01Now
Make capture frictionless
The first priority is making the common path feel instant: add a spark, mark it important, finish it, and find it again without remembering a complex command surface.
Roadmap
sparks is intentionally small. The roadmap is about strengthening the terminal workflow before adding anything that could slow it down.
The project grows in three loops: capture ideas quickly, understand how they relate, then move useful local data into other workflows when you ask for it. Each phase protects the same core promise: the terminal remains the fastest place to write down a thought.
02Next
Make connected ideas visible
Once capture feels natural, sparks needs stronger ways to show relationships between ideas, sub-ideas, and follow-up work.
03Later
Make local data useful elsewhere
The final layer is integration: exports, scripts, and automation points that keep data local by default but make it easy to use where work already happens.
Phase 01
Now
Make capture frictionless
The first priority is making the common path feel instant: add a spark, mark it important, finish it, and find it again without remembering a complex command surface.
This phase is about removing hesitation. Commands should be short, output should be obvious, and the default list should help you choose the next action without opening another app.
Sharper defaults for listing active sparks.
Clearer command feedback after add, edit, complete, and important actions.
More forgiving search for titles and nested ideas.
Phase 02
Next
Make connected ideas visible
Once capture feels natural, sparks needs stronger ways to show relationships between ideas, sub-ideas, and follow-up work.
The goal is to make a small local database feel like a thinking map. Tree output, filters, and review commands should make related work visible without adding ceremony.
Better tree and branch views for parent-child ideas.
Focused filters for important, completed, stale, and nested sparks.
Review commands that help clean up old or unfinished thoughts.
Phase 03
Later
Make local data useful elsewhere
The final layer is integration: exports, scripts, and automation points that keep data local by default but make it easy to use where work already happens.
This phase adds escape hatches, not a cloud dependency. sparks should stay scriptable, portable, and easy to back up.
JSON and plain-text export flows for automation.
Simple backup and restore guidance for the SQLite store.
Hooks that let other tools read sparks without owning them.