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awesome-codex-subagents

A curated collection of 136+ specialized Codex subagents for development tasks across 10 categories

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awesome-codex-subagents
description
A curated collection of 136+ specialized Codex subagents for development tasks across 10 categories
triggers
["install codex subagents for my project","set up specialized AI agents for development","add backend developer subagent to codex","how do I use codex subagents","configure project-specific agents","what subagents are available for security testing","install the python-pro subagent","add react specialist agent to my workspace"]
# awesome-codex-subagents > Skill by [ara.so](https://ara.so) — Codex Skills collection. A definitive collection of 136+ specialized Codex subagents covering development, infrastructure, quality assurance, and domain-specific tasks. Each subagent is a `.toml` configuration file that extends Codex with focused expertise for specific development scenarios. ## What This Project Does This repository provides pre-configured Codex subagents that you can install to delegate specialized tasks to AI assistants with domain expertise. Instead of asking a general-purpose agent to handle everything, you can explicitly invoke subagents optimized for: - **Core Development**: API design, frontend/backend development, fullstack work - **Language Specialists**: Python, TypeScript, Go, Rust, Java, and 20+ more languages - **Infrastructure**: DevOps, Kubernetes, Terraform, cloud architecture - **Quality & Security**: Testing, security audits, accessibility, code review - **Data & Analytics**: Data engineering, ML pipelines, analytics - **Content & Documentation**: Technical writing, API docs, localization - **Domain-Specific**: Fintech, healthcare, gaming, embedded systems - **Emerging Tech**: Blockchain, AI/ML infrastructure, quantum computing - **Research & Tools**: Web search, academic research, benchmarking ## Installation ### Global Installation (Available in All Projects) ```bash # Clone the repository git clone https://github.com/VoltAgent/awesome-codex-subagents.git cd awesome-codex-subagents # Create global agents directory mkdir -p ~/.codex/agents # Install specific subagents (examples) cp categories/01-core-development/backend-developer.toml ~/.codex/agents/ cp categories/02-language-specialists/python-pro.toml ~/.codex/agents/ cp categories/03-infrastructure/kubernetes-specialist.toml ~/.codex/agents/ ``` ### Project-Specific Installation (Higher Precedence) ```bash # In your project root mkdir -p .codex/agents # Install project-specific agents cp path/to/awesome-codex-subagents/categories/04-quality-security/reviewer.toml .codex/agents/ cp path/to/awesome-codex-subagents/categories/02-language-specialists/typescript-pro.toml .codex/agents/ ``` ### Install All Subagents in a Category ```bash # Install all language specialists globally mkdir -p ~/.codex/agents cp categories/02-language-specialists/*.toml ~/.codex/agents/ # Install all infrastructure agents for current project mkdir -p .codex/agents cp categories/03-infrastructure/*.toml .codex/agents/ ``` ## Subagent Structure Each subagent is a `.toml` file with this structure: ```toml name = "python-pro" description = "Python ecosystem master for development, testing, and packaging" model = "gpt-5.3-codex-spark" model_reasoning_effort = "medium" sandbox_mode = "workspace-write" [instructions] text = """ You are a Python development expert specializing in modern Python 3.10+... Core Responsibilities: - Write idiomatic, type-hinted Python code - Use virtual environments and modern packaging tools - Implement comprehensive testing with pytest ... """ ``` ### Key Configuration Fields - **name**: Unique identifier for the subagent - **description**: When to invoke this subagent (used by Codex for routing) - **model**: Which GPT model to use (`gpt-5.4` for deep reasoning, `gpt-5.3-codex-spark` for fast tasks) - **sandbox_mode**: Filesystem access (`read-only`, `workspace-write`, or `full`) - **instructions.text**: The system prompt defining the subagent's expertise and behavior ## Using Subagents ### Explicit Delegation in Prompts Codex does **not** auto-spawn custom subagents. You must explicitly delegate: ```bash # Invoke the backend developer subagent "@backend-developer create a REST API for user management with FastAPI" # Use the security auditor to review code "@security-auditor review the authentication module for vulnerabilities" # Get the Python specialist to refactor code "@python-pro refactor this script to use type hints and dataclasses" ``` ### Multiple Subagents in Sequence ```bash # Design API first, then implement "@api-designer design a RESTful API for a blog system" # After reviewing the design: "@backend-developer implement the blog API using the design from api-designer" ``` ### Project-Specific Overrides If both global and project-specific agents exist with the