Agent skill

creating-subagents

Use when need to create specialized subagent for recurring tasks, domain-specific work, or pipeline automation

Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/creating-subagents

SKILL.md

Creating Subagents

Subagents are specialized AI instances with focused expertise, isolated context, and scoped tool access.

When to Create

  • Task repeats across projects (code review, security audit)
  • Domain expertise needed (data science, API design)
  • Pipeline step requires isolation (PM → Architect → Implementer)
  • Tool access must be restricted

Skip if: one-off task, no specialization needed.

Quick Reference

Field Required Purpose
name Yes Lowercase, hyphens only
description Yes When to delegate (triggers)
tools No Allowlist; omit = inherit all
disallowedTools No Denylist; removed from inherited
permissionMode No default, acceptEdits, plan, etc.
skills No Preload skill content into context
hooks No Lifecycle hooks (PreToolUse, Stop)
color No Background color for UI identification

Design Process

1. Define the Problem

Before writing code, answer:

  • What task does this agent solve?
  • What does success look like?
  • What tools are absolutely required?
  • What should the agent NOT do?

2. Write Description First

Description determines when agent is invoked. Write it before the prompt.

Pattern: [Role]. [Trigger condition]. Use proactively [when].

yaml
# ❌ BAD: Vague, no trigger
description: Helps with code

# ❌ BAD: Describes process, not trigger
description: Reviews code and provides feedback

# ✅ GOOD: Clear role + trigger + proactive hint
description: Code quality reviewer. Use proactively after code changes.

3. Minimal Viable Prompt

Start with the smallest prompt that works:

markdown
---
name: code-reviewer
description: Code quality reviewer. Use proactively after code changes.
tools: Read, Glob, Grep, Bash
color: blue
---

You are a code reviewer. When invoked:

1. Run git diff to see changes
2. Review modified files
3. Report: Critical → Warnings → Suggestions

Include fix examples.

4. Test Before Deploy

Run the agent on real tasks. Document:

  • Did it activate when expected?
  • Did it have the right tools?
  • Was output useful and structured?
  • Did it stay in scope?

5. Iterate Based on Failures

When agent fails, identify the gap:

Symptom Fix
Doesn't activate Improve description triggers
Wrong scope Add constraints to prompt
Missing context Add required tools or skills
Unstructured output Define output format in prompt
Does too much Split into focused agents

Tool Access by Role

Role Tools
Read-only Read, Grep, Glob
Research Read, Grep, Glob, WebFetch, WebSearch
Code modification Read, Write, Edit, Bash, Glob, Grep
Full access (omit field — inherits all)

Common Mistakes

Mistake Fix
Vague description Specific triggers: "after code changes"
Tool inheritance by default Explicit tools field for security
No structured output Define format in prompt
Monolithic mega-agent Split into single-purpose agents
Missing "Use proactively" Add to description for auto-delegation
Prompt before description Write description first — it's the API

Creation Checklist

  • Problem clearly defined (what, success criteria, non-goals)
  • Description written first with trigger conditions
  • Tools explicitly listed (not inherited)
  • Prompt is minimal and focused
  • Output format defined
  • Tested on real task
  • Iterated based on failures

Pipeline Pattern

Chain agents for multi-stage workflows:

pm-spec → architect-review → implementer-tester

Each agent:

  • Has single responsibility
  • Updates status on completion
  • Hooks trigger next stage

Didn't find tool you were looking for?

Be as detailed as possible for better results