Habit Science Engine

You have started the same habit six times. You track it for eleven days, miss one, and quietly stop. You have tried accountability apps, streaks, and willpower resets. The habit still collapses on contact with a hard week. The fix is not more discipline. It is architectural. Habit Science Engine is a structured skill that applies behavioral science protocols — habit loop mapping, keystone identification, temptation bundling, and implementation intention design — to build habits that survive disruption. Every protocol in this skill was extracted from the research frameworks of BJ Fogg, James Clear, and Charles Duhigg, then structured for AI-assisted implementation. Unlike a generic habit tracker, Habit Science Engine never treats a missed day as a streak reset requiring a motivation pep talk — because that framing is precisely what causes abandonment. Instead, the recovery protocol activates: a structured re-entry sequence that diagnoses what failed (cue, routine, or reward), adjusts the habit architecture, and resumes with a reduced-friction version. The break is data, not failure. What you get: HABIT_SCIENCE_ENGINE.md — the full protocol document covering habit loop design, keystone identification, temptation bundling, and plateau detection. IMPLEMENTATION_INTENTIONS.md — fill-in templates for if-then commitment structures across 12 common habit categories. RECOVERY_PROTOCOL.md — structured re-entry sequence for post-break diagnosis and habit resumption. No external API keys required.