Create Subagent

Creating Custom Subagents

This skill guides you through creating custom subagents for this platform. Subagents are specialized AI assistants that run in isolated contexts with custom system prompts.

When to Use Subagents

Subagents help you:

• Preserve context by isolating exploration from your main conversation
• Specialize behavior with focused system prompts for specific domains
• Reuse configurations across projects with user-level subagents

Inferring from Context

If you have previous conversation context, infer the subagent's purpose and behavior from what was discussed. Create the subagent based on specialized tasks or workflows that emerged in the conversation.

Subagent Locations

When multiple subagents share the same name, the higher-priority location wins.

Project subagents (workspace-level): Ideal for codebase-specific agents. Check into version control to share with your team.

User subagents (user-level): Personal agents available across all your projects.

Subagent File Format

Create a .md file with YAML frontmatter and a markdown body (the system prompt):

---
name: code-reviewer
description: Reviews code for quality and best practices
---

You are a code reviewer. When invoked, analyze the code and provide
specific, actionable feedback on quality, security, and best practices.

Required Fields

Writing Effective Descriptions

The description is critical - the AI uses it to decide when to delegate.

# ❌ Too vague
description: Helps with code

# ✅ Specific and actionable
description: Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code.

Include "use proactively" to encourage automatic delegation.

Example Subagents

Code Reviewer

---
name: code-reviewer
description: Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code.
---

You are a senior code reviewer ensuring high standards of code quality and security.

When invoked:
1. Run git diff to see recent changes
2. Focus on modified files
3. Begin review immediately

Review checklist:
- Code is clear and readable
- Functions and variables are well-named
- No duplicated code
- Proper error handling
- No exposed secrets or API keys
- Input validation implemented
- Good test coverage
- Performance considerations addressed

Provide feedback organized by priority:
- Critical issues (must fix)
- Warnings (should fix)
- Suggestions (consider improving)

Include specific examples of how to fix issues.

Debugger

---
name: debugger
description: Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.
---

You are an expert debugger specializing in root cause analysis.

When invoked:
1. Capture error message and stack trace
2. Identify reproduction steps
3. Isolate the failure location
4. Implement minimal fix
5. Verify solution works

Debugging process:
- Analyze error messages and logs
- Check recent code changes
- Form and test hypotheses
- Add strategic debug logging
- Inspect variable states

For each issue, provide:
- Root cause explanation
- Evidence supporting the diagnosis
- Specific code fix
- Testing approach
- Prevention recommendations

Focus on fixing the underlying issue, not the symptoms.

Data Scientist

---
name: data-scientist
description: Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.
---

You are a data scientist specializing in SQL and BigQuery analysis.

When invoked:
1. Understand the data analysis requirement
2. Write efficient SQL queries
3. Use BigQuery command line tools (bq) when appropriate
4. Analyze and summarize results
5. Present findings clearly

Key practices:
- Write optimized SQL queries with proper filters
- Use appropriate aggregations and joins
- Include comments explaining complex logic
- Format results for readability
- Provide data-driven recommendations

For each analysis:
- Explain the query approach
- Document any assumptions
- Highlight key findings
- Suggest next steps based on data

Always ensure queries are efficient and cost-effective.

Subagent Creation Workflow

Step 1: Decide the Scope

• Project-level (workspace agents directory): For codebase-specific agents shared with team
• User-level (user agents directory): For personal agents across all projects

Step 2: Create the File

This repo uses platform-specific agents directories. Use this platform's project agents directory so agents are discovered correctly.

# For project-level (this platform)
mkdir -p .claude/agents
touch .claude/agents/my-agent.md

# For user-level (platform-specific user directory)
mkdir -p ~/.claude/agents
touch ~/.claude/agents/my-agent.md

Step 3: Define Configuration

Write the frontmatter with the required fields (name and description).

Step 4: Write the System Prompt

The body becomes the system prompt. Be specific about:

• What the agent should do when invoked
• The workflow or process to follow
• Output format and structure
• Any constraints or guidelines

Step 5: Test the Agent

Ask the AI to use your new agent:

Use the my-agent subagent to [task description]

Best Practices

1. Design focused subagents: Each should excel at one specific task
2. Write detailed descriptions: Include trigger terms so the AI knows when to delegate
3. Check into version control: Share project subagents with your team
4. Use proactive language: Include "use proactively" in descriptions

Troubleshooting

Subagent Not Found

• Ensure file is in the agents directory (workspace or user-level)
• Check file has .md extension
• Verify YAML frontmatter syntax is valid