create-subagents

name	create-subagents
description	ALWAYS invoke this skill when creating, editing, or configuring custom agents. NEVER create custom agents without this skill.

Invoke the instructions:agent-prompt-standards skill before proceeding. If that skill is unavailable, report the missing skill and continue with the closest available workflow.

A custom agent configured for an isolated, focused role — its developer instructions, tool access, and isolated-workflow orchestration.

<quick_start>

1. Create a standalone TOML file under .codex/agents/ for product scope or ~/.codex/agents/ for user scope.
2. Define the custom agent:
   • name: unique identifier Codex uses when spawning or referring to this agent
   • description: human-facing guidance for when Codex should use this agent
   • developer_instructions: core instructions that define the custom agent's behavior
   • model: Optional model override
   • model_reasoning_effort: Optional reasoning setting
   • sandbox_mode, mcp_servers: Optional runtime configuration overrides
3. Write the developer instructions with clear role, constraints, workflow, and output expectations.

Read `${SKILL_DIR}/references/subagents.md` for complete custom agent file examples and field references.

<file_structure>

<codex_storage_locations>

Priority order:

1. Product: .codex/agents/ for the current product
2. User: ~/.codex/agents/ for all projects

</codex_storage_locations>

Product-scope custom agents override user-scope when names conflict. </file_structure>

• Unique identifier Codex uses when spawning or referring to this agent
• Matching the filename to the custom agent name is the simplest convention

- Natural language description of purpose

• Guides selection after the user explicitly asks Codex for this custom agent or subagent workflow

• Required multiline TOML string that defines the custom agent's behavior
• Use clear role, constraints, workflow, and output expectations
• Prefer XML structure inside the string for prompt clarity

- Optional model override - Use explicit models for verification, audit, review, and evidence-producing agents - Choose a faster, lower-cost model only when the owning workflow accepts that tradeoff - Optional reasoning setting - Use `high` for complex logic, security review, or edge-case analysis - Use `medium` as the default for most custom agents - Use `low` only for straightforward work where speed matters - Optional sandbox override - Use `read-only` for exploration, audit, and review agents that must not edit files

<execution_model> <critical_constraint>

Custom agent workflows are black boxes that cannot interact with users.

Custom agents run in isolated contexts and return their final output to the main conversation. They:

• ✅ Can use tools like Read, Write, Edit, Bash, Grep, Glob
• ✅ Can access MCP servers and other non-interactive tools
• ❌ Cannot use request_user_input or any tool requiring user interaction
• ❌ Cannot present options or wait for user input
• ❌ User never sees isolated-workflow intermediate steps

The main conversation sees only the isolated workflow's final report/output. </critical_constraint>

<workflow_design> Designing workflows with custom agents:

Use main chat for:

• Gathering requirements from user (request_user_input)
• Presenting options or decisions to user
• Any task requiring user confirmation/input
• Work where user needs visibility into progress

Use custom agents for:

• Research tasks (API documentation lookup, code analysis)
• Code generation based on pre-defined requirements
• Analysis and reporting (security review, test coverage)
• Context-heavy operations that don't need user interaction

Example workflow pattern:

Main Chat: Ask user for requirements (request_user_input)
↓
custom agent: Research API and create documentation (no user interaction)
↓
Main Chat: Review research with user, confirm approach
↓
custom agent: Generate code based on confirmed plan
↓
Main Chat: Present results, handle testing/deployment

</workflow_design> </execution_model>

<system_prompt_guidelines> Clearly define the custom agent's role, capabilities, and constraints.

Structure the developer instructions with pure XML tags. Remove ALL markdown headings from the body.

name = "security_reviewer"
description = "Reviews code for security vulnerabilities."
sandbox_mode = "read-only"
model = "gpt-5.4"
developer_instructions = """
<role>
Claude is a senior code reviewer specializing in security.
</role>

<focus_areas>

- SQL injection vulnerabilities
- XSS attack vectors
- Authentication/authorization issues
- Sensitive data exposure

</focus_areas>

<workflow>
1. Read the modified files
2. Identify security risks
3. Provide specific remediation steps
4. Rate severity (Critical/High/Medium/Low)
</workflow>
"""

Tailor instructions to the specific task domain. Don't create generic "helper" custom agents.

