creating-claude-agents

name	creating-claude-agents
description	Use when creating or improving Claude Code agents. Expert guidance on agent file structure, frontmatter, persona definition, tool access, model selection, and validation against schema.
metadata	{"author":"agentworkforce"}

Creating Claude Code Agents - Expert Skill

Use this skill when creating or improving Claude Code agents. Provides comprehensive guidance on agent structure, schema validation, and best practices for building long-running AI assistants.

When to Use This Skill

Activate this skill when:

• User asks to create a new Claude Code agent
• User wants to improve an existing agent
• User needs help with agent frontmatter or structure
• User is troubleshooting agent validation issues
• User wants to understand agent format requirements
• User asks about agent vs skill vs slash command differences

Quick Reference

Agent File Structure

---
name: agent-name
description: When and why to use this agent
allowed-tools: Read, Write, Bash
model: sonnet
agentType: agent
---

# 🔍 Agent Display Name

You are [persona definition - describe the agent's role and expertise].

## Instructions

[Clear, actionable guidance on what the agent does]

## Process

[Step-by-step workflow the agent follows]

## Examples

[Code samples and use cases demonstrating the agent's capabilities]

File Location

Required Path:

.claude/agents/*.md

Agents must be placed in .claude/agents/ directory as markdown files.

Frontmatter Requirements

Required Fields

Field	Type	Description	Example
name	string	Agent identifier (lowercase, hyphens only)	code-reviewer
description	string	Brief overview of functionality and use cases	Reviews code for best practices and potential issues

Optional Fields

Field	Type	Description	Values
allowed-tools	string	Comma-separated list of available tools	Read, Write, Bash, WebSearch
model	string	Claude model to use	sonnet, opus, haiku, inherit
agentType	string	Explicit marker for format preservation	agent

Validation Rules

Name Field:

• Pattern: ^[a-z0-9-]+$ (lowercase letters, numbers, hyphens only)
• Max length: 64 characters
• Example: ✅ code-reviewer ❌ Code_Reviewer

Description Field:

• Max length: 1024 characters
• Should clearly explain when to use the agent
• Start with action words: "Reviews...", "Analyzes...", "Helps with..."

Allowed Tools: Valid tools: Read, Write, Edit, Grep, Glob, Bash, WebSearch, WebFetch, Task, Skill, SlashCommand, TodoWrite, AskUserQuestion

Model Values:

• sonnet - Balanced, good for most agents (default)
• opus - Complex reasoning, architectural decisions
• haiku - Fast, simple tasks
• inherit - Use parent conversation's model

Content Format Requirements

H1 Heading (Required)

The first line of content must be an H1 heading that serves as the agent's display title:

# 🔍 Code Reviewer

Best Practices:

• Include an emoji icon for visual distinction
• Use title case
• Keep concise (2-5 words)
• Make it descriptive and memorable

Persona Definition (Required for Agents)

Immediately after the H1, define the agent's persona using "You are..." format:

You are an expert code reviewer with deep knowledge of software engineering principles and security best practices.

Guidelines:

• Start with "You are..."
• Define role and expertise clearly
• Set expectations for the agent's capabilities
• Establish the agent's approach and tone

Content Structure

# 🔍 Agent Name

You are [persona definition].

## Instructions

[What the agent does and how it approaches tasks]

## Process

1. [Step 1]
2. [Step 2]
3. [Step 3]

## Examples

[Code samples showing good/bad patterns]

## Guidelines

- [Best practice 1]
- [Best practice 2]

Schema Validation

Agents must conform to the JSON schema at: https://github.com/pr-pm/prpm/blob/main/packages/converters/schemas/claude-agent.schema.json

Schema Structure

{
  "frontmatter": {
    "name": "string (required)",
    "description": "string (required)",
    "allowed-tools": "string (optional)",
    "model": "enum (optional)",
    "agentType": "agent (optional)"
  },
  "content": "string (markdown with H1, persona, instructions)"
}

Common Validation Errors

Error	Cause	Fix
Missing required field 'name'	Frontmatter lacks name field	Add name: agent-name
Missing required field 'description'	Frontmatter lacks description	Add description: ...
Invalid name pattern	Name contains uppercase or special chars	Use lowercase and hyphens only
Name too long	Name exceeds 64 characters	Shorten the name
Invalid model value	Model not in enum	Use: sonnet, opus, haiku, or inherit
Missing H1 heading	Content doesn't start with #	Add # Agent Name as first line

Tool Configuration

Inheriting All Tools

Omit the allowed-tools field to inherit all tools from the parent conversation:

---
name: full-access-agent
description: Agent needs access to everything
# No allowed-tools field = inherits all
---

Specific Tools Only

Grant minimal necessary permissions:

---
name: read-only-reviewer
description: Reviews code without making changes
allowed-tools: Read, Grep, Bash
---

