refactor-agent

name	refactor-agent
description	Restructure code to improve readability, maintainability, and extensibility without changing external behavior. Use when reducing complexity, extracting reusable components, splitting monoliths, or modernizing legacy code.
domain	agents
tags	["agent","ai-agent","automation","orchestration","refactor","rest-api"]

Refactor Agent

When to Use

Trigger phrases:

• "refactor agent"

• "Reducing complexity in a function or module (cyclomatic complexity >10)"

• "Extracting reusable components from duplicated code"

• "Applying design patterns where they reduce coupling"

• Reducing complexity in a function or module (cyclomatic complexity >10)

• Extracting reusable components from duplicated code

• Applying design patterns where they reduce coupling

• Splitting monolithic files into focused modules

• Modernizing legacy code (var to const, callbacks to async/await)

• Removing dead code and unused dependencies

• Preparing code for new features (make the change easy first)

• Improving testability (dependency injection, pure functions)

When NOT to Use

• When the task is simple enough for a single command
• When real-time human judgment is required
• When the agent lacks access to required tools or data

Overview

Refactor Agent is an AI agent skill for agent orchestration. It enables autonomous execution of complex tasks with minimal human intervention.

Capabilities

• Autonomous operation — Execute multi-step refactor agent workflows independently
• Context awareness — Adapt behavior based on current state and history
• Error recovery — Handle failures gracefully with retry and fallback logic
• Integration — Connect with external tools and services as needed

Workflow

# Example: Agent orchestration
from dataclasses import dataclass

@dataclass
class Task:
    name: str
    priority: int
    assigned_agent: str

def orchestrate(tasks: list[Task]) -> dict:
    results = {}
    for task in sorted(tasks, key=lambda t: t.priority):
        results[task.name] = execute(task)
    return results

1. Initialize — Set up the agent context and load required resources
2. Plan — Break down the task into executable steps
3. Execute — Run each step, monitoring for errors and adapting as needed
4. Verify — Validate results against acceptance criteria
5. Report — Summarize outcomes and suggest next steps

Configuration

• Define task objectives and constraints clearly
• Set appropriate timeout and retry limits
• Configure tool access and permissions
• Enable logging for debugging and audit

Anti-Rationalization

Rationalization	Reality
"I will just do it manually"	Agents automate repetitive tasks — manual work does not scale
"The agent will figure it out"	Without clear instructions, agents hallucinate. Give explicit context.
"One agent is enough"	Complex tasks benefit from specialized agents working in parallel

Process

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run refactor agent workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification

• All steps executed successfully
• Results validated against acceptance criteria
• Error handling tested with edge cases
• Documentation updated with findings