines and channels
Agent
Model
Description
javascript-pro
sonnet
Modern JavaScript with ES6+, async patterns, Node.js
typescript-pro
sonnet
Advanced TypeScript with type systems and generics
python-pro
sonnet
Python development with advanced features and optimization
ruby-pro
sonnet
Ruby with metaprogramming, Rails patterns, gem development
php-pro
sonnet
Modern PHP with frameworks and performance optimization
Agent
Model
Description
java-pro
sonnet
Modern Java with streams, concurrency, JVM optimization
scala-pro
sonnet
Enterprise Scala with functional programming and distributed systems
csharp-pro
sonnet
C# development with .NET frameworks and patterns
Agent
Model
Description
elixir-pro
sonnet
Elixir with OTP patterns and Phoenix frameworks
unity-developer
sonnet
Unity game development and optimization
minecraft-bukkit-pro
sonnet
Minecraft server plugin development
sql-pro
sonnet
Complex SQL queries and database optimization
Infrastructure & Operations
Agent
Model
Description
devops-troubleshooter
sonnet
Production debugging, log analysis, deployment troubleshooting
deployment-engineer
sonnet
CI/CD pipelines, containerization, cloud deployments
terraform-specialist
opus
Infrastructure as Code with Terraform modules and state management
dx-optimizer
sonnet
Developer experience optimization and tooling improvements
Agent
Model
Description
database-optimizer
opus
Query optimization, index design, migration strategies
database-admin
sonnet
Database operations, backup, replication, monitoring
Incident Response & Network
Quality Assurance & Security
Agent
Model
Description
test-automator
sonnet
Comprehensive test suite creation (unit, integration, e2e)
tdd-orchestrator
sonnet
Test-Driven Development methodology guidance
debugger
sonnet
Error resolution and test failure analysis
error-detective
sonnet
Log analysis and error pattern recognition
Data Engineering & Analytics
Agent
Model
Description
data-scientist
opus
Data analysis, SQL queries, BigQuery operations
data-engineer
sonnet
ETL pipelines, data warehouses, streaming architectures
Agent
Model
Description
ai-engineer
opus
LLM applications, RAG systems, prompt pipelines
ml-engineer
opus
ML pipelines, model serving, feature engineering
mlops-engineer
opus
ML infrastructure, experiment tracking, model registries
prompt-engineer
opus
LLM prompt optimization and engineering
Documentation & Technical Writing
Business Analysis & Finance
Agent
Model
Description
business-analyst
sonnet
Metrics analysis, reporting, KPI tracking
quant-analyst
opus
Financial modeling, trading strategies, market analysis
risk-manager
sonnet
Portfolio risk monitoring and management
Agent
Model
Description
content-marketer
sonnet
Blog posts, social media, email campaigns
sales-automator
haiku
Cold emails, follow-ups, proposal generation
Agent
Model
Description
customer-support
sonnet
Support tickets, FAQ responses, customer communication
hr-pro
opus
HR operations, policies, employee relations
legal-advisor
opus
Privacy policies, terms of service, legal documentation
SEO & Content Optimization
Agents are assigned to specific Claude models based on task complexity and computational requirements. The system uses three model tiers:
Model Distribution Summary
Model
Agent Count
Use Case
Haiku
11
Quick, focused tasks with minimal computational overhead
Sonnet
46
Standard development and specialized engineering tasks
Opus
21
Complex reasoning, architecture, and critical analysis
Category
Agents
Context & Reference
context-manager
, reference-builder
, sales-automator
, search-specialist
SEO Optimization
seo-meta-optimizer
, seo-keyword-strategist
, seo-structure-architect
, seo-snippet-hunter
, seo-content-refresher
, seo-cannibalization-detector
, seo-content-planner
Category
Count
Agents
Programming Languages
18
All language-specific agents (JavaScript, Python, Java, C++, etc.)
