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Agent Development

AnthropicOfficialPublished by Anthropic

  • Skill
  • Guidelines
  • Agent tooling
  • Apache-2.0
  • Low risk
  • Has scripts
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From the author

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.
— the description by Anthropic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides the framework and best practices for developing autonomous agents within Claude Code. It covers the complete agent lifecycle, including file structure, YAML frontmatter configuration, system prompt design, and testing procedures. It emphasizes creating agents that are modular, well-defined, and capable of handling multi-step tasks independently through clear triggering conditions and robust behavioral instructions.

What it does

  • Defines the required markdown and YAML frontmatter structure for agent files.
  • Provides patterns for writing effective system prompts in the second person.
  • Outlines best practices for defining triggering conditions and scenarios.
  • Offers guidance on model selection, color-coding for UI, and tool access control.
  • Includes templates and prompts for AI-assisted agent generation.
  • Provides validation and testing workflows to ensure agent reliability.

When to use it

  • When you need to create a new autonomous agent for a Claude Code plugin.
  • When you are refining an existing agent's system prompt or triggering logic.
  • When you need to ensure your agent configuration follows established structural standards.
  • When you are troubleshooting why an agent is not triggering as expected.

Advantages

  • Standardizes agent development across different plugins.
  • Improves agent reliability through clear triggering scenarios.
  • Promotes the principle of least privilege via tool restriction.
  • Facilitates consistent behavior through structured system prompt templates.

Usage examples

“Create an agent configuration for a security-analyzer that checks code for vulnerabilities.”
The agent will generate a JSON object with a unique identifier, a description with prose triggers, and a system prompt structured with responsibilities, analysis processes, and quality standards.
“How should I structure the system prompt for a new documentation-writer agent?”
The agent will provide a template including core responsibilities, a generation process, quality standards, output format, and edge case handling.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/anthropics/agent-development/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip agent-development.zip -d .agents/skills

Documentation by Anthropic

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

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
---
name: simple-agent
description: Use this agent when [condition]. Typical triggers include [trigger 1] and [trigger 2]. See "When to invoke" in the agent body.
model: inherit
color: blue
---

You are an agent that [does X].

## When to invoke

- **[Scenario A].** [Description.]
- **[Scenario B].** [Description.]

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

Output: [What to provide]
Frontmatter Fields Summary
FieldRequiredFormatExample
nameYeslowercase-hyphenscode-reviewer
descriptionYesProse triggersUse when... Typical triggers include...
modelYesinherit/sonnet/opus/haikuinherit
colorYesColor nameblue
toolsNoArray of tool names["Read", "Grep"]
Best Practices

DO:

  • ✅ Name 2-4 trigger scenarios in the description (as prose)

  • ✅ Put detailed worked scenarios in a "When to invoke" body section, as prose bullets

  • ✅ 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 trigger scenarios

  • ❌ 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. Name 2-4 trigger scenarios in description (prose) and detail them in a "When to invoke" body section

  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.

Files

examples/agent-creation-prompt.md

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... Typical triggers include [scenario 1], [scenario 2], and [scenario 3]. See \"When to invoke\" in the agent body for worked scenarios.",
  "systemPrompt": "You are...\n\n## When to invoke\n\n- **[Scenario A].** [Description]\n- **[Scenario B].** [Description]\n\n**Your Core Responsibilities:**..."
}

whenToUse is flat prose. systemPrompt includes a "When to invoke" section with prose bullets.

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. Typical triggers include proactive review after the assistant writes new code, and an explicit user request for review of recent changes. See \"When to invoke\" in the agent body for worked scenarios.",
  "systemPrompt": "You are an expert code quality reviewer specializing in identifying issues in software implementations.\n\n## When to invoke\n\n- **Proactive review after new code.** The assistant has just written or modified code (e.g. an authentication feature). Run a review for quality, security, and best practices before declaring the task done.\n- **Explicit review request.** The user asks for the recent changes to be reviewed for issues. Run a thorough review and report findings.\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. Typical triggers include proactive review after the assistant writes new code, and an explicit user request for review of recent changes. See "When to invoke" in the agent body for worked scenarios.
model: inherit
color: blue
tools: ["Read", "Grep", "Glob"]
---

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

## When to invoke

- **Proactive review after new code.** The assistant has just written or modified code (e.g. an authentication feature). Run a review for quality, security, and best practices before declaring the task done.
- **Explicit review request.** The user asks for the recent changes to be reviewed for issues. Run a thorough review and report findings.

**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. Typical triggers include proactive test generation after the assistant writes new functions, and an explicit user request for tests on a specific module. See \"When to invoke\" in the agent body.",
  "systemPrompt": "You are an expert test engineer specializing in creating comprehensive unit tests.\n\n## When to invoke\n\n- **Proactive coverage after new code.** The assistant has just implemented new functions (e.g. user authentication functions) without tests. Generate a comprehensive test suite before declaring the task done.\n- **Explicit test request.** The user asks for tests on a specific surface. Generate the requested suite following project conventions.\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, prose-style trigger description, and a "When to invoke" body section covering proactive doc generation after new API surface and explicit doc requests.

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 realistic invocation phrasings

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-named trigger scenarios in description: and "When to invoke"

    • 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

  • 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.

Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Anthropic
Publisher
Anthropic (Official)
Source
anthropics/claude-plugins-official · plugins/plugin-dev/skills/agent-development · commit 0412be7
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from plugins/plugin-dev/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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