Phase "score et récompenses" of the post-0.1.0 plan (user feedback:
wants a more game-like experience with scoring and rewards).
- MissionScoring: 1-3 stars from how each exercise was solved (never
speed; a finished mission always gives at least one star).
- Save schema v2: mission_results (best/last stars, completions) with a
v1->v2 migration that keeps existing stars and items.
- Total only grows by the improvement over a mission's best score.
- Data-driven collection (data/rewards, reward_item schema, validated
incl. mission cross-check) unlocked by RewardRules.
- Reward screen, collection screen, stars on the world map.
- Version 0.2.0.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Same directives as the CE1 chapter: 4 rules with a substitution test
each, 8 multiple-choice + 4 sentence corrections + 3 dictées per rule
(draft), 3 missions per rule, Piper audio for the 12 dictées.
21 missions don't fit on one map, so the world map now shows the
missions of the profile's school level (derived from the rule's level,
no data-format change), with a button letting a CE2 profile revisit the
CE1 map. Mission-position guard test now checks each level separately.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Wires every autoload up to a real UI: boot -> profile select/create
-> world map (three mission markers, positioned from data, not
hardcoded pixels) -> mission intro (narrative from data/missions/,
a new data type with its own schema) -> rule panel -> exercise host
-> one of the three exercise type scenes -> feedback panel -> reward
screen -> back to the map.
exercise_host is the only piece with real orchestration logic: picks
exercises via ExerciseEngine (now fully implemented, validating at
load like make validate-data does), validates answers through the
pure AnswerValidator/DictationAnalyzer, escalates aid over 3 attempts
via AttemptFlow, feeds DifficultyManager so adaptive difficulty is
actually wired in rather than dead code, and records every attempt
through ProgressManager. Every exercise-type scene (multiple_choice,
sentence_correction, dictation) only emits its answer by signal, per
the architecture invariant.
All child-facing text now comes from data/localization/fr.json via
UIText, not hardcoded in scripts or scenes. Getting that right
required extending SchemaValidator to support "additionalProperties"
as a schema (dynamic keys), which also closed a real gap: save.skills
was previously unvalidated against SkillState's actual shape.
16 new headless tests, including two integration tests that instance
exercise_host.tscn directly and drive it by emitting child-scene
signals, stopping short of the final scene transition (which would
tear down the test runner's own tree). Everything that needs a
screen -- layout at different sizes, touch, virtual keyboard, actual
audio playback (no .ogg files exist yet) -- is listed in the new
docs/testing/manual-checklist.md instead of claimed as verified.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Pins Godot 4.6.3-stable and GUT 9.6.0 (9.6.1+ reference
AccessibilityServer, unavailable in 4.6.3) via tools/setup.sh. Wires
make check (gdlint + gdformat config for 4-space indent, headless
GUT run, and a JSON well-formedness check ahead of real schema
validation). Seven autoload stubs match the architecture in the
cahier des charges; real logic lands with the following steps.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Denis Defolie