Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/lexfrei/cccnpx agentmods add skills/lexfrei/ccc/tuneWrote 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/lexfrei/ccc/tune)<a href="https://agentmods.dev/skills/lexfrei/ccc/tune"><img src="https://agentmods.dev/badge/skills/lexfrei/ccc/tune/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/lexfrei/ccc/tune"><img src="https://agentmods.dev/badge/skills/lexfrei/ccc/tune.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00120 | $0.01365 |
| Opus 5.5 | $0.00048 | $0.00546 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo ago First seen · 61 lines · 120 tokens per session scan A 8f1cfe0b7001
tune is a skill published in the GitHub repository lexfrei/ccc (11 stars, last pushed 14d ago), licensed BSD-3-Clause. It adds 120 tokens to every session and 1,365 once invoked, about $0.0005 per session on Opus 5.5. 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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