Claude Code Agent Development

Agent Development for Claude Code Plugins

Overview

Agents are autonomous subprocesses that handle complex, multi-step tasks independently. Understanding agent structure, triggering conditions, and system prompt design enables creating powerful autonomous capabilities.

Key concepts:

• Agents are FOR autonomous work, commands are FOR user-initiated actions
• Markdown file format with YAML frontmatter
• Triggering via description field with examples
• System prompt defines agent behavior
• Model and color customization

Agent File Structure

Complete Format

---
name: agent-identifier
description: Use this agent when [triggering conditions]. Typical triggers include [scenario 1 in prose], [scenario 2 in prose], and [scenario 3 in prose]. See "When to invoke" in the agent body for worked scenarios.
model: inherit
color: blue
tools: ["Read", "Write", "Grep"]
---

You are [agent role description]...

## When to invoke

[Two to four representative scenarios written as prose, e.g.:]
- **[Scenario name].** [What the situation looks like and what the agent should do.]
- **[Scenario name].** [Same.]

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

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

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

Frontmatter Fields

name (required)

Agent identifier used for namespacing and invocation.

Format: lowercase, numbers, hyphens only Length: 3-50 characters Pattern: Must start and end with alphanumeric

Good examples:

• code-reviewer
• test-generator
• api-docs-writer
• security-analyzer

Bad examples:

• helper (too generic)
• -agent- (starts/ends with hyphen)
• my_agent (underscores not allowed)
• ag (too short, < 3 chars)

description (required)

Defines when Claude should trigger this agent. This is the most critical field — it is loaded into context whenever the agent is registered, so the harness can decide when to dispatch.

Must include:

1. Triggering conditions ("Use this agent when...")
2. A short prose summary of the typical trigger scenarios
3. A pointer to a "When to invoke" section in the agent body for the detailed worked scenarios

Format:

Use this agent when [conditions]. Typical triggers include [scenario 1 in prose], [scenario 2 in prose], and [scenario 3 in prose]. See "When to invoke" in the agent body for worked scenarios.

Best practices:

• Name 2-4 trigger scenarios in the prose summary
• Cover both proactive (assistant invokes itself) and reactive (user requests) triggering
• Cover different phrasings of the same intent
• Be specific about when NOT to use the agent
• Put detailed scenarios in the body under "When to invoke" as a bullet list of prose descriptions

model (required)

Which model the agent should use.

Options:

• inherit - Use same model as parent (recommended)
• sonnet - Claude Sonnet (balanced)
• opus - Claude Opus (most capable, expensive)
• haiku - Claude Haiku (fast, cheap)

Recommendation: Use inherit unless agent needs specific model capabilities.

color (required)

Visual identifier for agent in UI.

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

Guidelines:

• Choose distinct colors for different agents in same plugin
• Use consistent colors for similar agent types
• Blue/cyan: Analysis, review
• Green: Success-oriented tasks
• Yellow: Caution, validation
• Red: Critical, security
• Magenta: Creative, generation

tools (optional)

Restrict agent to specific tools.

Format: Array of tool names

tools: ["Read", "Write", "Grep", "Bash"]

Default: If omitted, agent has access to all tools

Best practice: Limit tools to minimum needed (principle of least privilege)

Common tool sets:

• Read-only analysis: ["Read", "Grep", "Glob"]
• Code generation: ["Read", "Write", "Grep"]
• Testing: ["Read", "Bash", "Grep"]
• Full access: Omit field or use ["*"]

System Prompt Design

The markdown body becomes the agent's system prompt. Write in second person, addressing the agent directly.

Structure

Standard template:

You are [role] specializing in [domain].

**Your Core Responsibilities:**
1. [Primary responsibility]
2. [Secondary responsibility]
3. [Additional responsibilities...]

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

**Quality Standards:**
- [Standard 1]
- [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]

Best Practices

✅ DO:

• Write in second person ("You are...", "You will...")
• Be specific about responsibilities
• Provide step-by-step process
• Define output format
• Include quality standards
• Address edge cases
• Keep under 10,000 characters

❌ DON'T:

• Write in first person ("I am...", "I will...")
• Be vague or generic
• Omit process steps
• Leave output format undefined
• Skip quality guidance
• Ignore error cases

Creating Agents

Method 1: AI-Assisted Generation

Use this prompt pattern (extracted from Claude Code):

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
   - A "When to invoke" section listing 2-4 trigger scenarios as prose bullets
4. Create identifier (lowercase, hyphens, 3-50 chars)
5. Write description with triggering conditions and a short prose summary of trigger scenarios

Return JSON with:
{
  "identifier": "agent-name",
  "whenToUse": "Use this agent when... Typical triggers include [...]. See \"When to invoke\" in the agent body.",
  "systemPrompt": "You are..."
}

Then convert to agent file format with frontmatter.

See examples/agent-creation-prompt.md for complete template.

Method 2: Manual Creation

1. Choose agent identifier (3-50 chars, lowercase, hyphens)
2. Write description with examples
3. Select model (usually inherit)
4. Choose color for visual identification
5. Define tools (if restricting access)
6. Write system prompt with structure above
7. Save as agents/agent-name.md

Validation Rules

Identifier Validation

✅ Valid: code-reviewer, test-gen, api-analyzer-v2
❌ Invalid: ag (too short), -start (starts with hyphen), my_agent (underscore)

Rules:

• 3-50 characters
• Lowercase letters, numbers, hyphens only
• Must start and end with alphanumeric
• No underscores, spaces, or special characters

Description Validation

Length: 10-5,000 characters Must include: Triggering conditions and examples Best: 200-1,000 characters with 2-4 examples