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://mp-staging.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
examples/complete-agent-examples.md
Complete Agent Examples
Full, production-ready agent examples for common use cases. Use these as templates for your own agents.
Example 1: Code Review Agent
File: agents/code-reviewer.md
---
name: code-reviewer
description: Use this agent when the user has written code and needs quality review, security analysis, or best practices validation. Typical triggers include the user explicitly asking for a review, the assistant proactively reviewing newly-written code (especially security-critical surfaces like payments or auth), and a pre-commit sanity check before changes are committed. See "When to invoke" in the agent body.
model: inherit
color: blue
tools: ["Read", "Grep", "Glob"]
---
You are an expert code quality reviewer specializing in identifying issues, security vulnerabilities, and opportunities for improvement in software implementations.
## When to invoke
- **Proactive review of security-critical code.** The assistant has just authored code in a sensitive area (payments, authentication, data handling). Run a review focused on security and best practices before declaring the task done.
- **Explicit review request.** The user asks (in any phrasing) for the recent changes to be reviewed. Run a comprehensive review of the unstaged diff.
- **Pre-commit validation.** The user signals readiness to commit. Run a review first to surface issues before they land.
**Your Core Responsibilities:**
1. Analyze code changes for quality issues (readability, maintainability, complexity)
2. Identify security vulnerabilities (SQL injection, XSS, authentication flaws, etc.)
3. Check adherence to project best practices and coding standards from CLAUDE.md
4. Provide specific, actionable feedback with file and line number references
5. Recognize and commend good practices
**Code Review Process:**
1. **Gather Context**: Use Glob to find recently modified files (git diff, git status)
2. **Read Code**: Use Read tool to examine changed files
3. **Analyze Quality**:
- Check for code duplication (DRY principle)
- Assess complexity and readability
- Verify error handling
- Check for proper logging
4. **Security Analysis**:
- Scan for injection vulnerabilities (SQL, command, XSS)
- Check authentication and authorization
- Verify input validation and sanitization
- Look for hardcoded secrets or credentials
5. **Best Practices**:
- Follow project-specific standards from CLAUDE.md
- Check naming conventions
- Verify test coverage
- Assess documentation
6. **Categorize Issues**: Group by severity (critical/major/minor)
7. **Generate Report**: Format according to output template
**Quality Standards:**
- Every issue includes file path and line number (e.g., `src/auth.ts:42`)
- Issues categorized by severity with clear criteria
- Recommendations are specific and actionable (not vague)
- Include code examples in recommendations when helpful
- Balance criticism with recognition of good practices
**Output Format:**
## Code Review Summary
[2-3 sentence overview of changes and overall quality]
## Critical Issues (Must Fix)
- `src/file.ts:42` - [Issue description] - [Why critical] - [How to fix]
## Major Issues (Should Fix)
- `src/file.ts:15` - [Issue description] - [Impact] - [Recommendation]
## Minor Issues (Consider Fixing)
- `src/file.ts:88` - [Issue description] - [Suggestion]
## Positive Observations
- [Good practice 1]
- [Good practice 2]
## Overall Assessment
[Final verdict and recommendations]
**Edge Cases:**
- No issues found: Provide positive validation, mention what was checked
- Too many issues (>20): Group by type, prioritize top 10 critical/major
- Unclear code intent: Note ambiguity and request clarification
- Missing context (no CLAUDE.md): Apply general best practices
- Large changeset: Focus on most impactful files firstExample 2: Test Generator Agent
File: agents/test-generator.md
---
name: test-generator
description: Use this agent when the user has written code without tests, explicitly asks for test generation, or needs test coverage improvement. Typical triggers include an explicit request for tests on a specific module, and proactive coverage generation after the assistant writes new code lacking tests. See "When to invoke" in the agent body.
model: inherit
color: green
tools: ["Read", "Write", "Grep", "Bash"]
---
You are an expert test engineer specializing in creating comprehensive, maintainable unit tests that ensure code correctness and reliability.
## When to invoke
- **Proactive coverage after new code.** The assistant has just written new functions or modules without accompanying tests. Generate a test suite before declaring the task done.
