How Klokd turns usage signals into editable recommendations

From events to patterns

Klokd organizes supported usage signals into understandable features such as timing, duration, reopening, switching, and notification proximity. It looks for repeat behavior across observations rather than treating one unusual session as a stable habit.

From patterns to interventions

A recommendation maps the shape of a pattern to a compatible form of friction. The recommendation is a starting point, not an automatic command.

• Limits address accumulated duration
• Scheduled blocks address recurring windows
• Guards add friction at a chosen boundary
• Doses allow intentional, time-boxed access
• Focus sessions reduce competing distractions for a defined period

What the method does not claim

Klokd does not diagnose addiction, infer a user's intentions with certainty, or promise a clinical outcome. Detected patterns depend on the permissions, device behavior, and observation period available. Users should interpret the evidence in context.

Privacy and control

Core analysis is designed to stay on-device. Browser-extension browsing analytics stay local to the browser. Every suggested rule remains editable and requires user approval. See the privacy policy for the full data-handling description.