Frontend & UI
5
frontend-developer
, ui-ux-designer
, ui-visual-validator
, mobile-developer
, ios-developer
Infrastructure
8
devops-troubleshooter
, deployment-engineer
, dx-optimizer
, database-admin
, network-engineer
, flutter-expert
, api-documenter
, tutorial-engineer
Quality & Testing
4
test-automator
, tdd-orchestrator
, debugger
, error-detective
Business & Support
6
business-analyst
, risk-manager
, content-marketer
, customer-support
, mermaid-expert
, legacy-modernizer
Data & Content
5
data-engineer
, payment-integration
, seo-content-auditor
, seo-authority-builder
, seo-content-writer
Category
Count
Agents
Architecture & Design
7
architect-reviewer
, backend-architect
, cloud-architect
, hybrid-cloud-architect
, kubernetes-architect
, graphql-architect
, terraform-specialist
Critical Analysis
5
code-reviewer
, security-auditor
, performance-engineer
, incident-responder
, database-optimizer
AI/ML Complex
5
ai-engineer
, ml-engineer
, mlops-engineer
, data-scientist
, prompt-engineer
Business Critical
4
docs-architect
, hr-pro
, legal-advisor
, quant-analyst
Clone the repository to the Claude agents directory:
cd ~ /.claude
git clone https://github.com/wshobson/agents.git
The subagents will be automatically available to Claude Code once placed in the ~/.claude/agents/
directory.
Claude Code automatically selects the appropriate subagent based on task context and requirements. The system analyzes your request and delegates to the most suitable specialist.
Specify a subagent by name to use a particular specialist:
"Use code-reviewer to analyze the recent changes"
"Have security-auditor scan for vulnerabilities"
"Get performance-engineer to optimize this bottleneck"
code-reviewer: Analyze component for best practices
security-auditor: Check for OWASP compliance
tdd-orchestrator: Implement feature with test-first approach
performance-engineer: Profile and optimize bottlenecks
Development & Architecture
backend-architect: Design authentication API
frontend-developer: Create responsive dashboard
graphql-architect: Design federated GraphQL schema
mobile-developer: Build cross-platform mobile app
Infrastructure & Operations
devops-troubleshooter: Analyze production logs
cloud-architect: Design scalable AWS architecture
network-engineer: Debug SSL certificate issues
database-admin: Configure backup and replication
terraform-specialist: Write infrastructure modules
data-scientist: Analyze customer behavior dataset
ai-engineer: Build RAG system for document search
mlops-engineer: Set up experiment tracking
ml-engineer: Deploy model to production
business-analyst: Create metrics dashboard
docs-architect: Generate technical documentation
api-documenter: Write OpenAPI specifications
content-marketer: Create SEO-optimized content
Subagents coordinate automatically for complex tasks. The system intelligently sequences multiple specialists based on task requirements.
Feature Development
"Implement user authentication"
→ backend-architect → frontend-developer → test-automator → security-auditor
Performance Optimization
"Optimize checkout process"
→ performance-engineer → database-optimizer → frontend-developer
Production Incidents
"Debug high memory usage"
→ incident-responder → devops-troubleshooter → error-detective → performance-engineer
Infrastructure Setup
"Set up disaster recovery"
→ database-admin → database-optimizer → terraform-specialist
ML Pipeline Development
"Build ML pipeline with monitoring"
→ mlops-engineer → ml-engineer → data-engineer → performance-engineer
Integration with Claude Code Commands
For sophisticated multi-agent orchestration, use the Claude Code Commands collection which provides 52 pre-built slash commands:
/full-stack-feature # Coordinates 8+ agents for complete feature development
/incident-response # Activates incident management workflow
/ml-pipeline # Sets up end-to-end ML infrastructure
/security-hardening # Implements security best practices across stack
Each subagent is defined as a Markdown file with frontmatter:
---
name : subagent-name
description : Activation criteria for this subagent
model : haiku|sonnet|opus # Optional: Model selection
tools : tool1, tool2 # Optional: Tool restrictions
---
System prompt defining the subagent's expertise and behavior
haiku : Simple, deterministic tasks with minimal reasoning
sonnet : Standard development and engineering tasks
opus : Complex analysis, architecture, and critical operations
Agent Orchestration Patterns
Agents execute in sequence, passing context forward:
backend-architect → frontend-developer → test-automator → security-auditor
Multiple agents work simultaneously on different aspects:
performance-engineer + database-optimizer → Merged analysis
Dynamic agent selection based on analysis:
debugger → [backend-architect | frontend-developer | devops-troubleshooter]
Primary work followed by specialized review:
payment-integration → security-auditor → Validated implementation
Task
Recommended Agent
Key Capabilities
API Design
backend-architect
RESTful APIs, microservices, database schemas
Cloud Infrastructure
cloud-architect
AWS/Azure/GCP design, scalability planning
UI/UX Design
ui-ux-designer
Interface design, wireframes, design systems
System Architecture
architect-reviewer
Pattern validation, consistency analysis
Language Category
Agents
Primary Use Cases
Systems Programming
c-pro
, cpp-pro
, rust-pro
, golang-pro
OS interfaces, embedded systems, high performance
Web Development
javascript-pro
, typescript-pro
, python-pro
, ruby-pro
, php-pro
Full-stack web applications, APIs, scripting
Enterprise
java-pro
, csharp-pro
, scala-pro
Large-scale applications, enterprise systems
Mobile
ios-developer
, flutter-expert
, mobile-developer
Native and cross-platform mobile apps
Specialized
elixir-pro
, unity-developer
, minecraft-bukkit-pro
Domain-specific development
Operations & Infrastructure
Task
Recommended Agent
Key Capabilities
Production Issues
devops-troubleshooter
Log analysis, deployment debugging
Critical Incidents
incident-responder
Outage response, immediate mitigation
Database Performance
database-optimizer
Query optimization, indexing strategies
Database Operations
database-admin
Backup, replication, disaster recovery
Infrastructure as Code
terraform-specialist
Terraform modules, state management
Network Issues
network-engineer
Network debugging, load balancing
Task
Recommended Agent
Key Capabilities
Code Review
code-reviewer
Security focus, best practices
Security Audit
security-auditor
Vulnerability scanning, OWASP compliance
Test Creation
test-automator
Unit, integration, E2E test suites
Performance Issues
performance-engineer
Profiling, optimization
Bug Investigation
debugger
Error resolution, root cause analysis
Task
Recommended Agent
Key Capabilities
Data Analysis
data-scientist
SQL queries, statistical analysis
LLM Applications
ai-engineer
RAG systems, prompt pipelines
ML Development
ml-engineer
Model training, feature engineering
ML Operations
mlops-engineer
ML infrastructure, experiment tracking
Task
Recommended Agent
Key Capabilities
Technical Docs
docs-architect
Comprehensive documentation generation
API Documentation
api-documenter
OpenAPI/Swagger specifications
Business Metrics
business-analyst
KPI tracking, reporting
Legal Compliance
legal-advisor
Privacy policies, terms of service
Automatic selection - Let Claude Code analyze context and select optimal agents
Clear requirements - Specify constraints, tech stack, and quality standards
Trust specialization - Each agent is optimized for their specific domain
High-level requests - Allow agents to coordinate complex multi-step tasks
Context preservation - Ensure agents have necessary background information
Integration review - Verify how different agents' outputs work together
Direct invocation - Specify agents when you need particular expertise
Strategic combination - Use multiple specialists for validation
Review patterns - Request specific review workflows (e.g., "security-auditor reviews API design")
Monitor effectiveness - Track which agents work best for your use cases
Iterative refinement - Use agent feedback to improve requirements
Complexity matching - Align task complexity with agent capabilities
To add a new subagent:
Create a new .md
file with appropriate frontmatter
Use lowercase, hyphen-separated naming convention
Write clear activation criteria in the description
Define comprehensive system prompt with expertise areas
Ensure request clearly indicates the domain
Be specific about task type and requirements
Use explicit invocation if automatic selection fails
Unexpected Agent Selection
Provide more context about tech stack
Include specific requirements in request
Use direct agent naming for precise control
Conflicting Recommendations
Normal behavior - specialists have different priorities
Request reconciliation between specific agents
Consider trade-offs based on project requirements
Include background information in requests
Reference previous work or patterns
Provide project-specific constraints
MIT License - see LICENSE file for details.