Agent Development

This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.

5 stars

Best use case

Agent Development is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.

Teams using Agent Development should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/agent-development/SKILL.md --create-dirs "https://raw.githubusercontent.com/m31uk3/ai-skills/main/skills/anthropic/official-skills/agent-development/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/agent-development/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Agent Development Compares

Feature / AgentAgent DevelopmentStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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

```markdown
---
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

### 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.**

**Must include:**
1. Triggering conditions ("Use this agent when...")
2. Multiple `<example>` blocks showing usage
3. Context, user request, and assistant response in each example
4. `<commentary>` explaining why agent triggers

**Format:**
```
Use this agent when [conditions]. Examples:

<example>
Context: [Scenario description]
user: "[What user says]"
assistant: "[How Claude should respond]"
<commentary>
[Why this agent is appropriate]
</commentary>
</example>

[More examples...]
```

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

### 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

```yaml
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:**
```markdown
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
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..."
}
```

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

### System Prompt Validation

**Length:** 20-10,000 characters
**Best:** 500-3,000 characters
**Structure:** Clear responsibilities, process, output format

## Agent Organization

### Plugin Agents Directory

```
plugin-name/
└── agents/
    ├── analyzer.md
    ├── reviewer.md
    └── generator.md
```

All `.md` files in `agents/` are auto-discovered.

### Namespacing

Agents are namespaced automatically:
- Single plugin: `agent-name`
- With subdirectories: `plugin:subdir:agent-name`

## Testing Agents

### Test Triggering

Create test scenarios to verify agent triggers correctly:

1. Write agent with specific triggering examples
2. Use similar phrasing to examples in test
3. Check Claude loads the agent
4. Verify agent provides expected functionality

### Test System Prompt

Ensure system prompt is complete:

1. Give agent typical task
2. Check it follows process steps
3. Verify output format is correct
4. Test edge cases mentioned in prompt
5. Confirm quality standards are met

## Quick Reference

### Minimal Agent

```markdown
---
name: simple-agent
description: Use this agent when... Examples: <example>...</example>
model: inherit
color: blue
---

You are an agent that [does X].

Process:
1. [Step 1]
2. [Step 2]

Output: [What to provide]
```

### Frontmatter Fields Summary

| Field | Required | Format | Example |
|-------|----------|--------|---------|
| name | Yes | lowercase-hyphens | code-reviewer |
| description | Yes | Text + examples | Use when... <example>... |
| model | Yes | inherit/sonnet/opus/haiku | inherit |
| color | Yes | Color name | blue |
| tools | No | Array of tool names | ["Read", "Grep"] |

### Best Practices

**DO:**
- ✅ Include 2-4 concrete examples in description
- ✅ Write specific triggering conditions
- ✅ Use `inherit` for model unless specific need
- ✅ Choose appropriate tools (least privilege)
- ✅ Write clear, structured system prompts
- ✅ Test agent triggering thoroughly

**DON'T:**
- ❌ Use generic descriptions without examples
- ❌ Omit triggering conditions
- ❌ Give all agents same color
- ❌ Grant unnecessary tool access
- ❌ Write vague system prompts
- ❌ Skip testing

## Additional Resources

### Reference Files

For detailed guidance, consult:

- **`references/system-prompt-design.md`** - Complete system prompt patterns
- **`references/triggering-examples.md`** - Example formats and best practices
- **`references/agent-creation-system-prompt.md`** - The exact prompt from Claude Code

### Example Files

Working examples in `examples/`:

- **`agent-creation-prompt.md`** - AI-assisted agent generation template
- **`complete-agent-examples.md`** - Full agent examples for different use cases

### Utility Scripts

Development tools in `scripts/`:

- **`validate-agent.sh`** - Validate agent file structure
- **`test-agent-trigger.sh`** - Test if agent triggers correctly

## Implementation Workflow

To create an agent for a plugin:

1. Define agent purpose and triggering conditions
2. Choose creation method (AI-assisted or manual)
3. Create `agents/agent-name.md` file
4. Write frontmatter with all required fields
5. Write system prompt following best practices
6. Include 2-4 triggering examples in description
7. Validate with `scripts/validate-agent.sh`
8. Test triggering with real scenarios
9. Document agent in plugin README

Focus on clear triggering conditions and comprehensive system prompts for autonomous operation.

