Claude Task Master Just Fixed Our Vibe Coding Workflow, Here's What Happened

At Samelogic, we're always hunting for clever ways to speed up our development cycle. When tackling complex features, the right task management approach can shave weeks off delivery times. Recently, we struck gold by bringing Claude Task Master into our vibe coding workflow, and honestly, I wish we'd done it sooner.

The Task Management Reality Check

Most developers know the dance. You start with grand plans, sketch out some tasks, then promptly abandon your tracking system two days in when implementation reality hits. Your beautiful Kanban board becomes digital wallpaper while actual coordination happens in frantic Slack messages.

Claude Task Master flipped our workflow on its head

What Is Claude Task Master?

Claude Task Master is an AI-powered task management system that plays beautifully with Cursor (AI powered IDE), the code editor quietly changing how smart developers work. This clever system breaks projects into bite-sized tasks, maps dependencies, spots complexity issues before they become problems, and keeps your development momentum rolling when things get tough.

Unlike your standard task tracker, Claude Task Master taps into Claude's AI smarts to actually understand what you're building, generate sensible tasks, and keep everyone moving in the right direction.

What We Learned: Best Practices

Want to supercharge your own workflow? Here's what worked for us:

1. Start with an obsessively detailed PRD – The more specific your requirements, the better the generated tasks and code

2. Input task-master commands directly to Cursor Chat (Agent Mode) – Bypass the command line and talk to your AI assistant directly. I personally use 3.7 Sonnet or Gemini Pro 2.5 (as of writing) for best results.

3. Check complexity before implementation – Use task-master analyze-complexity in chat to spot trouble before starting

4. Break down complex tasks in chat – Use task-master expand commands to create manageable pieces

5. Keep task state current – Use task-master complete --id=<task_id> as you finish each component. At times it will automatically mark each task as complete, however, I love to review each task personally before marking a task as complete.

6. Regenerate task files after changes – task-master generate ensures Cursor's context stays fresh

7. Let Cursor implement complete features – Don't micromanage the agent; task context gives it enough direction

The Challenge: Building a Web Scraper with Firecrawl

We needed a serious web harvester built on Firecrawl's Node SDK. We're talking authenticated session handling, SPA navigation, dynamic content extraction, the works. The kind of project where tracking dependencies becomes a full-time job.

A scraper that crumbles when hit with CAPTCHAs or rate limiting isn't worth deploying. We needed proxy rotation, session persistence, smart retries, and clean data pipelines. Each piece with its own complexity and interdependencies.

The perfect testing ground for a new task management approach.

Setting Up Claude Task Master

Getting started with Claude Task Master couldn't be much simpler. We installed the package globally:

After a quick task-master init, we dropped our detailed product requirements document in the scripts/ directory. From there, Cursor transformed our PRD into structured tasks with clear dependencies, no more whiteboard sessions that somehow create more questions than answers.

The Command Arsenal: CLI Commands That Actually Help

Most CLI tools give you a Swiss Army knife when you need a chainsaw. Claude Task Master's commands actually match how projects evolve in real life.

Initial Project Setup

We pointed the system at our requirements doc. The PRD was written in Markdown and then saved to a .txt:

For more granular control, we occasionally limited the initial task generation:

This gave us a manageable starting point without overwhelming us with dozens of tasks.

The Flow: Build, Complete, Next, Repeat

The true power of our approach lies in the continuous build cycle. There's no "daily planning" here, just a seamless flow from one task to the next. After implementing a feature, verifying it worked, and running tests, we'd tell Cursor:

This marked the current task complete and updated all dependencies. Then, immediately, we'd run the command that drove our entire development flow:

Entered directly in Cursor's chat interface, this command would instantly:

1. Determine the highest-priority unblocked task

2. Present it to Cursor with complete context

3. Prepare the AI to implement the next logical piece

Cursor would respond with something like:

"Great! The next task to implement is Task #6: Build the rate limiter component. This task involves creating a system that manages request rates to prevent IP blocking, with configurable delays between requests. Would you like me to implement this now?"

With a simple "yes," Cursor would begin implementing the next component with full awareness of what we'd already built and how it should integrate with the existing code.

This tight loop, implement, complete, next, implement, became the engine of our development process. We'd review Cursor's implementation, make any necessary adjustments, mark it complete, and immediately move to the next task. Very little context switching (at times none), no debates about what to do next, just continuous, focused progress until the entire PRD was implemented.

When we needed to see the big picture:

Which looks like this:

This gave us a bird's-eye view of progress, but we rarely needed it. The beauty of the system was that the next command always knew exactly what should be built next based on dependencies and priorities in our task graph.

Handling Complex Features Through The Agent

When facing that beastly proxy rotation system, we typed directly to Cursor:

Task Master displayed the complexity analysis and identified our proxy rotation task as a 9/10 complexity monster. Then we had Cursor break it down by entering:

Task Master would split this into subtasks, explain the breakdown, and ask which subtask to implement first. The beauty of this approach? Cursor maintained complete context about the parent task and all subtasks throughout implementation.

For more focused breakdowns, we'd type:

And Cursor would create four specialized subtasks targeting error handling scenarios, then immediately offer to implement any of them.

When Plans Change Mid-Project

When we needed to pivot from Firecrawl's request handling to Axios, we told Cursor directly:

The magic happened immediately. Task Master updated the task definitions and offered:

"I've updated all pending tasks to use Axios instead of Firecrawl's native requests. I can refactor the existing implementation now. Would you like me to handle that?"

A simple "yes" would set Cursor to work updating code across multiple files with a consistent approach, all with the correct context from our task definitions.

When we discovered data dependencies:

Task Master would immediately understand the relationship and adjust its implementation:

"I've noted that the data normalization pipeline depends on the content extraction module. When implementing the pipeline, I'll ensure it correctly handles the output format from the extraction module and includes proper validation checks."

For sanity checks on complex dependency networks:

Cursor would catch potential issues: "I've detected a circular dependency between rate-limiting and proxy rotation. Would you like me to suggest an architectural approach that resolves this?"

Keeping Cursor's Context Fresh

After any significant changes to our task structure, we'd type:

Directly in the chat interface. This regenerated individual task files, ensuring Cursor had the latest task definitions when implementing features.

For research-heavy components, we'd tap into Claude's knowledge via Task Master:

The agent would create research-backed subtasks and offer implementation approaches based on industry best practices, all without leaving the chat interface.

Final Thoughts

The Claude Task Master + Cursor agent workflow didn't just improve our development process, it completely transformed it. By giving Cursor structured task context through direct task-master commands, we turned our AI assistant from a helpful code suggester into an active implementation partner.

For our Firecrawl web scraper, this meant building a robust, production-ready tool in a fraction of the time it would have taken with traditional approaches. The combination of structured tasks and AI implementation eliminated the most time-consuming aspects of development: context switching, integration headaches, and implementation details.

You don't need to rebuild your entire workflow. Just try this approach on your next feature and see what happens when your AI assistant has crystal-clear context on what to build.