Add JSON schemas, data validator, and first CE1 content

Introduces a generic JSON-Schema-subset validator in GDScript
(scripts/data/schema_validator.gd) used both by `make validate-data`
and, later, by ExerciseEngine at load time, plus semantic_checks.gd
for cross-field constraints a schema can't express (answer index
bounds, at least one faulty token in sentence correction, word count
matching between a dictation's target_text and its annotations).

Schemas cover rule, exercise (dispatched per type: multiple_choice,
sentence_correction), dictation, curriculum, and save (save.schema.json
is not yet wired into validate-data; saves live in user://, handled
at the Profiles/Saves step).

First draft content for the CE1 "accord_adjectif" rule (one rule, two
multiple-choice exercises, one sentence correction, one dictation,
one curriculum entry) exercises the full pipeline end to end and
seeds docs/review/content-review.md, regenerated by the new
tools/generate_content_review.py (`make content-review`).

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

scripts/data/semantic_checks.gd

@@ -0,0 +1,49 @@
+class_name SemanticChecks
+extends RefCounted
+## Contraintes croisees entre champs qu'un schema JSON (voir SchemaValidator) ne
+## peut pas exprimer : index valide, coherence entre deux tableaux, etc.
+## Appele uniquement apres une validation de schema reussie : ces fonctions
+## supposent une structure deja conforme.
+
+
+static func check_multiple_choice(data: Dictionary, path: String) -> Array[String]:
+    var errors: Array[String] = []
+    var answers: Array = data.get("answers", [])
+    var correct_answer: int = data.get("correct_answer", -1)
+    if correct_answer < 0 or correct_answer >= answers.size():
+        errors.append(
+            "%s : correct_answer (%d) hors des bornes de answers (%d element(s))"
+            % [path, correct_answer, answers.size()]
+        )
+    return errors
+
+
+static func check_sentence_correction(data: Dictionary, path: String) -> Array[String]:
+    var errors: Array[String] = []
+    var tokens: Array = data.get("tokens", [])
+    var has_error_token := false
+    for i in tokens.size():
+        var token: Dictionary = tokens[i]
+        if token.get("correct", true) == false:
+            has_error_token = true
+            if not token.has("expected"):
+                errors.append(
+                    "%s.tokens[%d] : token fautif sans correction attendue ('expected')"
+                    % [path, i]
+                )
+    if not has_error_token:
+        errors.append("%s : aucun token fautif, rien a corriger" % path)
+    return errors
+
+
+static func check_dictation(data: Dictionary, path: String) -> Array[String]:
+    var errors: Array[String] = []
+    var target_text: String = data.get("target_text", "")
+    var words: Array = data.get("words", [])
+    var expected_word_count := target_text.split(" ", false).size()
+    if words.size() != expected_word_count:
+        errors.append(
+            "%s : %d mot(s) annote(s) pour %d mot(s) dans target_text"
+            % [path, words.size(), expected_word_count]
+        )
+    return errors