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 agentlas-ai/agentlas-desktop --skill statistical-analysisgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/statistical-analysis)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/statistical-analysis"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/statistical-analysis"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/statistical-analysis.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 | $0.00111 | $0.05478 |
| Opus 5.5 | $0.00044 | $0.02191 |
| Sonnet 5.5 | $0.00022 | $0.01096 |
| Haiku 4.5 | $0.00011 | $0.00548 |
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.
This is a copy
97% identical to statistical-analysis — 142 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 464 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Analysis
Overview
Conduct hypothesis tests (t-tests, ANOVA, chi-square), regression, correlation, and Bayesian analyses with systematic assumption checking, effect sizes, and APA-style reporting. The goal is an analysis a reviewer could not tear apart: the right test, verified assumptions, honest effect sizes, and a complete write-up.
When to Use This Skill
Use this skill when:
- Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square, non-parametric)
- Performing regression or correlation analyses
- Running Bayesian statistical analyses
- Checking statistical assumptions and diagnostics
- Calculating effect sizes and conducting power analyses
- Reporting statistical results in APA format
- Analyzing experimental or observational data for research
Installation
Use uv to install the libraries used in this skill. Pin versions in production; unpinned installs are fine for exploration.
# Core frequentist stack (Python 3.10+; 3.12+ recommended for latest SciPy/ArviZ)
uv pip install "pingouin>=0.6" "scipy>=1.11" "statsmodels>=0.14.6" pandas matplotlib seaborn
# Bayesian modeling (PyMC 5 + ArviZ)
uv pip install "pymc>=5.0" "arviz>=1.0"
Compatibility notes (verified against pingouin 0.6.1, statsmodels 0.14.6, arviz 1.2, 2026):
- Pingouin 0.6.0 renamed output columns to remove special characters:
p_val,cohen_d,CI95,p_unc(previouslyp-val,cohen-d,CI95%,p-uncin 0.5.x). Examples below use the current names; if stuck on 0.5.x, use the hyphenated forms. - statsmodels + SciPy: use
statsmodels>=0.14.6withscipy>=1.11to avoid_lazywhereimport errors on SciPy 1.16+. - ArviZ 1.x:
az.summary()now defaults to 89% intervals (eti89columns) and the width parameter isci_prob(nothdi_prob). To report a conventional 95% credible interval, passaz.summary(trace, ci_prob=0.95). - One-sided Bayes Factors are gone from Pingouin:
pg.ttest(..., alternative='greater')silently drops theBF10column, andpg.bayesfactor_ttestraises on one-sided alternatives. For one-sided Bayesian tests, use PyMC directly (compute the posterior probability of the directional hypothesis) or JASP/R's BayesFactor.
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.
- 1mo ago First seen · 464 lines · 111 tokens per session scan A dc597139f1fb
statistical-analysis is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 111 tokens to every session and 5,478 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to statistical-analysis, differing in 142 lines, and is treated as a copy.
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