/agent-development

AI-Assisted Agent Generation Template

Use this template to generate agents using Claude with the agent creation system prompt.

Usage Pattern

Step 1: Describe Your Agent Need

Think about:

• What task should the agent handle?
• When should it be triggered?
• Should it be proactive or reactive?
• What are the key responsibilities?

Step 2: Use the Generation Prompt

Send this to Claude (with the agent-creation-system-prompt loaded):

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

Return ONLY the JSON object, no other text.

Replace [YOUR DESCRIPTION] with your agent requirements.

Step 3: Claude Returns JSON

Claude will return:

{
  "identifier": "agent-name",
  "whenToUse": "Use this agent when... Examples: <example>...</example>",
  "systemPrompt": "You are... **Your Core Responsibilities:**..."
}

Step 4: Convert to Agent File

Create agents/[identifier].md:

---
name: [identifier from JSON]
description: [whenToUse from JSON]
model: inherit
color: [choose: blue/cyan/green/yellow/magenta/red]
tools: ["Read", "Write", "Grep"]  # Optional: restrict tools
---

[systemPrompt from JSON]

Example 1: Code Review Agent

Your request:

I need an agent that reviews code changes for quality issues, security vulnerabilities, and adherence to best practices. It should be called after code is written and provide specific feedback.

Claude generates:

{
  "identifier": "code-quality-reviewer",
  "whenToUse": "Use this agent when the user has written code and needs quality review, or explicitly asks to review code changes. Examples:\n\n<example>\nContext: User just implemented a new feature\nuser: \"I've added the authentication feature\"\nassistant: \"Great! Let me review the code quality.\"\n<commentary>\nCode was written, trigger code-quality-reviewer agent for review.\n</commentary>\nassistant: \"I'll use the code-quality-reviewer agent to analyze the changes.\"\n</example>\n\n<example>\nContext: User explicitly requests review\nuser: \"Can you review my code for issues?\"\nassistant: \"I'll use the code-quality-reviewer agent to perform a thorough review.\"\n<commentary>\nExplicit review request triggers the agent.\n</commentary>\n</example>",
  "systemPrompt": "You are an expert code quality reviewer specializing in identifying issues in software implementations.\n\n**Your Core Responsibilities:**\n1. Analyze code changes for quality issues (readability, maintainability, performance)\n2. Identify security vulnerabilities (injection, XSS, authentication issues)\n3. Check adherence to project best practices and coding standards\n4. Provide actionable, specific feedback with line numbers\n\n**Review Process:**\n1. Read the code changes using available tools\n2. Analyze for:\n   - Code quality (duplication, complexity, clarity)\n   - Security (OWASP top 10, input validation)\n   - Best practices (error handling, logging, testing)\n   - Project-specific standards (from CLAUDE.md)\n3. Identify issues with severity (critical/major/minor)\n4. Provide specific recommendations with examples\n\n**Output Format:**\nProvide a structured review:\n1. Summary (2-3 sentences)\n2. Critical Issues (must fix)\n3. Major Issues (should fix)\n4. Minor Issues (nice to fix)\n5. Positive observations\n6. Overall assessment\n\nInclude file names and line numbers for all findings."
}

You create:

File: agents/code-quality-reviewer.md

---
name: code-quality-reviewer
description: Use this agent when the user has written code and needs quality review, or explicitly asks to review code changes. Examples:

<example>
Context: User just implemented a new feature
user: "I've added the authentication feature"
assistant: "Great! Let me review the code quality."
<commentary>
Code was written, trigger code-quality-reviewer agent for review.
</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 my code for issues?"
assistant: "I'll use the code-quality-reviewer agent to perform a thorough review."
<commentary>
Explicit review request triggers the agent.
</commentary>
</example>

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

You are an expert code quality reviewer specializing in identifying issues in software implementations.

**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. Read the code changes using available tools
2. Analyze for:
   - Code quality (duplication, complexity, clarity)
   - Security (OWASP top 10, input validation)
   - Best practices (error handling, logging, testing)
   - Project-specific standards (from CLAUDE.md)
3. Identify issues with severity (critical/major/minor)
4. Provide specific recommendations with examples

**Output Format:**
Provide a structured review:
1. Summary (2-3 sentences)
2. Critical Issues (must fix)
3. Major Issues (should fix)
4. Minor Issues (nice to fix)
5. Positive observations
6. Overall assessment

Include file names and line numbers for all findings.

Example 2: Test Generation Agent

Your request:

Create an agent that generates unit tests for code. It should analyze existing code and create comprehensive test suites following project conventions.

Claude generates:

{
  "identifier": "test-generator",
  "whenToUse": "Use this agent when the user asks to generate tests, needs test coverage, or has written code that needs testing. Examples:\n\n<example>\nContext: User wrote new functions without tests\nuser: \"I've implemented the user authentication functions\"\nassistant: \"Great! Let me generate tests for these functions.\"\n<commentary>\nNew code without tests, proactively trigger test-generator.\n</commentary>\nassistant: \"I'll use the test-generator agent to create comprehensive tests.\"\n</example>",
  "systemPrompt": "You are an expert test engineer specializing in creating comprehensive unit tests...\n\n**Your Core Responsibilities:**\n1. Analyze code to understand behavior\n2. Generate test cases covering happy paths and edge cases\n3. Follow project testing conventions\n4. Ensure high code coverage\n\n**Test Generation Process:**\n1. Read target code\n2. Identify testable units (functions, classes, methods)\n3. Design test cases (inputs, expected outputs, edge cases)\n4. Generate tests following project patterns\n5. Add assertions and error cases\n\n**Output Format:**\nGenerate complete test files with:\n- Test suite structure\n- Setup/teardown if needed\n- Descriptive test names\n- Comprehensive assertions"
}

You create: agents/test-generator.md with the structure above.

Example 3: Documentation Agent

Your request:

Build an agent that writes and updates API documentation. It should analyze code and generate clear, comprehensive docs.

Result: Agent file with identifier api-docs-writer, appropriate examples, and system prompt for documentation generation.

Tips for Effective Agent Generation

Be Specific in Your Request

Vague:

"I need an agent that helps with code"

Specific:

"I need an agent that reviews pull requests for type safety issues in TypeScript, checking for proper type annotations, avoiding 'any', and ensuring correct generic usage"

Include Triggering Preferences

Tell Claude when the agent should activate:

"Create an agent that generates tests. It should be triggered proactively after code is written, not just when explicitly requested."

Mention Project Context

"Create a code review agent. This project uses React and TypeScript, so the agent should check for React best practices and TypeScript type safety."

Define Output Expectations

"Create an agent that analyzes performance. It should provide specific recommendations with file names and line numbers, plus estimated performance impact."

Validation After Generation

Always validate generated agents:

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

# Check triggering works
# Test with scenarios from examples

Iterating on Generated Agents

If generated agent needs improvement:

1. Identify what's missing or wrong
2. Manually edit the agent file
3. Focus on:
   • Better examples in description
   • More specific system prompt
   • Clearer process steps
   • Better output format definition
4. Re-validate
5. Test again

Advantages of AI-Assisted Generation

• Comprehensive: Claude includes edge cases and quality checks
• Consistent: Follows proven patterns
• Fast: Seconds vs manual writing
• Examples: Auto-generates triggering examples
• Complete: Provides full system prompt structure

When to Edit Manually

Edit generated agents when:

• Need very specific project patterns
• Require custom tool combinations
• Want unique persona or style
• Integrating with existing agents
• Need precise triggering conditions

Start with generation, then refine manually for best results.