Bash Tool Restrictions

Use command patterns to restrict Bash access:

---
name: git-helper
description: Git operations only
allowed-tools: Bash(git *), Read
---

Syntax:

• Bash(git *) - Only git commands
• Bash(npm test:*) - Only npm test scripts
• Bash(git status:*), Bash(git diff:*) - Multiple specific commands

Model Selection Guide

Sonnet (Most Agents)

Use for:

• Code review
• Debugging
• Data analysis
• General problem-solving

model: sonnet

Opus (Complex Reasoning)

Use for:

• Architecture decisions
• Complex refactoring
• Deep security analysis
• Novel problem-solving

model: opus

Haiku (Speed Matters)

Use for:

• Syntax checks
• Simple formatting
• Quick validations
• Low-latency needs

model: haiku

Inherit (Context-Dependent)

Use for:

• Agent should match user's model choice
• Cost sensitivity

model: inherit

Common Mistakes

Mistake	Problem	Solution
Using _ in name	Violates pattern constraint	Use hyphens: code-reviewer not code_reviewer
Uppercase in name	Violates pattern constraint	Lowercase only: debugger not Debugger
Missing persona	Agent lacks role definition	Add "You are..." after H1
No H1 heading	Content format invalid	Start content with # Agent Name
Vague description	Agent won't activate correctly	Be specific about when to use
Too many tools	Security risk, violates least privilege	Grant only necessary tools
No agentType field	May lose type info in conversion	Add agentType: agent
Generic agent name	Conflicts or unclear purpose	Use specific, descriptive names

Best Practices

1. Write Clear, Specific Descriptions

The description determines when Claude automatically invokes your agent.

✅ Good:

description: Reviews code changes for quality, security, and maintainability issues

❌ Poor:

description: A helpful agent  # Too vague

2. Define Strong Personas

Establish expertise and approach immediately after the H1:

# 🔍 Code Reviewer

You are an expert code reviewer specializing in TypeScript and React, with 10+ years of experience in security-focused development. You approach code review systematically, checking for security vulnerabilities, performance issues, and maintainability concerns.

3. Provide Step-by-Step Processes

Guide the agent's workflow explicitly:

## Review Process

1. **Read the changes**
   - Get recent git diff or specified files
   - Understand the context and purpose

2. **Analyze systematically**
   - Check each category (quality, security, performance)
   - Provide specific file:line references
   - Explain why something is an issue

3. **Provide actionable feedback**
   - Categorize by severity
   - Include fix suggestions
   - Highlight positive patterns

4. Include Examples

Show both good and bad patterns:

## Examples

When reviewing error handling:

❌ **Bad - Silent failure:**
\`\`\`typescript
try {
  await fetchData();
} catch (error) {
  console.log(error);
}
\`\`\`

✅ **Good - Proper error handling:**
\`\`\`typescript
try {
  await fetchData();
} catch (error) {
  logger.error('Failed to fetch data', error);
  throw new AppError('Data fetch failed', { cause: error });
}
\`\`\`

5. Use Icons in H1 for Visual Distinction

Choose emojis that represent the agent's purpose:

• 🔍 Code Reviewer
• 🐛 Debugger
• 📊 Data Scientist
• 🔒 Security Auditor
• ⚡ Performance Optimizer
• 📝 Documentation Writer
• 🧪 Test Generator

6. Maintain Single Responsibility

Each agent should excel at ONE specific task:

✅ Good:

• code-reviewer - Reviews code for quality and security
• debugger - Root cause analysis and minimal fixes

❌ Poor:

• code-helper - Reviews, debugs, tests, refactors, documents (too broad)

7. Grant Minimal Tool Access

Follow the principle of least privilege:

# Read-only analysis agent
allowed-tools: Read, Grep

# Code modification agent
allowed-tools: Read, Edit, Bash(git *)

# Full development agent
allowed-tools: Read, Write, Edit, Bash, Grep, Glob

8. Include agentType for Round-Trip Conversion

Always include agentType: agent in frontmatter to preserve type information during format conversions:

---
name: code-reviewer
description: Reviews code for best practices
agentType: agent
---

Example Agent Templates

Minimal Agent

---
name: simple-reviewer
description: Quick code review for common issues
allowed-tools: Read, Grep
model: haiku
agentType: agent
---

# 🔍 Simple Code Reviewer

You are a code reviewer focused on catching common mistakes quickly.

## Instructions

Review code for:
- Syntax errors
- Common anti-patterns
- Missing error handling
- Console.log statements

Provide concise feedback with file:line references.

Comprehensive Agent

---
name: security-auditor
description: Deep security vulnerability analysis for code changes
allowed-tools: Read, Grep, WebSearch, Bash(git *)
model: opus
agentType: agent
---

# 🔒 Security Auditor

You are a security expert specializing in application security, with expertise in OWASP Top 10, secure coding practices, and threat modeling. You perform thorough security analysis of code changes.

