Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add duthaho/skillhub --skill cachewisegit clone --depth 1 https://github.com/duthaho/skillhubWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/duthaho/skillhub/cachewise)<a href="https://agentmods.dev/skills/duthaho/skillhub/cachewise"><img src="https://agentmods.dev/badge/skills/duthaho/skillhub/cachewise/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/duthaho/skillhub/cachewise"><img src="https://agentmods.dev/badge/skills/duthaho/skillhub/cachewise.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00204 | $0.01521 |
| Opus 5 | $0.00102 | $0.00760 |
| Sonnet 5 | $0.00041 | $0.00304 |
| Haiku 4.5 | $0.00020 | $0.00152 |
Grade A, and why
cachewise scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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, andunattributed(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.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 124 lines · 204 tokens per session scan A eeb6f0276dbe
cachewise is a skill published in the GitHub repository duthaho/skillhub (9 stars, last pushed 3d ago), licensed MIT. It adds 204 tokens to every session and 1,521 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-09.
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