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 j4flmao/agent-skills --skill statistical-analysisgit clone --depth 1 https://github.com/j4flmao/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/j4flmao/agent-skills/statistical-analysis)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/statistical-analysis"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/statistical-analysis.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 | $0.00103 | $0.05909 |
| Opus 5 | $0.00051 | $0.02955 |
| Sonnet 5 | $0.00021 | $0.01182 |
| Haiku 4.5 | $0.00010 | $0.00591 |
Grade A, and why
data-science-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 3d 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 — 596 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Analysis
Purpose
Perform rigorous statistical analysis: descriptive statistics, hypothesis testing, regression, time series, Bayesian inference, and reproducible reporting.
Agent Protocol
Trigger
Exact user phrases: "statistical analysis", "hypothesis test", "t-test", "ANOVA", "chi-square", "regression", "linear regression", "logistic regression", "time series", "ARIMA", "forecast", "Bayesian", "confidence interval", "p-value", "effect size", "power analysis", "data exploration", "descriptive statistics", "correlation", "outlier detection".
Input Context
- Analysis goal (exploration, inference, prediction, forecasting)
- Data structure (rows, columns, types, missingness)
- Question to answer: what decision depends on this analysis?
- Assumptions about data (independence, distribution, measurement scale)
- Audience: technical or business stakeholders
- Reproducibility requirements (notebook, script, report)
Output Artifact
Statistical analysis results with interpretation, assumptions checked, and business implications.
Response Format
## Analysis Summary
Question: {what was asked}
Data: {N rows, N features, time period}
Method: {test/model, software, parameters}
Results: {estimate, CI, p-value, effect size}
Assumptions Checked: {normality, independence, homoscedasticity}
Interpretation: {what the result means}
Business Implication: {what to do about it}
Completion Criteria
- Data explored and cleaned (missingness, outliers, distributions)
- Appropriate test/model selected based on data and question
- Assumptions checked and violations addressed
- Results reported with effect size and confidence interval
- Interpretation translated to business context
- Reproducible code/notebook provided
Workflow
Step 1: Data Exploration
Summarize data: shape, types, missing values, summary statistics. Visualize: univariate distributions (histograms, box plots), bivariate relationships (scatter plots, correlation heatmaps), multivariate structure (pair plots, PCA). Check data quality: range violations, duplicates, logical inconsistencies, timestamp ordering.
What ships with it
6 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.
- 3d ago First seen · 596 lines · 103 tokens per session scan A 0f3ac2302249
data-science-statistical-analysis is a skill published in the GitHub repository j4flmao/agent-skills (21 stars, last pushed yesterday), licensed MIT. It adds 103 tokens to every session and 5,909 once invoked, about $0.0005 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 2026-09-03.
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