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 agentmods add skills/lexfrei/ccc/tunenpx skills add lexfrei/ccc --skill tunegit clone --depth 1 https://github.com/lexfrei/cccWhat 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 | $0.00120 | $0.01365 |
| Opus 5 | $0.00060 | $0.00682 |
| Sonnet 5 | $0.00024 | $0.00273 |
| Haiku 4.5 | $0.00012 | $0.00136 |
Grade A, and why
tune 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 3d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tuning is the taguchi skill pointed at optimization instead of blame: same arrays, same run sheets, but the outcome is a number to improve and the analysis picks the best level of every knob at once. One L9 pass over four 3-level knobs reads out all four response curves in 9 runs; one-knob-at-a-time needs 12 runs and still misses interactions.
Step 1 — design via the taguchi skill
Use the taguchi skill's steps 1-4 for factor definition, array selection, and the run sheet, with two tuning-specific defaults:
- Prefer 3 levels per knob — low, mid, high of the plausible range. Two levels see only a line; three see curvature, which is where optima live. L9 fits four 3-level knobs, L18 fits seven plus one binary.
- Run every row 2+ times. Variance per row is data here, not noise to average away — the S/N analysis below needs it.
Generate the sheet with the taguchi skill's scripts/design.py (../taguchi/scripts/design.py from here) — the array, the columns and the dummy treatment are mechanical, and a hand-built sheet is where a silent transcription error enters.
Keep it in the journal (../taguchi/scripts/experiment.py new ... --repeats 3) and let ../taguchi/scripts/run.py execute the sheet — a benchmark is a command, so the array is one invocation with --metric 'p95=([0-9.]+)' instead of a dozen hand-run measurements, and the per-row repeats the S/N ratio needs come out balanced.
Measure the current configuration first, before the array. It is the reference every S/N number is judged against, and it is the cheapest check that the measurement harness reports what you think it does.
Step 2 — pick the S/N ratio for the goal
Taguchi's signal-to-noise ratio folds "good on average" and "stable" into one number computed per row from its repeats y1..yn. Higher is always better:
| Goal | S/N per row |
|---|---|
| Minimize (latency, RSS, cost) | −10·log10(mean(y²)) |
| Maximize (throughput, hit rate) | −10·log10(mean(1/y²)) |
| Hit a target (offset, utilization) | 10·log10(ȳ²/s²) |
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.
- 3d ago First seen · 61 lines · 120 tokens per session scan A 8f1cfe0b7001
tune is a skill published in the GitHub repository lexfrei/ccc (9 stars, last pushed 3d ago), licensed BSD-3-Clause. It adds 120 tokens to every session and 1,365 once invoked, about $0.0006 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 2026-08-31.
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