## Review Process

1. **Gather Context**
   - Read changed files
   - Review git history for context
   - Identify data flows and trust boundaries

2. **Security Analysis**
   - Input validation and sanitization
   - Authentication and authorization
   - SQL injection risks
   - XSS vulnerabilities
   - CSRF protection
   - Secrets exposure
   - Cryptography usage
   - Dependency vulnerabilities

3. **Threat Assessment**
   - Rate severity (Critical/High/Medium/Low)
   - Assess exploitability
   - Determine business impact
   - Provide remediation guidance

4. **Report Findings**
   Use structured format with CVE references where applicable.

## Output Format

**Security Score: X/10**

### Critical Issues (Fix Immediately)
- [Vulnerability] (file:line) - [Explanation] - [CVE if applicable] - [Fix]

### High Priority
- [Issue] (file:line) - [Explanation] - [Fix]

### Medium Priority
- [Concern] (file:line) - [Explanation] - [Recommendation]

### Best Practices
- [Positive security pattern observed]

**Recommendation:** [Approve/Request Changes/Block]

## Examples

### SQL Injection Check

❌ **Vulnerable:**
\`\`\`typescript
const query = \`SELECT * FROM users WHERE id = \${userId}\`;
db.query(query);
\`\`\`

✅ **Safe:**
\`\`\`typescript
const query = 'SELECT * FROM users WHERE id = $1';
db.query(query, [userId]);
\`\`\`

Validation Checklist

Before finalizing an agent:

• Name is lowercase with hyphens only
• Name is 64 characters or less
• Description clearly explains when to use the agent
• Description is 1024 characters or less
• Content starts with H1 heading (with emoji icon)
• Persona is defined using "You are..." format
• Process or instructions are clearly outlined
• Examples are included (showing good/bad patterns)
• Tool access is minimal and specific
• Model selection is appropriate for task complexity
• agentType field is set to "agent"
• File is saved in .claude/agents/ directory
• Agent has been tested with real tasks
• Edge cases are considered

Schema Reference

Official Schema URL:

https://github.com/pr-pm/prpm/blob/main/packages/converters/schemas/claude-agent.schema.json

Local Schema Path:

/Users/khaliqgant/Projects/prpm/app/packages/converters/schemas/claude-agent.schema.json

Related Documentation

• agent-builder skill - Creating effective subagents
• slash-command-builder skill - For simpler, command-based prompts
• creating-skills skill - For context-aware reference documentation
• Claude Code Docs: https://docs.claude.com/claude-code

Agents vs Skills vs Commands

Use Agents When:

• ✅ Long-running assistants with persistent context
• ✅ Complex multi-step workflows
• ✅ Specialized expertise needed
• ✅ Tool access required
• ✅ Repeatable processes with quality standards

Use Skills When:

• ✅ Context-aware automatic activation
• ✅ Reference documentation and patterns
• ✅ Team standardization
• ✅ No persistent state needed

Use Slash Commands When:

• ✅ Simple, focused prompts
• ✅ Quick manual invocation
• ✅ Personal productivity shortcuts
• ✅ Single-file prompts

Decision Tree:

Need specialized AI assistant?
├─ Yes → Needs tools and persistent context?
│         ├─ Yes → Use Agent
│         └─ No → Quick invocation?
│                 ├─ Yes → Use Slash Command
│                 └─ No → Use Skill
└─ No → Just documentation? → Use Skill

Troubleshooting

Agent Not Activating

Problem: Agent doesn't get invoked when expected

Solutions:

1. Make description more specific to match use case
2. Verify file is in .claude/agents/*.md
3. Check for frontmatter syntax errors
4. Explicitly request: "Use the [agent-name] agent"

Validation Errors

Problem: Agent file doesn't validate against schema

Solutions:

1. Check name pattern (lowercase, hyphens only)
2. Verify required fields (name, description)
3. Ensure content starts with H1 heading
4. Validate model value is in enum
5. Check allowed-tools spelling and capitalization

Tool Permission Denied

Problem: Agent can't access needed tools

Solutions:

1. Add tools to allowed-tools in frontmatter
2. Use correct capitalization (e.g., Read, not read)
3. For Bash restrictions, use pattern syntax: Bash(git *)
4. Omit allowed-tools field to inherit all tools

Poor Agent Performance

Problem: Agent produces inconsistent or low-quality results

Solutions:

1. Strengthen persona definition
2. Add more specific process steps
3. Include examples of good/bad patterns
4. Define explicit output format
5. Consider using more powerful model (opus)
6. Break complex agents into specialized ones

Remember: Great agents are specialized experts with clear personas, step-by-step processes, and minimal tool access. Focus each agent on doing ONE thing exceptionally well with measurable outcomes.

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