Codeg is a collaborative workspace that brings multiple AI coding agents and their sessions into one searchable application. Developers use it to coordinate agents, delegate work, and review unattended coding tasks across desktop, server, Docker, and mobile clients. The catalogue entries are skills and instructions for workflows within Codeg.
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 xintaofei/codeg --skill statistical-analysisgit clone --depth 1 https://github.com/xintaofei/codegWrote 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/xintaofei/codeg/statistical-analysis)<a href="https://agentmods.dev/skills/xintaofei/codeg/statistical-analysis"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/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/xintaofei/codeg/statistical-analysis"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/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 | $0.05222 |
| Opus 5.5 | $0.00044 | $0.02089 |
| Sonnet 5.5 | $0.00022 | $0.01044 |
| Haiku 4.5 | $0.00011 | $0.00522 |
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
94% identical to statistical-analysis — 155 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 — 445 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 · 445 lines · 111 tokens per session scan A 159dcdebac52
statistical-analysis is a skill published in the GitHub repository xintaofei/codeg (4,087 stars, last pushed yesterday), licensed Apache-2.0. It adds 111 tokens to every session and 5,222 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to statistical-analysis, differing in 155 lines, and is treated as a copy.
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