agent-development
anthropics/claude-plugins-official
Create autonomous agents for Claude Code plugins with system prompts, triggering conditions, and configuration.
What is agent-development?
This skill guides you through building autonomous agents—subprocesses that handle complex multi-step tasks independently. Use it when creating agents, writing system prompts, defining triggering conditions, or structuring agent files with YAML frontmatter for Claude Code plugins.
- Define agent identifiers, descriptions, models, colors, and tool restrictions via YAML frontmatter
- Write system prompts that specify agent responsibilities, analysis processes, quality standards, and output formats
- Design triggering conditions so agents activate automatically when user requests match specific scenarios
- Organize agents in plugin directories with auto-discovery of .md files
- Validate agent configuration against naming rules, description requirements, and prompt structure guidelines
- Create agents via AI-assisted generation or manual configuration following best-practice templates
How to install agent-development
npx skills add https://github.com/anthropics/claude-plugins-official --skill agent-developmentHow to use agent-development
- 1.Choose an agent identifier (3-50 chars, lowercase, hyphens only)
- 2.Write a description field that specifies triggering conditions and includes 2-4 prose examples of when to invoke
- 3.Select a model (use 'inherit' unless the agent needs specific capabilities like Opus)
- 4.Pick a color (blue/cyan for analysis, green for success tasks, yellow for validation, red for critical work, magenta for generation)
- 5.Optionally restrict tools to a minimal set (e.g., ['Read', 'Write', 'Grep'] for code generation)
- 6.Write the system prompt in the markdown body using second person, including responsibilities, analysis process, quality standards, output format, and edge cases
- 7.Create a 'When to invoke' section with 2-4 detailed prose scenarios showing representative use cases
- 8.Save the file as agents/agent-name.md in your plugin directory
Use cases
- Build a code-reviewer agent that autonomously analyzes pull requests and suggests improvements
- Create a test-generator agent that writes unit tests for new functions without user intervention
- Develop a security-analyzer agent that scans code for vulnerabilities and reports findings
- Set up an api-docs-writer agent that generates documentation from code comments and signatures
- Configure a multi-agent plugin where different colored agents handle analysis, generation, and validation tasks
- Plugin developers building Claude Code extensions
- Teams creating autonomous coding workflows
- Developers designing multi-agent systems for complex tasks
- Anyone building specialized agents for specific domains like testing, security, or documentation
agent-development FAQ
Agents are FOR autonomous work—they run independently to handle multi-step tasks. Commands are FOR user-initiated actions where the user explicitly requests something.
Cover both proactive (agent invokes itself) and reactive (user requests) scenarios. Include 2-4 different phrasings of the same intent, and be specific about when NOT to use the agent. Put detailed examples in the 'When to invoke' section.
Yes, 'inherit' is recommended unless your agent needs specific capabilities. Use 'opus' for complex reasoning, 'sonnet' for balanced performance, or 'haiku' for speed.
Apply principle of least privilege. For read-only analysis use ['Read', 'Grep', 'Glob']. For code generation use ['Read', 'Write', 'Grep']. For testing use ['Read', 'Bash', 'Grep']. Omit the tools field only if full access is necessary.
Aim for 500-3,000 characters. Include clear responsibilities, step-by-step process, quality standards, output format, and edge case handling. Keep it under 10,000 characters total.
Full instructions (SKILL.md)
Source of truth, from anthropics/claude-plugins-official.
name: agent-development description: 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. version: 0.1.0
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-reviewertest-generatorapi-docs-writersecurity-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:
- Triggering conditions ("Use this agent when...")
- A short prose summary of the typical trigger scenarios
- 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
- Choose agent identifier (3-50 chars, lowercase, hyphens)
- Write description with examples
- Select model (usually
inherit) - Choose color for visual identification
- Define tools (if restricting access)
- Write system prompt with structure above
- 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:
- Write agent with specific triggering examples
- Use similar phrasing to examples in test
- Check Claude loads the agent
- Verify agent provides expected functionality
Test System Prompt
Ensure system prompt is complete:
- Give agent typical task
- Check it follows process steps
- Verify output format is correct
- Test edge cases mentioned in prompt
- 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
| Field | Required | Format | Example |
|---|---|---|---|
| name | Yes | lowercase-hyphens | code-reviewer |
| description | Yes | Prose triggers | Use when... Typical triggers include... |
| model | Yes | inherit/sonnet/opus/haiku | inherit |
| color | Yes | Color name | blue |
| tools | No | Array 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
inheritfor 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 patternsreferences/triggering-examples.md- Example formats and best practicesreferences/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 templatecomplete-agent-examples.md- Full agent examples for different use cases
Utility Scripts
Development tools in scripts/:
validate-agent.sh- Validate agent file structuretest-agent-trigger.sh- Test if agent triggers correctly
Implementation Workflow
To create an agent for a plugin:
- Define agent purpose and triggering conditions
- Choose creation method (AI-assisted or manual)
- Create
agents/agent-name.mdfile - Write frontmatter with all required fields
- Write system prompt following best practices
- Name 2-4 trigger scenarios in description (prose) and detail them in a "When to invoke" body section
- Validate with
scripts/validate-agent.sh - Test triggering with real scenarios
- Document agent in plugin README
Focus on clear triggering conditions and comprehensive system prompts for autonomous operation.
Related skills
More from anthropics/claude-plugins-official and the wider catalog.

build-mcp-app
Build interactive UI widgets for MCP servers that render inline in Claude and other chat hosts.

build-mcp-server
Design and build MCP servers for Claude with guided discovery and deployment recommendations.

build-mcpb
Package an MCP server with its runtime into a single .mcpb file for distribution without requiring Node or Python.

cardputer-buddy
Iterate on Cardputer-Adv MicroPython apps after provisioning—push code, watch logs, run REPL commands.

claude-automation-recommender
Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.

claude-md-improver
Audit and improve CLAUDE.md files to optimize Claude Code's project context.