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 KhaledSaeed18/dotclaude --skill statistics-advisorgit clone --depth 1 https://github.com/KhaledSaeed18/dotclaudeWrote 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/khaledsaeed18/dotclaude/statistics-advisor)<a href="https://agentmods.dev/skills/khaledsaeed18/dotclaude/statistics-advisor"><img src="https://agentmods.dev/badge/skills/khaledsaeed18/dotclaude/statistics-advisor/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/khaledsaeed18/dotclaude/statistics-advisor"><img src="https://agentmods.dev/badge/skills/khaledsaeed18/dotclaude/statistics-advisor.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 | $0.00112 | $0.01640 |
| Opus 5.5 | $0.00045 | $0.00656 |
| Sonnet 5.5 | $0.00022 | $0.00328 |
| Haiku 4.5 | $0.00011 | $0.00164 |
Grade A, and why
statistics-advisor 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 25d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistics answer one question: how much of what was observed could be chance, and how big is the effect if it is real. Everything here serves reporting both parts honestly. Decide the analysis before seeing the data, and report deviations from that plan.
Step 1: describe the data before testing it
Always: n per group, mean and SD (or median and IQR for skewed data), min and max, missing values and why. Plot it (research-figures): a box plot with points or an ECDF per group shows more than any test. Check the shape (histogram, Q-Q plot) and the outliers, and decide how outliers are handled by rule (pre-specified), not by looking at which removal helps.
Step 2: choose the test
| Comparison | Data | Parametric | Non-parametric / robust |
|---|---|---|---|
| two independent groups | continuous | Welch's t-test (default over Student's) | Mann-Whitney U; bootstrap CI of the difference |
| two paired measurements | continuous | paired t-test | Wilcoxon signed-rank |
| three or more groups | continuous | one-way ANOVA (Welch) then Games-Howell post hoc | Kruskal-Wallis then Dunn with Holm |
| repeated measures over conditions | continuous | repeated-measures ANOVA or mixed model | Friedman; mixed model with robust SE |
| two categorical variables | counts | chi-square (expected counts ≥ 5) | Fisher's exact |
| relationship between two continuous | continuous | Pearson r | Spearman rho; Kendall tau for small n |
| outcome from several predictors | continuous | multiple regression | robust regression; quantile regression |
| binary outcome from predictors | binary | logistic regression | penalised logistic (small n) |
| Likert items | ordinal | treat a validated scale total as interval | ordinal regression per item; never a t-test on one item |
| time to event | durations with censoring | Cox regression | Kaplan-Meier with log-rank |
| agreement between raters | categorical | Cohen's kappa (2 raters), Fleiss (more) | Krippendorff's alpha |
Prefer estimation over testing when the question is "how much": report the difference with a 95% CI (bootstrap when in doubt) and let the p-value be secondary.
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.
- 25d ago First seen · 88 lines · 112 tokens per session scan A f456e4ab4c1a
statistics-advisor is a skill published in the GitHub repository KhaledSaeed18/dotclaude (5 stars, last pushed 2d ago), licensed MIT. It adds 112 tokens to every session and 1,640 once invoked, about $0.0004 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 2026-09-22.
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