Cachewise

cachewise — where your cache dollars leak

/cachewise — analyze the last 30 days across every project /cachewise --days N — widen or narrow the window

Answer one question: which habits are rebuilding cache you already paid for, and what would each fix save? Prompt caching makes a cache read cost 0.1× and a rebuild cost 1.25× — so every avoidable miss is a ~12× markup on that slice of context. ccusage and /cost count the tokens; cachewise says why the expensive ones happened and what to change. The evidence is on disk — the transcripts recorded every cache write; this skill attributes them so the numbers, not folklore, drive the fix.

Step 0 — Run the analyzer

The counting is deterministic and token-free — never read the raw JSONL yourself (parsing thousands of transcript lines with the model is the exact token burn this skill exists to stop). Run the bundled script:

python3 .claude/skills/cachewise/scripts/analyze.py --days 30

It scans ~/.claude/projects/**/*.jsonl and prints one JSON document. No ~/.claude data (fresh machine, or none in the window) → the script returns zero turns; say so plainly and stop, there's nothing to diagnose.

Before trusting the numbers, glance at flags:

• pricing_fallback_models — models priced at the Sonnet-4.x fallback because their id wasn't in the table (USD is approximate for those).
• missing_timestamp_turns / malformed_lines — data the parser skipped; large counts mean the picture is partial.

Step 1 — Read the attribution

The report splits cost three ways — keep them distinct, they prescribe differently:

• miss_attribution — cache rebuilds, the avoidable core. Each cause carries tokens + USD (the money a cache hit would have saved): idle_gap, model_switch, write_churn, and unattributed (cause undeterminable, usually missing timestamps). The USD is avoidable spend, not total spend.
• context_tax — not a miss: the standing read-cost of sessions that carry far more prefix per turn than your same-model norm. Sprawl, priced as excess reads.
• dead_session — low-confidence heuristic: cold re-boots of a same-project session soon after another ended. Report it as a lead, not a fact — say "possibly" and cite the count.

totals.usd.total is the whole API-equivalent spend in the window; miss_attribution.total_usd is the slice that better habits recover.

Step 2 — Render the report

Present, in chat, most-expensive-first:

1. Headline — window, total API-equivalent spend, cache hit rate, and the avoidable miss total in USD (e.g. "94% hit rate; $1,534 of $5,944 was avoidable cache rebuilds").
2. Prescriptions — walk prescriptions in order (already ranked by USD). For each: the cause, its dollar cost, the one-line fix from the script, and the top offending sessions/projects from top_offenders so the user sees where it happened. Mark dead_session and context_tax as the softer, lower-confidence lines they are.
3. Caveats — surface any flags that make the numbers approximate, and the standing note: for subscription (Pro/Max) users these are API-equivalent values, not a bill — the ratios still hold, the absolute dollars are a yardstick.

Keep it honest: if the hit rate is already high and avoidable spend is a rounding error, say the setup is healthy rather than manufacturing a problem.

Step 3 — Track before/after

Memory is what turns a one-off report into a measurable fix. Append a run to out/cachewise/tracker.md (create it if absent; date via date +%F):

## <date> · window <N>d
- Spend: $<total> · hit rate <pct>% · avoidable $<miss_total>
- By cause: idle_gap $X · write_churn $Y · model_switch $Z · dead_session $W (low-conf) · context_tax $V
- Prescribed: <the top 1–3 fixes handed to the user this run>
- Since last run: <hit-rate and avoidable-$ delta vs the previous entry, or "baseline">

On every run after the first, read the previous entry and lead the report with the delta — did avoidable spend fall after last run's fix, or not? A prescription whose number didn't move next run is one to retire or rethink, not repeat.

Guardrails

• The script counts; the model interprets. Reading raw transcripts into context to tally tokens is the waste this skill diagnoses — don't commit it. If the script can't answer something, extend the script, not the habit.
• Avoidable, not total. Miss USD is what better habits recover, never the whole bill — conflating them scares the user off legitimate spend.
• Confidence is labeled, not implied. dead_session and context_tax are heuristics; present them as leads. Never state a cause the transcript can't support — unattributed exists so misses aren't force-fit.
• Prices carry a date. The USD table is stamped pricing_asof; when it ages, reverify against the official pricing page rather than trusting it.
• Privacy is structural. Transcripts stay local; only aggregate numbers reach the report. out/cachewise/ holds no prompt content.