Manage tasks as a directed acyclic graph.
I'm still experimenting regularly with tdag: its current state may not reflect its future state much at all. Use with caution: commands or underlying database structure may change without warning!
So many task management systems structure tasks as lists, or, in the better case, as trees. However, neither accurately captures the aspect of multiple parents. What we really need is a directed acyclic graph (DAG) to express the more complex relationships that tasks tend to have.
I like modeling my problems as potentially deep graphs that capture the top-level problem statement, all the way down to the discrete concrete tasks I need to do to work up one level of abstraction to the next layer of tasks. Oftentimes completing one task ought to free up multiple tasks all over my task set, which a graph captures well.
tdag offers the tg
command, which provides quick command line access to your
task graph. tg
wants to be really good at understanding task blockages and
dependencies, in order to excel at answering the question, "What things can I
work on now?".
npm install -g tdag
USAGE: tg
tg
print all top-level tasks
tg ID
print the dependency tree rooted at ID
tg add "fix hyperlog dataset issues"
insert task at root
tg add ID "regenerate corrupted indexes"
add task that is a dependency of todo #ID
tg ready
print all tasks that are ready to be worked on
tg done ID
mark a task as done
tg block ID
tg unblock ID
mark a task as blocked or unblocked
TODO: expand on this!
sww@figure8 $ tg ready
0 ° Try to use tdag for a real project
1 » foo
2 ✓ bar
2 ✓ bar
3 » finish readme
sww@figure8 $ tg done 3
sww@figure8 $ tg ready
0 ° Try to use tdag for a real project
1 » foo
2 ✓ bar
2 ✓ bar
3 ✓ finish readme
tg
manipulates a file named todo.json
in your current directory. This is
nice for easy per-project use, but might not always be desirable and may change
in the future.
tg
operates on a plain JSON file. This is convenient right now, but may change
in the future. However, tdag will always operate on human readable text
formats!
MIT