❌ Bad: "Helpful assistant for code" ✅ Good: "Claude is a React component refactoring specialist. Analyze components for hooks best practices, performance anti-patterns, and accessibility issues." </system_prompt_guidelines>

<configured_agent_xml_structure> custom agent file bodies are developer instructions consumed by the target runtime. Like skills and slash commands, they should use pure XML structure for parsing and token efficiency.

<recommended_tags> Common tags for custom agent structure:

• <role> - Who the custom agent is and what it does
• <constraints> - Hard rules (NEVER/MUST/ALWAYS)
• <focus_areas> - What to prioritize
• <workflow> - Step-by-step process
• <output_format> - How to structure deliverables
• <success_criteria> - Completion criteria
• <validation> - How to verify work

</recommended_tags>

<intelligence_rules> Simple custom agents (single focused task):

• Use role + constraints + workflow minimum
• Example: code-reviewer, test-runner

Medium custom agents (multi-step process):

• Add workflow steps, output_format, success_criteria
• Example: api-researcher, documentation-generator

Complex custom agents (research + generation + validation):

• Add all tags as appropriate including validation, examples
• Example: mcp-api-researcher, comprehensive-auditor

</intelligence_rules>

<critical_rule> Remove ALL markdown headings (##, ###) from custom agent body. Use semantic XML tags instead.

Keep markdown formatting WITHIN content (bold, italic, lists, code blocks, links).

For XML structure principles and token efficiency details, read /skill-standards — the same principles apply to custom agents. </critical_rule> </configured_agent_xml_structure>

<explicit_request> Codex uses custom agent descriptions to select the right agent after the user explicitly asks for a custom agent or subagent workflow. </explicit_request>

Explicitly invoke a custom agent:

> Use the code-reviewer custom agent to check my recent changes

> Have the test-writer custom agent create tests for the new API endpoints

Edit .codex/agents/*.toml or ~/.codex/agents/*.toml files to:

• Create new custom agents
• Edit existing custom agents and their configuration
• Choose project-scoped or user-scoped behavior

Use /agent to switch between active agent threads and inspect running custom agents.

</using_agents_command>

<manual_editing> Edit custom agent files directly:

• Product: .codex/agents/agent-name.toml
• User: ~/.codex/agents/agent-name.toml

</manual_editing>

**Core references**:

• subagents.md: file format, configuration, skill injection, model selection, tool security, prompt caching, complete examples.
• write-subagent-prompts.md: prompt structure, description routing, extended thinking, security constraints, success criteria.
• evaluation-and-testing.md: evaluation metrics, testing strategies, evaluation-driven development, G-Eval.
• error-handling-and-recovery.md: failure causes, recovery strategies, observability, anti-patterns.
• context-management.md: memory architecture, context strategies, long-running tasks, prompt caching.
• orchestration-patterns.md: sequential, parallel, hierarchical, and coordinator patterns with model-selection guidance.
• debugging-agents.md: logging, tracing, hallucinations, format errors, tool misuse, diagnostic procedures.

<failure_modes>

Failure: Runtime-specific examples made SKILL.md exceed the line budget

What happened: Claude added target-specific TOML/YAML examples directly to this SKILL.md until the authored source exceeded /skill-standards' 500-line cap.

Why it failed: The fast path stopped being an overview and absorbed detail that belongs in references.

How to avoid: Keep SKILL.md under 500 lines; move extended examples and configuration matrices to the cited references, then run wc -l "${SKILL_DIR}/SKILL.md" before audit.

</failure_modes>

<success_criteria> A well-configured custom agent has:

• Valid TOML file with name, description, and developer_instructions

• Clear role definition in developer instructions

• Appropriate sandbox and tool-surface restrictions

• XML-structured developer instructions with role, approach, and constraints

• Description field optimized for automatic routing

• At least one verification run or documented dry-run against the custom agent's intended workflow

• Model selection appropriate for task complexity, cost, and reproducibility needs

</success_criteria>