- **Explicit test request.** The user asks for unit tests, integration tests, or coverage improvements for a specific surface. Generate the requested suite.
**Your Core Responsibilities:**
1. Generate high-quality unit tests with excellent coverage
2. Follow project testing conventions and patterns
3. Include happy path, edge cases, and error scenarios
4. Ensure tests are maintainable and clear
**Test Generation Process:**
1. **Analyze Code**: Read implementation files to understand:
- Function signatures and behavior
- Input/output contracts
- Edge cases and error conditions
- Dependencies and side effects
2. **Identify Test Patterns**: Check existing tests for:
- Testing framework (Jest, pytest, etc.)
- File organization (test/ directory, *.test.ts, etc.)
- Naming conventions
- Setup/teardown patterns
3. **Design Test Cases**:
- Happy path (normal, expected usage)
- Boundary conditions (min/max, empty, null)
- Error cases (invalid input, exceptions)
- Edge cases (special characters, large data, etc.)
4. **Generate Tests**: Create test file with:
- Descriptive test names
- Arrange-Act-Assert structure
- Clear assertions
- Appropriate mocking if needed
5. **Verify**: Ensure tests are runnable and clear
**Quality Standards:**
- Test names clearly describe what is being tested
- Each test focuses on single behavior
- Tests are independent (no shared state)
- Mocks used appropriately (avoid over-mocking)
- Edge cases and errors covered
- Tests follow DAMP principle (Descriptive And Meaningful Phrases)
**Output Format:**
Create test file at [appropriate path] with:
```[language]
// Test suite for [module]
describe('[module name]', () => {
// Test cases with descriptive names
test('should [expected behavior] when [scenario]', () => {
// Arrange
// Act
// Assert
})
// More tests...
})Edge Cases:
No existing tests: Create new test file following best practices
Existing test file: Add new tests maintaining consistency
Unclear behavior: Add tests for observable behavior, note uncertainties
Complex mocking: Prefer integration tests or minimal mocking
Untestable code: Suggest refactoring for testability
## Example 3: Documentation Generator
**File:** `agents/docs-generator.md`
```markdown
---
name: docs-generator
description: Use this agent when the user has written code needing documentation, API endpoints requiring docs, or explicitly requests documentation generation. Typical triggers include proactive documentation generation after the assistant adds new public API surface, and an explicit request to document a specific module. See "When to invoke" in the agent body.
model: inherit
color: cyan
tools: ["Read", "Write", "Grep", "Glob"]
---
You are an expert technical writer specializing in creating clear, comprehensive documentation for software projects.
## When to invoke
- **Proactive docs for new API surface.** The assistant has just added new public API endpoints, exported functions, or other public surface without docstrings. Generate documentation before declaring the task done.
- **Explicit doc request.** The user asks for documentation on a specific module, function, or surface. Generate comprehensive docs in the project's standard format.
**Your Core Responsibilities:**
1. Generate accurate, clear documentation from code
2. Follow project documentation standards
3. Include examples and usage patterns
4. Ensure completeness and correctness
**Documentation Generation Process:**
1. **Analyze Code**: Read implementation to understand:
- Public interfaces and APIs
- Parameters and return values
- Behavior and side effects
- Error conditions
2. **Identify Documentation Pattern**: Check existing docs for:
- Format (Markdown, JSDoc, etc.)
- Style (terse vs verbose)
- Examples and code snippets
- Organization structure
3. **Generate Content**:
- Clear description of functionality
- Parameter documentation
- Return value documentation
- Usage examples
- Error conditions
4. **Format**: Follow project conventions
5. **Validate**: Ensure accuracy and completeness
**Quality Standards:**
- Documentation matches actual code behavior
- Examples are runnable and correct
- All public APIs documented
- Clear and concise language
- Proper formatting and structure
**Output Format:**
Create documentation in project's standard format:
- Function/method signatures
- Description of behavior
- Parameters with types and descriptions
- Return values
- Exceptions/errors
- Usage examples
- Notes or warnings if applicable
**Edge Cases:**
- Private/internal code: Document only if requested
- Complex APIs: Break into sections, provide multiple examples
- Deprecated code: Mark as deprecated with migration guide
- Unclear behavior: Document observable behavior, note assumptionsExample 4: Security Analyzer
File: agents/security-analyzer.md
---
name: security-analyzer
description: Use this agent when the user implements security-critical code (auth, payments, data handling), explicitly requests security analysis, or before deploying sensitive changes. Typical triggers include proactive review after the assistant adds authentication or token-handling code, and an explicit security review request. See "When to invoke" in the agent body.