Related Skills

prompt-driven-development

5
from m31uk3/ai-skills

Transform rough ideas into detailed design documents with implementation plans. Use when a user wants to develop an idea into a complete specification, create a design document from a concept, plan a feature implementation, or mentions "PDD", "prompt-driven development", "idea to design", "design doc from idea", or wants to systematically refine requirements before building. Guides through requirements clarification, research, detailed design, and implementation planning.

Skill Development

5
from m31uk3/ai-skills

This skill should be used when the user wants to "create a skill", "add a skill to plugin", "write a new skill", "improve skill description", "organize skill content", or needs guidance on skill structure, progressive disclosure, or skill development best practices for Claude Code plugins.

Hook Development

5
from m31uk3/ai-skills

This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.

Command Development

5
from m31uk3/ai-skills

This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.

writing-eval-sloptastic

5
from m31uk3/ai-skills

Quantitative framework for detecting AI-generated "slop" in prose through systematic analysis of structural, lexical, rhetorical, and logical patterns. Use when analyzing text authenticity, evaluating writing quality, detecting AI-generated content, or assessing whether prose has characteristic AI patterns like excessive parallelism, abstraction laddering, chiasmus abuse, platitudes, tautologies, or rhetorical overengineering.

validated-knowledge-synthesis

5
from m31uk3/ai-skills

Transform raw, unorganized information into actionable knowledge through systematic validation. Use when users want to synthesize information from multiple sources (documents, URLs, transcripts, notes) into structured knowledge documents. Supports three document types - curated context (default, optimized for recall), guidance (implementation-focused narrative), and reference (quick lookup). Combines convergent synthesis with tension preservation to maintain productive contradictions. Triggers on requests like "synthesize this information", "create knowledge document from these sources", "transform these notes into actionable guidance", or "help me organize this research".

transcribing-youtube

5
from m31uk3/ai-skills

Download and transcribe YouTube videos into clean, deduplicated Markdown documents with chapter headings. Wraps yt-dlp to fetch subtitles (manual or auto-generated), removes the rolling-text triplication artifacts from auto-subs, inserts chapter markers from video metadata, and produces both a timestamped transcript and a prose-only version. Use when the user wants to: (1) transcribe a YouTube video, (2) get a transcript or subtitles from YouTube, (3) create an InfoNugget from a video, (4) extract text from a YouTube URL or video ID, or (5) mentions yt-dlp, YouTube transcript, or video subtitles.

synthesize-knowledge-graph

5
from m31uk3/ai-skills

Transform source materials into K-DAGs (Knowledge DAGs) — modular, curated domain knowledge structured as directed acyclic graphs with typed edges, mermaid visualization, and prose context. Use when users want to build knowledge graphs from documents, synthesize multiple sources into structured ontologies, intersect existing K-DAGs to discover emergent relationships, or create machine-readable knowledge structures that resist context rot. Triggers on: 'build a knowledge graph', 'create a K-DAG', 'intersect these knowledge sources', 'map the relationships between these documents', 'synthesize an ontology from these sources'.

skill-resiliency

5
from m31uk3/ai-skills

This skill should be used when the user asks to "add resiliency to a skill", "make this skill more robust", "improve error handling", "add validation mechanisms", "create self-correcting behavior", or discusses determinism, robustness, error correction, or homeostatic patterns in Agent Skills. Applies biological resiliency principles from Michael Levin's work to Agent Skill design.

codebase-summary

5
from m31uk3/ai-skills

Analyze a codebase and generate comprehensive documentation including architecture, components, interfaces, workflows, and dependencies. Creates an AI-optimized knowledge base (index.md) and can consolidate into AGENTS.md, README.md, or CONTRIBUTING.md. Use when the user wants to document a codebase, create AGENTS.md, understand system architecture, generate developer documentation, or asks to "summarize the codebase".

Plugin Structure

5
from m31uk3/ai-skills

This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture best practices.

Plugin Settings

5
from m31uk3/ai-skills

This skill should be used when the user asks about "plugin settings", "store plugin configuration", "user-configurable plugin", ".local.md files", "plugin state files", "read YAML frontmatter", "per-project plugin settings", or wants to make plugin behavior configurable. Documents the .claude/plugin-name.local.md pattern for storing plugin-specific configuration with YAML frontmatter and markdown content.