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 winstonkoh87/Athena-Public --skill statistical-analysisgit clone --depth 1 https://github.com/winstonkoh87/Athena-PublicWrote 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/winstonkoh87/athena-public/statistical-analysis)<a href="https://agentmods.dev/skills/winstonkoh87/athena-public/statistical-analysis"><img src="https://agentmods.dev/badge/skills/winstonkoh87/athena-public/statistical-analysis/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/winstonkoh87/athena-public/statistical-analysis"><img src="https://agentmods.dev/badge/skills/winstonkoh87/athena-public/statistical-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.01448 |
| Opus 5.5 | $0.00015 | $0.00579 |
| Sonnet 5.5 | $0.00007 | $0.00290 |
| Haiku 4.5 | $0.00004 | $0.00145 |
Grade A, and why
statistical-analysis 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Analysis Skill
Purpose: Structured pipeline for statistical analysis deliverables. Prevents assumption violations, missed effect sizes, and uninterpretable output. Origin: Created ahead of a client SPSS assignment. No protocol coverage existed for this domain.
The 5-Step Pipeline
Step 1: DATA AUDIT
- Load dataset (CSV, SPSS .sav, Excel)
- Profile: N, variable types (nominal/ordinal/interval/ratio), missing data %, outliers
- Check for:
- Missing data pattern (MCAR/MAR/MNAR) — Little's MCAR test if available
- Outliers (z-score > 3 or IQR method)
- Variable coding (reverse-coded items, string-to-numeric conversion)
- Sample size adequacy per planned test (rule of thumb: 10–15 observations per predictor for regression)
Step 2: ASSUMPTION MATRIX
[!IMPORTANT] Every statistical test has assumptions. Violating them invalidates results. Check BEFORE running.
| Test Family | Assumptions | Check Method |
|---|---|---|
| Reliability (Cronbach's α) | Unidimensionality, interval/ratio data, ≥3 items per scale | Factor analysis / item-total correlations |
| Chi-Square (χ²) | Independence, expected frequency ≥ 5 in 80%+ cells, categorical variables | Expected frequency table |
| Pearson Correlation | Linearity, normality (both vars), no significant outliers, interval/ratio | Scatter plot, Shapiro-Wilk |
| Spearman Correlation | Monotonic relationship, ordinal or non-normal interval | Scatter plot (monotonic check) |
| Multiple Regression | Linearity, independence (Durbin-Watson), homoscedasticity, normality of residuals, no multicollinearity (VIF < 10) | Residual plots, VIF table, Durbin-Watson |
| Independent t-test | Normality, homogeneity of variance (Levene's), interval/ratio DV | Shapiro-Wilk, Levene's |
| One-way ANOVA | Normality, homogeneity (Levene's), independence, interval/ratio DV | Same as t-test + post-hoc if significant |
Step 3: TEST EXECUTION
For each test in the scope:
- State the hypothesis (H₀ and H₁) explicitly
- Run the test — output test statistic, df, p-value, effect size
- Effect size (mandatory — p-value alone is insufficient):
- Cohen's d (t-test)
- η² or partial η² (ANOVA)
- r or R² (correlation/regression)
- Cramér's V (chi-square)
- Cronbach's α (reliability — this IS the effect)
- Decision: Reject/Fail to reject H₀ at α = 0.05 (unless specified otherwise)
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 · 127 lines · 37 tokens per session scan A 11b58c2493ff
statistical-analysis is a skill published in the GitHub repository winstonkoh87/Athena-Public (619 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 1,448 once invoked, about $0.0001 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 2026-08-30.
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