Create Agent Command

Create autonomous Claude Code agents that handle complex, multi-step tasks independently. This command provides comprehensive guidance based on official Anthropic documentation and proven patterns.

User Input

Agent Name: $1
Description: $2

What Are Agents?

Agents are autonomous subprocesses spawned via the Task tool that:

• Handle complex, multi-step tasks independently
• Have their own isolated context window
• Return results to the parent conversation
• Can be specialized for specific domains

Agent File Structure

Agents use a unique format combining YAML frontmatter with a markdown system prompt:

---
name: agent-identifier
description: Use this agent when [triggering conditions]. Examples:

<example>
Context: [Situation description]
user: "[User request]"
assistant: "[How assistant should respond and use this agent]"
<commentary>
[Why this agent should be triggered]
</commentary>
</example>

<example>
[Additional example...]
</example>

model: inherit
color: blue
tools: ["Read", "Write", "Grep"]
---

You are [agent role description]...

**Your Core Responsibilities:**
1. [Responsibility 1]
2. [Responsibility 2]

**Analysis Process:**
[Step-by-step workflow]

**Output Format:**
[What to return]

Frontmatter Fields Reference

Required Fields

name (Required)

Format: Lowercase with hyphens only Length: 3-50 characters Rules:

• Must start and end with alphanumeric character
• Only lowercase letters, numbers, and hyphens
• No underscores, spaces, or special characters

description (Required, Critical)

The most important field - Defines when Claude triggers the agent.

Requirements:

• Length: 10-5,000 characters (ideal: 200-1,000 with 2-4 examples)
• MUST start with: "Use this agent when..."
• MUST include: <example> blocks showing usage patterns
• Each example needs: context, user request, assistant response, commentary

Example Block Format:

<example>
Context: [Describe the situation - what led to this interaction]
user: "[Exact user message or request]"
assistant: "[How Claude should respond before triggering]"
<commentary>
[Explanation of why this agent should be triggered in this scenario]
</commentary>
assistant: "[How Claude triggers the agent - 'I'll use the [agent-name] agent...']"
</example>

Best Practices for Descriptions:

• Include 2-4 concrete examples
• Show both proactive and reactive triggering scenarios
• Cover different phrasings of the same intent
• Explain reasoning in commentary
• Be specific about when NOT to use the agent

model (Required)

Values: inherit, sonnet, opus, haiku Default: inherit (recommended)

Recommendation: Use inherit unless you have a specific reason to override.

color (Required)

Purpose: Visual indicator in UI to distinguish agents

Values: blue, cyan, green, yellow, magenta, red

Best Practice: Use different colors for different agents in the same plugin to distinguish them visually.

Optional Fields

tools (Optional)

Purpose: Restrict available tools (principle of least privilege) Format: Array of tool names Default: All tools available

# Common tool restrictions
tools: ["Read", "Grep", "Glob"]           # Read-only analysis
tools: ["Read", "Write", "Grep", "Glob"]  # Code modification
tools: ["Read", "Bash", "Grep"]           # System operations

Security Principle: Only grant tools the agent actually needs.

Triggering Patterns

Pattern 1: Explicit Request

User directly asks for the agent's function.

<example>
Context: User wants code reviewed
user: "Can you review my code changes?"
assistant: "I'll use the code-reviewer agent to analyze the changes."
<commentary>
User explicitly requested code review, trigger the code-reviewer agent.
</commentary>
</example>

Pattern 2: Implicit Need

Agent needed based on context, not explicit request.

<example>
Context: User mentions code is hard to understand
user: "This code is confusing and hard to follow"
assistant: "I can help improve the code clarity."
<commentary>
User indicates complexity issues. Trigger code-simplifier agent to refactor
for better readability.
</commentary>
assistant: "I'll use the code-simplifier agent to suggest improvements."
</example>

Pattern 3: Proactive Trigger

Agent triggers after completing relevant work without explicit request.

<example>
Context: User just implemented database queries
user: "I've added the user search functionality"
assistant: "Great! Let me review it for security."
<commentary>
Database code written. Proactively trigger security-analyzer to check
for SQL injection and other database security issues.
</commentary>
assistant: "I'll use the security-analyzer agent to check the database code."
</example>

Pattern 4: Tool Usage Pattern

Agent triggers based on prior tool usage.

<example>
Context: User made multiple edits to test files
user: "I've updated all the tests"
assistant: "Great! Let me verify test quality."
<commentary>
Multiple Edit tools used on test files. Proactively trigger test-quality-analyzer
to ensure tests follow best practices.
</commentary>
assistant: "I'll use the test-quality-analyzer agent to review the tests."
</example>

System Prompt Design

The system prompt (markdown body after frontmatter) defines agent behavior. Use this proven template:

You are [role] specializing in [domain].

**Your Core Responsibilities:**
1. [Primary responsibility - what the agent MUST do]
2. [Secondary responsibility]
3. [Additional responsibilities...]

