I Built a Structured Plan Implement Review Workflow for Claude Code

I've been using Claude Code for larger development tasks, and while it works really well for individual coding tasks, I started noticing a problem when working on more complex features.

A larger request usually involves several different things:

• Understanding the existing codebase
• Figuring out the requirements
• Creating an implementation plan
• Breaking the work into smaller tasks
• Writing the code
• Reviewing the implementation
• Fixing issues discovered during review

When all of this happens in a single agent session, it can become difficult to keep the work structured and predictable.

That led me to experiment with a different approach.

The Workflow I Wanted

Instead of:

Request
   ↓
Claude
   ↓
Code
   ↓
Done

I wanted something closer to:

Request
   ↓
Plan
   ↓
Specs
   ↓
Tasks
   ↓
Implementation
   ↓
Review
   ↓
Fixes
   ↓
Done

The idea is simple: separate planning, implementation, and review instead of treating the entire development task as one operation.

Introducing Taskify

To experiment with this workflow, I built Taskify, an open-source Claude Code plugin.

sheikhfahad67 / taskify

This is claude code plugin helps to cretae the plans and specs

Taskify

A Claude Code plugin with two skills that work as a pair:

• taskify turns a piece of work into a plan (what and why) and a set of executable task specs (do this, prove it). Each spec has preconditions, acceptance criteria with real commands and a block where the real output gets pasted as evidence.
• taskify-implementer runs those specs, one task at a time. Each task is built by one agent and reviewed by a different one. Progress is saved after every step, so a stopped or crashed run can continue where it left off.

Install

From the fahad-marketplace marketplace:

/plugin marketplace add sheikhfahad67/fahad-marketplace
/plugin install taskify@fahad-marketplace

Needed for review: the reviewer is feature-dev:code-reviewer, from the feature-dev plugin. Install it too:

/plugin install feature-dev@claude-plugins-official

Usage

1. Write the plan and specs

/taskify:taskify add rate limiting to the public API

Flag	Effect
--slug <name>	Name of the output folder

…

View on GitHub

Taskify organizes a larger development request into smaller stages and executable tasks.

The workflow starts by analyzing the request and creating an implementation plan. That plan is then converted into smaller tasks that can be implemented incrementally.

After implementation, the changes go through a separate review stage. If issues are found, they can be addressed before moving on.

It also keeps track of progress so that work can be resumed instead of starting the entire process again.

See It in Action

The basic workflow looks like this:

┌─────────┐
│ Request │
└────┬────┘
     ↓
┌─────────┐
│  Plan   │
└────┬────┘
     ↓
┌─────────┐
│  Specs  │
└────┬────┘
     ↓
┌─────────┐
│  Tasks  │
└────┬────┘
     ↓
┌──────────────┐
│ Implementation│
└──────┬───────┘
       ↓
┌─────────┐
│ Review  │
└────┬────┘
     ↓
┌─────────┐
│  Fixes  │
└────┬────┘
     │
     └──────────────→ Review

The important part is that implementation isn't treated as the final step.

There is an explicit review stage before considering the work complete.

Why Separate the Review?

One thing I wanted to experiment with was separating implementation from review.

An agent that just implemented a feature may have a different perspective when reviewing the result later.

So instead of:

Implement → Done

the workflow becomes:

Implement
    ↓
Review
    ↓
Issues?
 ┌──┴──┐
No    Yes
 ↓      ↓
Done   Fix
        ↓
      Review

This doesn't guarantee that the implementation is correct.

But it gives the development process another explicit checkpoint.

What Taskify Currently Does

Taskify currently focuses on:

• Creating implementation plans
• Generating specifications
• Breaking work into executable tasks
• Implementing tasks incrementally
• Reviewing completed work
• Fixing issues found during review
• Tracking progress
• Resuming work from where it was left off

The goal isn't simply to add more AI to the development process.

The goal is to make the development process around AI coding agents more structured.

Why I Think This Can Help With Larger Tasks

Consider a small request:

Add a button to this component.

A complete planning and review workflow would probably be unnecessary.

But consider a larger request:

Add role-based permissions across the application, update the database schema, modify the APIs, update the frontend, and add tests.

Now there are multiple areas of the codebase involved.

There are dependencies between tasks.

There are implementation decisions to make.

And there are more opportunities for something to be missed.

This is where I think an explicit workflow can become useful:

Large Request
     ↓
Understand the Codebase
     ↓
Create Plan
     ↓
Break Into Tasks
     ↓
Implement Incrementally
     ↓
Review
     ↓
Fix

Instead of trying to solve everything in one large operation, the work becomes a sequence of smaller, trackable steps.

The Trade-Off

I don't think this approach is better for every coding task.

More structure also means more overhead.

It can mean:

• More agent calls
• More context
• More processing time
• More steps for simple tasks

For a small change, that overhead may not be worth it.

So one of the things I'm trying to understand with Taskify is:

At what point does structured agent orchestration become more useful than simply letting Claude Code handle the entire task in one session?

I don't have a definitive answer yet.

That's part of why I built it.

What I'm Still Exploring

Taskify is still an experiment and I'm continuing to improve the workflow.

Some of the areas I'm interested in exploring further are:

• Better task decomposition
• More reliable implementation planning
• Improving the review process
• Handling larger multi-step changes
• Better progress and resume capabilities
• Reducing unnecessary agent calls
• Finding the right balance between automation and developer control

The interesting part isn't just making an AI agent write code.

It's figuring out how developers should work with coding agents when the task becomes large and complex.

Try Taskify

Taskify is open source and available on GitHub:

https://github.com/sheikhfahad67/taskify

If you're using Claude Code for larger projects, I'd love to hear how you currently structure your workflow.

Do you prefer a single agent session, or do you separate planning, implementation, and review into different stages?

I'd also be interested in hearing where you think this approach adds value—and where it simply adds unnecessary overhead.

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Disclosure: AI assistance was used to help edit and structure this article. The project, workflow, and technical experience described here are my own.