Agent Development
AnthropicOfficialPublished by Anthropic
- Skill
- Guidelines
- Agent tooling
- Apache-2.0
- Low risk
- Has scripts
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/anthropics/agent-development/downloadUnzip 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-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.mdAll .md files in agents/ are auto-discovered.
Namespacing
Agents are namespaced automatically:
Single plugin:
agent-nameWith 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.mdfileWrite 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.shTest triggering with real scenarios
Document agent in plugin README
Focus on clear triggering conditions and comprehensive system prompts for autonomous operation.
Files
- ATTRIBUTION.mdAdded by AutoGineer972 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md10.9 KiB
- examples/agent-creation-prompt.md9.6 KiB
- examples/complete-agent-examples.md13.6 KiB
- references/agent-creation-system-prompt.md9.5 KiB
- references/system-prompt-design.md9.8 KiB
- references/triggering-examples.md8.7 KiB
- scripts/validate-agent.shScript5.7 KiB
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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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