16bff634b5
A to-do app that doesn't believe you — an enforcement layer rather than a
neutral ledger.
Architecture ported from Autoreceptives/Frontend/Receptive: stacked MVVM with
the mandatory 4-file screen pattern, one ParentViewModel owning the loading /
network / error overlays and the handleError decision tree, one AppDataManager
gateway, dio comms carrying the three identity headers, secure storage with
random-suffixed keys, and a single-chokepoint Navigator. Package root and Dart
package name are both Grounded; org is nya.
The enforcement engine, one unit per formula in utils/:
- DebtEngine w(class) x severity(d) x decay(t), sublinear severity so old
misses cannot swamp the score; abandonment 2x with 30-day
decay immunity; late complete retains 30%
- StandingEngine Good -> Warned -> Grounded -> Lockdown, derived not set;
Grounded replaces home with the overdue queue
- CapacityEngine blocks over-scheduling against p50 of historically completed
minutes, with a learned per-category estimation multiplier
- IntegrityEngine session integrity, weekly volume, plyometric contact ceiling
and enforced recovery gaps
- ExcuseAnalyser on-device excuse clustering plus the confrontation copy
- GuardrailEngine distress detection and rationed amnesty
- ToneEngine all enforcement copy, so the tone cap lives in one place
CommitmentEvent is append-only and is the source of truth rather than the
status field, which is what makes honest history and excuse analysis possible.
Goals contain commitments via parentId, and a task can be run from a
full-screen runner that derives elapsed time from wall-clock so screen-off
cannot lose time. Backgrounding pauses the clock and is counted. The runner is
mirrored into an ongoing notification, with alarm-class full-screen intents
reserved for non-negotiables.
Design language, fonts, icon and native splash are in place; Mason bricks are
retargeted to this project and verified end-to-end.
flutter analyze lib/ reports no errors.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Mfu2gQLSFN21YRBcU2NrTt
74 lines
2.1 KiB
Dart
74 lines
2.1 KiB
Dart
/// Frequency-based rather than instance-based: a single miss costs nothing,
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/// falling below the target in the rolling window is what accrues debt.
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class Habit {
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String? id;
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String title;
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String category;
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/// Target completions per rolling window, e.g. 5.
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int targetPerWindow;
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/// Rolling window length in days, e.g. 7.
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int windowDays;
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/// Completions inside the current window.
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int completionsInWindow;
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/// Marked keystone once the data says its completion predicts day quality —
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/// the app works this out after ~60 days rather than taking your word.
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bool keystone;
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/// 0..1 correlation with overall day quality; -1 until there is enough data.
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double predictiveStrength;
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Habit({
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this.id,
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this.title = "",
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this.category = "",
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this.targetPerWindow = 0,
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this.windowDays = 7,
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this.completionsInWindow = 0,
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this.keystone = false,
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this.predictiveStrength = -1,
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});
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factory Habit.fromJson(Map<String, dynamic> json) {
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return Habit(
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id: json['id'],
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title: json['title'] ?? "",
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category: json['category'] ?? "",
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targetPerWindow: json['targetPerWindow'] ?? 0,
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windowDays: json['windowDays'] ?? 7,
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completionsInWindow: json['completionsInWindow'] ?? 0,
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keystone: json['keystone'] ?? false,
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predictiveStrength: (json['predictiveStrength'] ?? -1).toDouble(),
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);
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}
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Map<String, dynamic> toJson() {
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final Map<String, dynamic> data = <String, dynamic>{};
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data['id'] = id;
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data['title'] = title;
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data['category'] = category;
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data['targetPerWindow'] = targetPerWindow;
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data['windowDays'] = windowDays;
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data['completionsInWindow'] = completionsInWindow;
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data['keystone'] = keystone;
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data['predictiveStrength'] = predictiveStrength;
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return data;
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}
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/// Behind target for the window — the only condition under which a habit
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/// accrues debt.
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bool get behindTarget {
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return completionsInWindow < targetPerWindow;
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}
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int get shortfall {
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final int gap = targetPerWindow - completionsInWindow;
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return gap > 0 ? gap : 0;
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}
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}
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