# prepare_batches.py: 入力データを N 件ずつのテキストファイルに分割
# → 各サブエージェントが1ファイルだけ読めばよい
batches = [items[i:i+BATCH_SIZE] for i in range(0, len(items), BATCH_SIZE)]

# 21バッチ × Sonnet subagent を同時起動
for batch in 1..21:
    Task(subagent_type="general-purpose", model="sonnet",
         run_in_background=true, prompt=f"Read batch_{batch}.txt ...")

def normalize_batch(batch_data):
    """辞書型・リスト型どちらでも { id: data } に正規化"""
    if isinstance(batch_data, dict):
        return batch_data
    elif isinstance(batch_data, list):
        result = {}
        for item in batch_data:
            qid = item.get("id") or item.get("questionId")
            data = item.get("enhancedExplanation", item)
            result[qid] = data
        return result

# relatedConcepts: String? なのに array で返ってきた場合
if isinstance(val, list):
    val = "".join(val) if val else None

raw = raw.replace(':="', ': "')  # := → : のタイポ修正

# contrastTable の point 重複チェック
points = [e["point"] for e in contrast_table]
if len(set(points)) < len(points):
    # → 手動で固有テキストに差し替え

key_sets = set()
for item in data['items']:
    for entry in item.get('nested', []):
        key_sets.add(tuple(sorted(entry.keys())))
print(f'Unique key patterns: {len(key_sets)}')
for ks in sorted(key_sets):
    print(f'  {ks}')

for entry in item.get('nested', []):
    normalized = {}
    normalized['choice'] = entry.get('choice') or entry.get('choiceLabel') or entry.get('option', '')
    normalized['point'] = entry.get('point') or entry.get('reason', '')
    # ...

bad = [(item['id'], set(e.keys()))
       for item in data['items']
       for e in item.get('nested', [])
       if set(e.keys()) != EXPECTED_KEYS]
assert not bad, f'Still inconsistent: {bad}'