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 hamzabellouch/agent-skills --skill statistics-and-data-sciencegit clone --depth 1 https://github.com/hamzabellouch/agent-skillsWrote 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/hamzabellouch/agent-skills/statistics-and-data-science)<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/statistics-and-data-science"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/statistics-and-data-science/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/hamzabellouch/agent-skills/statistics-and-data-science"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/statistics-and-data-science.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 | $0.00063 | $0.00681 |
| Opus 5.5 | $0.00025 | $0.00272 |
| Sonnet 5.5 | $0.00013 | $0.00136 |
| Haiku 4.5 | $0.00006 | $0.00068 |
Grade A, and why
statistics-and-data-science 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 today.
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 — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistics & Data Science
Descriptive
Mean, median, mode, variance s^2 (n-1), IQR, skewness, kurtosis, correlation (Pearson, Spearman), outliers, visualization (histogram, box plot, scatter, QQ plot).
Estimation & inference
- Estimators: bias, variance, MSE, consistency, efficiency; MLE (maximize log-likelihood; asymptotically normal, Fisher information, Cramer-Rao bound); method of moments.
- Confidence interval for a mean: x-bar +- t_{n-1,a/2} s/sqrt(n); proportion p-hat +- z sqrt(p-hat(1-p-hat)/n) (use Wilson interval for small n). A 95% CI means the procedure covers the true value 95% of the time, not a 95% probability for this interval.
- Hypothesis testing: null/alternative, test statistic, p-value = P(data at least as extreme | H0), alpha, Type I/II errors, power. Tests: z, t (one-sample, two-sample Welch, paired), chi-square (goodness of fit, independence), F-test, ANOVA, Mann-Whitney, Wilcoxon, Kolmogorov-Smirnov, permutation tests.
- Multiple testing: Bonferroni, Holm, Benjamini-Hochberg FDR. Effect sizes (Cohen d) and practical significance; p-hacking and pre-registration; sample-size planning.
- Bayesian: posterior ~ likelihood x prior; conjugate pairs (Beta-Binomial, Normal-Normal, Gamma-Poisson); credible intervals; Bayes factors; MCMC (Metropolis, Gibbs, HMC); posterior predictive checks.
- Bootstrap and jackknife for uncertainty without formulas.
Regression & modeling
- OLS: beta = (X^T X)^{-1} X^T y; R^2; residual diagnostics (linearity, homoscedasticity, normality, independence); multicollinearity (VIF); leverage and Cook's distance.
- GLMs: logistic (odds ratio), Poisson regression; regularization (ridge, lasso, elastic net); cross-validation; bias-variance tradeoff; AIC/BIC.
- Time series: stationarity, ACF/PACF, ARIMA, exponential smoothing, state-space models.
- Machine learning basics: train/validation/test split, overfitting, ensemble methods, calibration. Correlation is not causation: confounders, randomized trials, instrumental variables, difference-in-differences, DAGs.
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.
- today First seen · 30 lines · 63 tokens per session scan A 65602a52c65b
statistics-and-data-science is a skill published in the GitHub repository hamzabellouch/agent-skills (8 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 681 once invoked, about $0.0003 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 2026-10-17.
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