Stage 1: project setup, Exercise Library, both Mode designs and the Mode switch

- Flutter Android app on CookIT's stack (provider, shared_preferences, gen-l10n)
- tools/exercise-import turns free-exercise-db into 876 bundled Exercises
  with 1,746 WebP images (20 MB); stretching -> Stretching, rest -> Workout
- ZenStyle tokens per Mode: calm rounded sage/teal with breath ripples vs.
  dark ember-to-red with floor-tape slashes; the theme morphs at each
  Mode's own pace
- Remembered global Mode switch, Plans / Library / History tabs

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

tools/exercise-import/exercise_import.py

@@ -0,0 +1,69 @@
+"""Turns a checkout of free-exercise-db into ZenXercise's bundled Exercise Library.
+
+Writes `exercises.json` (slimmed, with each Exercise's Mode) and one WebP per
+image into the output folder, flattened to `images/<id>_<n>.webp` because
+Flutter assets are declared per folder.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from PIL import Image
+
+STRETCHING_CATEGORY = "stretching"
+MAX_EDGE = 480
+WEBP_QUALITY = 72
+
+
+def mode_of(category: str) -> str:
+    """Category `stretching` belongs to Stretching Mode, everything else to Workout Mode."""
+    return "stretching" if category == STRETCHING_CATEGORY else "workout"
+
+
+def slim(exercise: dict) -> dict:
+    """The fields the app needs, with images reduced to a count."""
+    return {
+        "id": exercise["id"],
+        "name": exercise["name"],
+        "mode": mode_of(exercise["category"]),
+        "category": exercise["category"],
+        "level": exercise["level"],
+        "equipment": exercise.get("equipment"),
+        "force": exercise.get("force"),
+        "mechanic": exercise.get("mechanic"),
+        "primaryMuscles": exercise["primaryMuscles"],
+        "secondaryMuscles": exercise["secondaryMuscles"],
+        "instructions": [line.strip() for line in exercise["instructions"] if line.strip()],
+        "imageCount": len(exercise["images"]),
+    }
+
+
+def image_name(exercise_id: str, index: int) -> str:
+    return f"{exercise_id}_{index}.webp"
+
+
+def convert_image(source: Path, target: Path) -> None:
+    with Image.open(source) as image:
+        image = image.convert("RGB")
+        image.thumbnail((MAX_EDGE, MAX_EDGE))
+        image.save(target, "WEBP", quality=WEBP_QUALITY, method=6)
+
+
+def run(source_root: Path, output_root: Path) -> list[dict]:
+    exercises = json.loads((source_root / "dist" / "exercises.json").read_text(encoding="utf-8"))
+    images = output_root / "images"
+    images.mkdir(parents=True, exist_ok=True)
+    for stale in images.glob("*.webp"):
+        stale.unlink()
+
+    slimmed = sorted((slim(e) for e in exercises), key=lambda e: e["name"].lower())
+    for exercise in exercises:
+        for index, relative in enumerate(exercise["images"]):
+            convert_image(source_root / "exercises" / relative, images / image_name(exercise["id"], index))
+
+    (output_root / "exercises.json").write_text(
+        json.dumps(slimmed, ensure_ascii=False, separators=(",", ":")), encoding="utf-8"
+    )
+    return slimmed