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/wangyendt/wayne-skills/statisticsnpx skills add wangyendt/wayne-skills --skill statisticsgit clone --depth 1 https://github.com/wangyendt/wayne-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/wangyendt/wayne-skills/statistics)<a href="https://agentmods.dev/skills/wangyendt/wayne-skills/statistics"><img src="https://agentmods.dev/badge/skills/wangyendt/wayne-skills/statistics.svg" alt="Measured on agentmods" 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 | $0.00080 | $0.02490 |
| Opus 5 | $0.00040 | $0.01245 |
| Sonnet 5 | $0.00016 | $0.00498 |
| Haiku 4.5 | $0.00008 | $0.00249 |
Grade A, and why
pywayne-statistics 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 5d 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pywayne Statistics
Comprehensive statistical testing library for hypothesis testing, A/B testing, and data analysis.
Quick Start
from pywayne.statistics import NormalityTests, LocationTests
import numpy as np
# Test data normality
nt = NormalityTests()
data = np.random.normal(0, 1, 100)
result = nt.shapiro_wilk(data)
print(f"p-value: {result.p_value:.4f}, is_normal: {not result.reject_null}")
# Compare two groups
lt = LocationTests()
group_a = np.random.normal(100, 15, 50)
group_b = np.random.normal(105, 15, 50)
result = lt.two_sample_ttest(group_a, group_b)
print(f"Significant difference: {result.reject_null}")
Test Categories
NormalityTests (NormalityTests)
Test if data follows a normal distribution or other specified distributions.
| Method | Description | Use Case |
|---|---|---|
shapiro_wilk |
Shapiro-Wilk test | Small-medium samples (n ≤ 5000) |
ks_test_normal |
K-S normality test | Medium-large samples |
ks_test_two_sample |
Two-sample K-S test | Compare two sample distributions |
anderson_darling |
Anderson-Darling test | Tail-sensitive normality test |
dagostino_pearson |
D'Agostino-Pearson K² | Based on skewness and kurtosis |
jarque_bera |
Jarque-Bera test | Large samples, regression residuals |
chi_square_goodness_of_fit |
Chi-square goodness-of-fit | Categorical data |
lilliefors_test |
Lilliefors test | Unknown parameters K-S test |
Example:
from pywayne.statistics import NormalityTests
nt = NormalityTests()
result = nt.shapiro_wilk(data)
if result.p_value < 0.05:
print("Data is NOT normally distributed")
else:
print("Data follows normal distribution")
LocationTests (LocationTests)
Compare means or medians across groups (parametric and non-parametric).
| Method | Description | Use Case |
|---|---|---|
one_sample_ttest |
One-sample t-test | Compare sample mean to a value |
two_sample_ttest |
Two-sample t-test | Compare two independent group means |
paired_ttest |
Paired t-test | Compare before/after measurements |
one_way_anova |
One-way ANOVA | Compare 3+ group means |
mann_whitney_u |
Mann-Whitney U test | Non-parametric two-sample test |
wilcoxon_signed_rank |
Wilcoxon signed-rank | Non-parametric paired test |
kruskal_wallis |
Kruskal-Wallis H test | Non-parametric multi-group test |
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.
- 5d ago First seen · 310 lines · 80 tokens per session scan A 2cb70574292a
pywayne-statistics is a skill published in the GitHub repository wangyendt/wayne-skills (8 stars, last pushed 9d ago), licensed MIT. It adds 80 tokens to every session and 2,490 once invoked, about $0.0004 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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