**Analysis Process:**
1. [Step one - be specific]
2. [Step two]
3. [Step three]
[...]

**Quality Standards:**
- [Standard 1 - measurable criteria]
- [Standard 2]

**Output Format:**
Provide results in this format:
- [What to include]
- [How to structure]

**Edge Cases:**
Handle these situations:
- [Edge case 1]: [How to handle]
- [Edge case 2]: [How to handle]

**What NOT to Do:**
- [Anti-pattern 1]
- [Anti-pattern 2]

System Prompt Principles

• Issue 1`" | "Use proper formatting" | | Define boundaries | "Do NOT modify files, only analyze" | No boundaries stated | | Provide fallbacks | "If unsure, ask for clarification" | Assume and proceed | | Quality mechanisms | "Verify each finding with evidence" | No verification |

Validation Requirements

System prompts must be:

• Length: 20-10,000 characters (ideal: 500-3,000)
• Well-structured: Clear sections with responsibilities, process, output format
• Specific: Actionable instructions, not vague guidance
• Complete: Handles edge cases and quality standards

AI-Assisted Agent Generation

Use this prompt to generate agent configurations automatically:

Create an agent configuration based on this request: "[YOUR DESCRIPTION]"

Requirements:
1. Extract core intent and responsibilities
2. Design expert persona for the domain
3. Create comprehensive system prompt with:
   - Clear behavioral boundaries
   - Specific methodologies
   - Edge case handling
   - Output format
4. Create identifier (lowercase, hyphens, 3-50 chars)
5. Write description with triggering conditions
6. Include 2-3 <example> blocks showing when to use

Return JSON with:
{
  "identifier": "agent-name",
  "whenToUse": "Use this agent when... Examples: <example>...</example>",
  "systemPrompt": "You are..."
}

Elite Agent Architect Process

When creating agents, follow this 6-step process:

1. Extract Core Intent: Identify fundamental purpose, key responsibilities, success criteria
2. Design Expert Persona: Create compelling expert identity with domain knowledge
3. Architect Comprehensive Instructions: Behavioral boundaries, methodologies, edge cases, output formats
4. Optimize for Performance: Decision frameworks, quality control, workflow patterns, fallback strategies
5. Create Identifier: Concise, descriptive, 2-4 words with hyphens
6. Generate Examples: Triggering scenarios with context, user/assistant dialogue, commentary

Default Agent Standards

Frontmatter Rules

• description: Keep to ONE sentence - descriptions load into parent context, every token counts
• Do NOT add verbose <example> blocks in description - they waste context tokens

Required Agent Sections (in order)

1. Title - # with strong identity statement
2. Identity - Quality expectations and motivation (consequences for poor work)
3. Goal - Clear single-paragraph objective
4. Input - What files/data the agent receives
5. CRITICAL: Load Context - Explicit requirement to read ALL relevant files BEFORE analysis
6. Process/Stages - Step-by-step workflow with proper ordering

Process Stage Ordering (critical for multi-stage agents)

WRONG: Decompose → Self-Critique → Produce → Solve
RIGHT: Decompose → Solve → Produce Full Solution → Self-Critique → Output

• Self-critique comes as the last step, always
• Always produce everything first, then evaluate and select

Decision Tables

Put reasoning column BEFORE decision column:

WRONG: | Section | Include? | Reasoning |
RIGHT: | Section | Reasoning | Include? |

This forces the agent to explain WHY before deciding, improving decision quality.

Validation Rules

Structural Validation

Validation Script

# Validate agent structure
scripts/validate-agent.sh agents/your-agent.md

Quality Checklist

Before deployment:

• Name follows conventions (lowercase, hyphens, 3-50 chars)
• Description starts with "Use this agent when..."
• Description includes 2-4 <example> blocks
• Each example has context, user, assistant, commentary
• Model is appropriate for task complexity
• Color is unique among related agents
• Tools restricted to what's needed (least privilege)
• System prompt has clear structure
• Responsibilities are specific and actionable
• Process steps are concrete
• Output format is defined
• Edge cases are addressed

Production Examples

Code Quality Reviewer Agent

---
name: code-quality-reviewer
description: Use this agent when the user asks to review code changes, check code quality, or analyze modifications for bugs and improvements. Examples:

<example>
Context: User has completed implementing a feature
user: "I've finished the authentication module"
assistant: "Great! Let me review it for quality."
<commentary>
Code implementation complete. Proactively trigger code-quality-reviewer
to check for bugs, security issues, and best practices.
</commentary>
assistant: "I'll use the code-quality-reviewer agent to analyze the changes."
</example>

<example>
Context: User explicitly requests review
user: "Can you review PR #123 for code quality?"
assistant: "I'll use the code-quality-reviewer agent to analyze the PR."
<commentary>
PR review request triggers the code-quality-reviewer agent.
</commentary>
</example>

model: inherit
color: blue
tools: ["Read", "Grep", "Glob"]
---

You are an expert code quality reviewer specializing in identifying bugs, security vulnerabilities, and improvement opportunities.

**Your Core Responsibilities:**
1. Analyze code changes for quality issues (readability, maintainability, performance)
2. Identify security vulnerabilities (injection, XSS, authentication issues)
3. Check adherence to project best practices and coding standards
4. Provide actionable, specific feedback with line numbers

**Review Process:**
1. **Initial scan**: Quick pass for obvious issues
2. **Deep analysis**: Line-by-line review of changed code
3. **Context evaluation**: Check impact on related code
4. **Best practices**: Compare against project and language standards
5. **Recommendations**: Prioritized list of improvements

**Output Format:**
For each file reviewed:

## Summary
[2-3 sentence overview]

## Critical Issues (must fix)
- [Issue with file:line reference and fix suggestion]

## Major Issues (should fix)
- [Issue with explanation]

## Minor Issues (nice to fix)
- [Suggestion]

## Positive Observations
- [What was done well]

## Overall Assessment
[Pass/Needs Work/Fail with reasoning]

**Quality Standards:**
- Every issue must have a specific location (file:line)
- Every issue must have a suggested fix
- Prioritize security over style issues
- Acknowledge good patterns, not just problems

Test Generator Agent

---
name: test-generator
description: Use this agent when the user asks for test generation, needs test coverage, or has written new code requiring tests. Examples:

<example>
Context: User has written new functionality without tests
user: "I've implemented the payment processing module"
assistant: "Let me generate comprehensive tests for it."