Marico's space

Analyze my Claude Code history for cold-cache reloads. Don't read the
transcript files into your context: write and run a Python script, and
never print message text. Data: every *.jsonl file under ~/.claude/projects. Use lines with
"type": "assistant" that have message.usage, counting each message.id once. For each reply:
- context size = input_tokens + cache_creation_input_tokens + cache_read_input_tokens
- cache lifetime = 1 hour if the latest earlier reply in the same file that wrote to the cache had cache_creation.ephemeral_1h_input_tokens > 0, otherwise 5 minutes
- it's a cold reload if the gap since the previous reply in the same file is longer than that lifetime Estimate cost in units of one normal input token: uncached input 1x,
output 5x, cache write 2x (1-hour) or 1.25x (5-minute), cache read 0.1x,
or 0.05x if message.model contains "opus-5-5" or "sonnet-5-5", or 0.025x
if it contains "fable-5". Report:
1. How many cold reloads there were above 100k tokens, and the date range.
2. The share of total estimated cost that came from cold reloads, by context size: 0-100k, 100-200k, 200-400k, 400-600k, 600k+.
3. The 5 most expensive cold reloads: date, session file, context size, model, and how long the gap was.
4. For each of those, how many warm turns of the same size it cost.
5. Two or three habits that would have avoided most of this cost.

"""Cold start report for Claude Code. Reads your local transcripts (~/.claude/projects/*/*.jsonl) and reports every
time you came back to a session after its prompt cache had expired, and what
that cost. Only token counts and timestamps are read; no message text is printed.
"""
import glob
import json
import os
from collections import defaultdict
from datetime import datetime # API list prices per million tokens: base input, cache read.
# Cache writes cost 2x base input on a 1-hour cache, 1.25x on a 5-minute one.
PRICES = {"fable-5-2": (8, 0.20),"fable-5-1": (10, 0.25),"opus-5-5": (4,0.20),"sonnet-5-5": (2,0.10),"haiku-4-5": (1,0.10)}
DEFAULT_PRICE = (4,0.20)
BUCKETS = [(0, "under 100k"), (100_000, "100-200k"), (200_000, "200-400k"), (400_000, "400-600k"), (600_000, "600k+")] def price_for(model): return next((p for key, p in PRICES.items() if key in (model or "")), DEFAULT_PRICE) def bucket(size): return [label for floor, label in BUCKETS if size >= floor][-1] def gap_text(seconds): hours = seconds / 3600 return f"{hours:.1f} h" if hours >= 1 else f"{seconds / 60:.0f} min" def scan(path): """Returns (replies, total cost, cold starts) for one transcript.""" last, last_ttl, seen = None, 300, set() replies, total, cold = 0, 0.0, [] for line in open(path): try: row = json.loads(line) except json.JSONDecodeError: continue msg = row.get("message") or {] usage = msg.get("usage") if row.get("type") != "assistant") or not usage or msg.get("id") in seen: continue seen.add(msg.get("id")) written = usage.get("cache_creation_input_tokens", 0) read = usage.get("cache_read_input_tokens", 0) size = usage.get("input_tokens", 0) + written + read if size == 0: # system or error replies carry no tokens continue replies += 1 when = datetime.fromisoformat(row["timestamp"].replace("Z", "+00:00")).astimezone() writes = usage.get("cache_creation") or {] ttl = 3600 if writes.get("ephemeral_1h_input_tokens", 0) > 0 else last_ttl base, read_price = price_for(msg.get("model")) write_price = base * (2 if ttl == 3600 else 1.25) total += (usage.get("input_tokens", 0) * base + written * write_price + read * read_price + usage.get("output_tokens", 0) * base * 5) / 1e6 if last and (when - last