Tasker: Multi-agent Development with Claude Code

Tasker is a multi-agent architecture built with Claude Code that facilitates planning and implementation through a simple UI: /plan and /execute. It features a TUI dashboard for monitoring progress (tmux required).

The project began by taking a deep dive into task decomposition, developing a protocol for it and then using it as the basis for agentic planning. The task decomposition protocol is at the heart of the planning stage of Tasker.

Tasker Execels at Large Projects

You can go a really long way with Claude Code using off-the-shelf capabilities for planning and implementation. I often do, using prompts like these:

Planning:

review {spec} and @README.md. then, review the source code. we're just planning,
converting specs to well-documented, self-contained tasks. These tasks will be
worked on by sub-agents, who need sufficient context to do a good job. Figure out
a concurrency strategy that you can apply across your task dependencies, where you
may safely spawn asynchronous sub-agents to work on tasks. Save a Task DAG
dependency graph along with the plan to plan.md

Execution:

I want you to read {specs} and {plan}. Use a concurrency strategy where you may
safely spawn asynchronous sub-agents to work on tasks in waves. Then, for each
task in the plan, spawn a sub-agent, concurrently when safe to do so, giving the
sub-agent the task and instruction to implement a solution for the task, and also
give it a full copy of {specs} for context about the project. Tell the sub-agent
to review the specs and the source code for the project before starting.

This simple pattern works well for small changes whereas Tasker excels for larger projects.

Observations and Insights

I built Tasker in December 2025 and tested it across several Python projects of varying scope. To evaluate performance, I developed rubrics comparing implementations built with off-the-shelf Claude Code against those built with Tasker. Tasker consistently scored as well as, if not better than, the baseline.

Tasker currently uses skills and sub-agents powered by Opus 4.5 with unoptimized prompts. Typically, you wouldn’t want an LLM to author prompts for production use since skilled engineers can still do a better job designing them. Given this limitation and the current results, I expect an optimized version of Tasker to perform even better. This is a promising start.

Next Steps

Take a look at the Github repo and give Tasker a try.