Guide: LLM/agent planning workflow with taskdb

taskdb is a good fit for agentic workflows because each task is a file and every status change is explicit.

Example feature request

“I’d like to add a React-based contact form to my website.”

An agent can break this into tasks:

• Define fields + validation rules
• Create React form component
• Add server endpoint
• Add spam protection and tests
• Document usage

Then track each step via CLI updates/comments.

Suggested AGENTS.md snippet

## Task Tracking

Before starting multi-step work, create tasks in `taskdb`.

- Use `taskdb create` for each subtask.
- Move active work to `in-progress`.
- Add comments as progress notes and decision logs.
- Mark completed work with `taskdb complete <task-identifier>`.
- Prefer small, reviewable tasks over large umbrella tasks.

Minimal command sequence

taskdb create "Define contact form schema" --labels='["feat","frontend"]'
taskdb create "Implement React contact form UI" --labels='["feat","frontend"]'
taskdb create "Add POST /contact handler" --labels='["feat","backend"]'

taskdb update 12 --status=in-progress
taskdb comment 12 "Schema agreed: name/email/message"
taskdb complete 12