Classify Capture

To convert stream-of-consciousness capture text into actionable backlog items, the skill parses raw input and assigns a kind, title, description, priority, tags, and confidence to each distinct task. It outputs structured backlog suggestions while writing classification.json.

No-op detection for unactionable capture text triage works by recognizing greetings, tests, or gibberish and returning an empty items array. This prevents meaningless backlog noise from unactionable phrases entering your workflow.

The best way to automate capture folder workflows for backlog triage is to process raw user input, write classification.json with backlog suggestions, and update capture.json to a classified status to support downstream planning.

Yes, you can use this skill to triage raw user input without creating meaningless backlog noise. It distinguishes actionable versus unactionable phrases, returning an empty no-op payload for unactionable text to keep your backlog clean.

Yes, this backlog triage process requires guideline-driven assignment to classify capture text accurately. It uses guidelines to assign kind, title, description, priority, tags, and confidence to each extracted task.

You should classify stream captures before writing classification.json to ensure raw user input is properly triaged into structured backlog ideas. This classification step flips capture.json to a classified status, completing the triage workflow.