name: creating-subagents description: Guide for creating effective subagents with concise prompts and skill integration. This skill should be used when users want to create a new subagent, improve existing subagent definitions, or need guidance on subagent architecture. Triggers on "create subagent", "create agent", "new subagent", "improve agent", or "subagent definition".

Creating Subagents

Create specialized, concise subagents that leverage existing skills for task-specific workflows.

When to Use

• Creating new subagents for specialized tasks
• Improving or refactoring existing subagent definitions
• Validating subagent architecture and design
• Determining optimal tool access for subagents

Core Principles

1. Concise and Focused

Subagent prompts should be short and directive - typically 50-200 words. Avoid lengthy explanations.

Why: Subagents operate in separate contexts. Verbose prompts waste tokens and reduce effectiveness.

2. Leverage Skills, Not Prompts

When subagents need specialized knowledge, create or reference skills instead of embedding instructions in the prompt.

Why: Skills are reusable, maintainable, and can include bundled resources. Prompts should orchestrate, not educate.

3. Single Responsibility

Each subagent should handle one clear domain with minimal scope creep.

Why: Focused subagents are more predictable and easier to maintain.

Creation Workflow

Step 1: Define Purpose

Clearly articulate what the subagent will do:

• What specific task domain does it handle?
• When should it be invoked (proactively or on-demand)?
• What makes this worthy of a separate subagent?

Step 2: Skill Validation

Critical step: Before writing the subagent prompt, determine if a skill should be created.

Create a skill when:

• Subagent needs procedural knowledge (multi-step workflows)
• Domain requires reference material (schemas, APIs, templates)
• Task involves reusable assets (scripts, boilerplate code)
• Complexity exceeds ~100 words of instructions

Use prompt-only when:

• Task is simple orchestration with existing tools
• Behavior is purely about tool usage patterns
• No specialized domain knowledge required

If skill needed: Create the skill first using Skill(command='creating-skills'), then reference it in the subagent with Skill() invocations.

Step 3: Tool Selection

Grant minimal necessary tools. Omit the tools field only if all tools are genuinely needed.

Common tool patterns:

• Read-only research: Read, Grep, Glob
• Code modification: Read, Edit, Grep, Glob, Bash
• Testing: Bash, Read, Grep
• Documentation: Read, Write, Grep, Glob

Step 4: Choose Color

Select an appropriate color from the color coding system (see references/color-codes.md):

• purple: Review/audit agents
• blue: Implementation/development agents
• green: Testing/validation agents
• yellow: Documentation agents
• orange: Maintenance/refactoring agents
• red: Debugging/troubleshooting agents
• cyan: Research/analysis agents
• magenta: Deployment/operations agents
• white: General purpose agents

Choose based on the agent's primary purpose.

Step 5: Craft the Prompt

Use the Skill(command='crafting-agentic-prompts') for prompt engineering guidance.

Structure:

---
name: subagent-name
description: When to invoke this subagent (proactive triggers)
tools: Tool1, Tool2, Tool3
model: inherit  # or sonnet/opus/haiku
color: blue  # See references/color-codes.md for color system
---

[Role: 1 sentence]

[Core instructions: 3-5 directive statements]

[Constraints or workflow if needed: 2-3 lines]

[Skill references: Skill(command='skill-name') if applicable]

Example of concise prompt:

---
name: api-tester
description: Test API endpoints and validate responses. Use proactively after API implementation or when endpoints mentioned.
tools: Bash, Read, Grep
model: inherit
color: green
---

Test API endpoints systematically using curl or HTTPie.

Verify:
- Response status codes match expectations
- Response schemas are valid
- Error handling works correctly
- Authentication flows properly

Invoke Skill(command='api-validation') for schema validation patterns.

Step 6: Location Decision

Choose storage location:

• Project: .claude/agents/ - Task-specific to this codebase
• User: ~/.claude/agents/ - Reusable across projects

Step 7: Create and Test

Create the agent file:

# For project agents
mkdir -p .claude/agents
# Write agent definition to .claude/agents/agent-name.md

# For user agents
mkdir -p ~/.claude/agents
# Write agent definition to ~/.claude/agents/agent-name.md

Testing approach:

1. Invoke explicitly: "Use the [name] subagent to..."
2. Test proactive triggering with relevant prompts
3. Verify tool access is sufficient
4. Confirm prompt conciseness delivers results

Step 8: Iterate

After testing:

• Remove unnecessary instructions
• Add missing constraints if behavior drifts
• Update skill references if domain knowledge grows
• Adjust tool access based on actual usage

Skill Integration

When subagents need specialized knowledge:

Invoke skills explicitly:

Invoke Skill(command='crafting-agentic-prompts') for prompt optimization.
Invoke Skill(command='api-security') for authentication patterns.
Invoke Skill(command='database-schema') for table relationships.

Benefits:

• Skills load on-demand (progressive disclosure)
• Prompts stay lean
• Knowledge stays maintainable
• Skills are reusable across agents

Anti-Patterns

Avoid:

• ❌ Lengthy procedural instructions (create a skill instead)
• ❌ Embedding reference material in prompts (use references/)
• ❌ Granting all tools by default (be selective)
• ❌ Creating subagents for one-off tasks (use main thread)
• ❌ Duplicate knowledge across agent and skill

Prefer:

• ✅ Short, directive prompts (50-200 words)
• ✅ Skill references for complex workflows
• ✅ Minimal tool access
• ✅ Clear invocation patterns
• ✅ Single source of truth for knowledge

Validation Checklist

Before finalizing the subagent:

• Prompt is under 200 words
• Description clearly states when to invoke
• Tool list is minimal and justified
• Color chosen based on primary purpose (see references/color-codes.md)
• Skills created/referenced for complex knowledge
• Tested with both explicit and proactive invocation
• No redundant instructions in both prompt and skills
• Model selection appropriate for task complexity
• Validated with scripts/validate_subagent.py
• Quality reviewed against references/subagent-quality-review.md criteria

Examples

See references/subagent-examples.md for complete examples demonstrating skill integration patterns.

Resources

references/

color-codes.md - Comprehensive color coding system for categorizing subagents by purpose. Use this to select appropriate colors for subagents.

subagent-examples.md - Collection of well-designed subagents showing various patterns, skill integration approaches, and color coding.

subagent-quality-review.md - Comprehensive quality review framework with automated validation, scoring rubrics, and approval criteria.