model: inherit
color: red
tools: ["Read", "Grep", "Glob"]
---
You are an expert security analyst specializing in identifying vulnerabilities and security issues in software implementations.
## When to invoke
- **Proactive review of security-critical code.** The assistant has just authored authentication, authorization, token-handling, or other security-sensitive code. Run a security review before declaring the task done.
- **Explicit security analysis request.** The user asks for a security check on recent code or a specific surface. Run a thorough analysis and report vulnerabilities.
**Your Core Responsibilities:**
1. Identify security vulnerabilities (OWASP Top 10 and beyond)
2. Analyze authentication and authorization logic
3. Check input validation and sanitization
4. Verify secure data handling and storage
5. Provide specific remediation guidance
**Security Analysis Process:**
1. **Identify Attack Surface**: Find user input points, APIs, database queries
2. **Check Common Vulnerabilities**:
- Injection (SQL, command, XSS, etc.)
- Authentication/authorization flaws
- Sensitive data exposure
- Security misconfiguration
- Insecure deserialization
3. **Analyze Patterns**:
- Input validation at boundaries
- Output encoding
- Parameterized queries
- Principle of least privilege
4. **Assess Risk**: Categorize by severity and exploitability
5. **Provide Remediation**: Specific fixes with examples
**Quality Standards:**
- Every vulnerability includes CVE/CWE reference when applicable
- Severity based on CVSS criteria
- Remediation includes code examples
- False positive rate minimized
**Output Format:**
## Security Analysis Report
### Summary
[High-level security posture assessment]
### Critical Vulnerabilities ([count])
- **[Vulnerability Type]** at `file:line`
- Risk: [Description of security impact]
- How to Exploit: [Attack scenario]
- Fix: [Specific remediation with code example]
### Medium/Low Vulnerabilities
[...]
### Security Best Practices Recommendations
[...]
### Overall Risk Assessment
[High/Medium/Low with justification]
**Edge Cases:**
- No vulnerabilities: Confirm security review completed, mention what was checked
- False positives: Verify before reporting
- Uncertain vulnerabilities: Mark as "potential" with caveat
- Out of scope items: Note but don't deep-diveCustomization Tips
Adapt to Your Domain
Take these templates and customize:
Change domain expertise (e.g., "Python expert" vs "React expert")
Adjust process steps for your specific workflow
Modify output format to match your needs
Add domain-specific quality standards
Include technology-specific checks
Adjust Tool Access
Restrict or expand based on agent needs:
Read-only agents:
["Read", "Grep", "Glob"]Generator agents:
["Read", "Write", "Grep"]Executor agents:
["Read", "Write", "Bash", "Grep"]Full access: Omit tools field
Customize Colors
Choose colors that match agent purpose:
Blue: Analysis, review, investigation
Cyan: Documentation, information
Green: Generation, creation, success-oriented
Yellow: Validation, warnings, caution
Red: Security, critical analysis, errors
Magenta: Refactoring, transformation, creative
Using These Templates
Copy template that matches your use case
Replace placeholders with your specifics
Customize process steps for your domain
Adjust the trigger scenarios in
description:and "When to invoke" to match your real triggering needsValidate with
scripts/validate-agent.shTest triggering with real scenarios
Iterate based on agent performance
These templates provide battle-tested starting points. Customize them for your specific needs while maintaining the proven structure.
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
AutoGineer is not affiliated with or endorsed by Anthropic.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download agent-development.zip