Shipping Better With Lightweight Task Management

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I recently added a simple task-management layer to my AI CLI setup, and it has been a big quality-of-life upgrade.

The core idea is straightforward: keep work trackable without adding process overhead.

  • Tasks use stable IDs (0001, 0002, ...)
  • Bugs use their own stable IDs (BUG-0001, BUG-0002, ...)
  • Active files stay small (TODO.md, BUGS.md)
  • History is preserved (DONE.md, BUGS_DONE.md, REMOVED.md)
  • Progress is append-only (progress_log.md)

What I like most is that it works for both solo and agent-driven workflows. I can run tasks in sequence or parallel, and still keep clear visibility on what’s active, what’s done, and what was dropped.

I also added optional Discord notifications for agent progress. Secrets are handled safely by default .

The goal wasn’t to build another project-management system. It was to keep execution resumable, context small, and momentum high.

If you’re using coding agents, I highly recommend having a minimal, file-based task protocol like this. It scales surprisingly well.

Feel free to check it out in my repo dedicated to agent skills, policies and configurations: https://github.com/aleksandarristic/ai-cli-config

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