same name, the project-specific one takes precedence: ```bash # Global agent at ~/.codex/agents/reviewer.toml # Project agent at .codex/agents/reviewer.toml # The project version will be used when you invoke @reviewer ``` ## Common Patterns ### Backend Development Workflow ```bash # 1. Design the API mkdir -p .codex/agents cp categories/01-core-development/api-designer.toml .codex/agents/ # 2. Install language-specific agent cp categories/02-language-specialists/python-pro.toml .codex/agents/ # 3. Add security review cp categories/04-quality-security/security-auditor.toml .codex/agents/ # Usage: "@api-designer design a REST API for inventory management" "@python-pro implement the inventory API with FastAPI and SQLAlchemy" "@security-auditor review the authentication and authorization logic" ``` ### Infrastructure Setup ```bash # Install infrastructure agents mkdir -p ~/.codex/agents cp categories/03-infrastructure/terraform-engineer.toml ~/.codex/agents/ cp categories/03-infrastructure/kubernetes-specialist.toml ~/.codex/agents/ cp categories/03-infrastructure/cloud-architect.toml ~/.codex/agents/ # Usage: "@cloud-architect design AWS infrastructure for a multi-region web app" "@terraform-engineer write Terraform modules for the AWS design" "@kubernetes-specialist create Kubernetes manifests for the application" ``` ### Full-Stack Feature Development ```bash # Install full-stack agents cp categories/02-language-specialists/react-specialist.toml .codex/agents/ cp categories/02-language-specialists/nodejs-expert.toml .codex/agents/ cp categories/04-quality-security/e2e-tester.toml .codex/agents/ # Usage: "@react-specialist build a product listing page with filtering and pagination" "@nodejs-expert create Express API endpoints for product data" "@e2e-tester write Playwright tests for the product listing flow" ``` ### Code Quality Pipeline ```bash # Install quality agents cp categories/04-quality-security/reviewer.toml .codex/agents/ cp categories/04-quality-security/test-engineer.toml .codex/agents/ cp categories/04-quality-security/accessibility-tester.toml .codex/agents/ # Usage: "@reviewer analyze the new payment module for design issues" "@test-engineer add unit and integration tests for the payment module" "@accessibility-tester audit the checkout page for WCAG 2.1 AA compliance" ``` ## Configuration ### Custom Subagent Configuration Create `.codex/config.toml` in your project: ```toml [agents] # Override default models for specific agents python-pro.model = "gpt-5.4" # Use more powerful model reviewer.sandbox_mode = "read-only" # Restrict to read-only # Set default reasoning effort *.model_reasoning_effort = "high" ``` ### Creating Custom Subagents Create a new `.toml` file in `.codex/agents/`: ```toml name = "my-custom-agent" description = "Specialized agent for my team's specific needs" model = "gpt-5.3-codex-spark" sandbox_mode = "workspace-write" [instructions] text = """ You are a custom development agent for [YOUR TEAM/PROJECT]. Your primary responsibilities: 1. Follow our team's coding standards (link to internal docs) 2. Use our specific tech stack: [list technologies] 3. Implement features according to our architecture patterns Code Style: - Use [specific linter/formatter] - Follow [naming conventions] - Include [specific testing patterns] Always: - Check our internal documentation at [URL] - Reference our API patterns in [repo location] - Use our shared component library [package name] """ ``` ## Category Overview ### Core Development (12 agents) - `api-designer` - REST and GraphQL API design - `backend-developer` - Server-side development - `frontend-developer` - UI/UX implementation - `fullstack-developer` - End-to-end features - `mobile-developer` - Cross-platform mobile apps ### Language Specialists (28 agents) - `python-pro` - Python ecosystem expert - `typescript-pro` - TypeScript development - `rust-engineer` - Systems programming - `golang-pro` - Go concurrency and services - `react-specialist` - React 18+ patterns - `nextjs-developer` - Next.js 14+ full-stack - `vue-expert` - Vue 3 Composition API - `angular-architect` - Angular 15+ enterprise ### Infrastructure (16 agents) - `devops-engineer` - CI/CD pipelines - `kubernetes-specialist` - K8s orchestration - `terraform-engineer` - Infrastructure as Code - `cloud-architect` - AWS/GCP/Azure design - `sre-engineer` - Site reliability - `docker-expert` - Container optimization ### Quality & Security (16 agents) - `security-auditor` - Vulnerability assessment - `test-engineer` - Testing strategy - `reviewer` - Code review specialist - `accessibility-tester` - WCAG compliance - `performance-optimizer` - Performance tuning ## Troubleshooting ### Subagent Not Found ```bash # Check if subagent is installed ls -la ~/.codex/agents/ ls -la .codex/agents/ # Verify the name matches the file cat .codex/agents/python-pro.toml | grep "^name" # Restart Codex session # (Implementation-specific, usually closing and reopening) ``` ### Wrong Subagent Responding ```bash # Check for name conflicts find ~/.codex/agents .codex/agents -name "*.toml" -exec grep "^name" {} \; -print # Project-specific agents override global ones # Remove the duplicate or rename one: mv .codex/agents/python-pro.toml .codex/agents/python-pro-custom.toml ``` ### Subagent Not Following Instructions ```bash # Review the instruction text cat .codex/agents/your-agent.toml # Ensure description clearly states when to invoke # Update the instructions section to be more specific: nano .codex/agents/your-agent.toml # Be explicit in your delegation: "@your-agent [very specific task description]" ``` ### Performance Issues ```bash # Check model configuration cat .codex/agents/slow-agent.toml | grep "^model" # Switch to faster model for simple tasks: # Change model = "gpt-5.4" to model = "gpt-5.3-codex-spark" # Reduce reasoning effort in config: [agents] slow-agent.model_reasoning_effort = "low" ``` ### Sandbox Restrictions ```bash # If agent can't modify files: # Check sandbox_mode in the .toml file cat .codex/agents/your-agent.toml | grep "sandbox_mode" # Update to allow writes: sandbox_mode = "workspace-write" # Or for full system access (use cautiously): sandbox_mode = "full" ``` ## Real-World Examples ### Example 1: Building a REST API with Python ```bash # Install agents cp categories/02-language-specialists/python-pro.toml .codex/agents/ cp categories/01-core-development/api-designer.toml .codex/agents/ # Step 1: Design "@api-designer design a REST API for a task management system with users, projects, and tasks" # Step 2: Implement "@python-pro implement the task management API using FastAPI with: - JWT authentication - SQLAlchemy models - Pydantic schemas - CRUD endpoints for users, projects, and tasks - PostgreSQL database" # Step 3: Test "@python-pro add pytest tests for all endpoints with fixtures and mocks" ``` ### Example 2: Infrastructure with Terraform ```bash # Install agents cp categories/03-infrastructure/terraform-engineer.toml .codex/agents/ cp categories/03-infrastructure/cloud-architect.toml .codex/agents/ # Design infrastructure "@cloud-architect design AWS infrastructure for a containerized web application with: - ECS Fargate for compute - RDS PostgreSQL for database - ElastiCache Redis for sessions - ALB for load balancing - S3 for static assets - CloudFront for CDN" # Implement with Terraform "@terraform-engineer create Terraform modules for the AWS infrastructure design: - Use remote state in S3 - Separate modules for VPC, ECS, RDS, Redis, ALB - Variables for environment-specific config - Outputs for endpoints and connection strings" ``` ### Example 3: Frontend Development with React ```bash # Install agents cp categories/02-language-specialists/react-specialist.toml .codex/agents/ cp categories/02-language-specialists/typescript-pro.toml .codex/agents/ # Build UI component "@react-specialist create a DataTable component with: - TypeScript types - Column sorting - Pagination - Row selection - Filtering - Virtualized scrolling for large datasets - Tailwind CSS styling" # Add tests "@react-specialist write React Testing Library tests for DataTable covering: - Rendering with mock data - Sorting functionality - Pagination controls - Filter interactions" ``` ### Example 4: Security Review Pipeline ```bash # Install security agents cp categories/04-quality-security/security-auditor.toml .codex/agents/ cp categories/04-quality-security/dependency-auditor.toml .codex/agents/ # Audit authentication code "@security-auditor review the authentication system in src/auth/ for: - SQL injection vulnerabilities - XSS risks - CSRF protection - Session management - Password hashing - Rate limiting" # Check dependencies "@dependency-auditor scan package.json and identify: - Known CVEs in dependencies - Outdated packages with security patches - License compliance issues - Recommended updates" ``` ## Environment Variables Subagents should reference environment variables for sensitive data: ```python # ✅ Correct - use environment variables import os DATABASE_URL = os.getenv("DATABASE_URL") API_KEY = os.getenv("API_KEY") # ❌ Incorrect - never hardcode secrets DATABASE_URL = "postgresql://user:pass@localhost/db" API_KEY = "sk-1234567890abcdef" ``` When instructing subagents: ```bash "@python-pro create a database connection using the DATABASE_URL environment variable from .env" "@nodejs-expert set up AWS SDK to use credentials from AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables" ``` ## Additional Resources - [Official Codex Documentation](https://developers.openai.com/codex/subagents) - [GitHub Repository](https://github.com/VoltAgent/awesome-codex-subagents) - [Discord Community](https://s.voltagent.dev/discord) - [Related Collections](https://github.com/VoltAgent) - Agent skills, Claude subagents, OpenClaw skills ## Contributing To add custom subagents to your local collection: 1. Create a new `.toml` file following the structure above
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