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 PKU-YuanGroup/OpenAI4S --skill bio-clinical-biostatistics-categorical-testsgit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-clinical-biostatistics-categorical-tests)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clinical-biostatistics-categorical-tests"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-biostatistics-categorical-tests/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/pku-yuangroup/openai4s/bio-clinical-biostatistics-categorical-tests"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-biostatistics-categorical-tests.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 | $0.00086 | $0.06428 |
| Opus 5 | $0.00043 | $0.03214 |
| Sonnet 5 | $0.00017 | $0.01286 |
| Haiku 4.5 | $0.00009 | $0.00643 |
Grade A, and why
bio-clinical-biostatistics-categorical-tests 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 9d 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
100% identical to bio-clinical-biostatistics-categorical-tests — 12 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 — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: scipy 1.12+ (Boschloo and Barnard added in 1.7), statsmodels 0.14+, pingouin 0.5+, exact2x2 (R) 1.6+, pandas 2.1+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R packages cited for reference (exact2x2, Exact, ratesci): use
packageVersion()then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Categorical Association Tests for Clinical Data
"Test association between categorical variables" -> Determine whether treatment and a categorical clinical outcome are statistically independent (or that marginal proportions agree, for paired data) using a test calibrated to the design, the sample size, and the regulatory question.
Algorithmic Taxonomy
| Test | Design | Asymptotic / exact | Conditioning | Strength | Fails when |
|---|---|---|---|---|---|
| Pearson chi-square (no continuity correction) | Independent groups, any RxC | Asymptotic | None | Standard for n>=40 with all expected counts >=5; matches Miettinen-Nurminen score CI | Any expected cell <1; >20% of cells with expected <5 (Cochran 1954) |
| Fisher's exact (conditional) | Independent 2x2 | Exact | Conditions on BOTH margins | Exact small-sample guarantee on level | Conservative (true alpha << nominal); discards information by double conditioning (Mehta-Senchaudhuri 2003) |
| Boschloo's exact | Independent 2x2 | Exact unconditional | Conditions on ONE margin only | Uniformly more powerful than Fisher (Boschloo 1970; Mehta-Senchaudhuri 2003); preserves nominal alpha exactly | Computationally heavier; RxC extensions limited |
| Barnard's exact | Independent 2x2 | Exact unconditional | Conditions on ONE margin only | Maximises nuisance parameter; well-calibrated | Slightly less powerful than Boschloo on average; compute scales O(n^2) |
| CMH (Mantel-Haenszel) | Stratified independent groups | Asymptotic | Conditions within strata | Tests common-OR null across strata; pooled OR estimator | Assumes no qualitative interaction; misleading when ORs reverse direction across strata |
| Breslow-Day | Stratified independent groups | Asymptotic | Within strata | Tests homogeneity of stratum ORs | Underpowered with few strata or sparse strata; non-significance does NOT prove homogeneity |
| McNemar (asymptotic, no continuity correction) | Paired binary | Asymptotic | Conditions on discordant pairs | Fagerland 2013 default; outperforms exact conditional | Discordant pair count b+c < 25 (chi-square approximation breaks) |
| Mid-p McNemar | Paired binary | Quasi-exact | Discordant pairs | Fagerland-Lydersen-Laake 2013 recommended default; less conservative than exact conditional | Slight under-coverage tolerable at small b+c |
| Exact conditional McNemar (Liddell 1983) | Paired binary | Exact | Discordant pairs only | Guaranteed coverage | Over-conservative; loses power vs mid-p or unconditional |
| Suissa-Shuster exact unconditional | Paired binary | Exact unconditional | All N pairs | Uniformly more powerful than exact conditional McNemar; 20-40% smaller n for same power | Implementation only in R exact2x2::mcnemarExactDP and SAS macros |
What ships with it
2 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.
- 9d ago First seen · 319 lines · 86 tokens per session scan A 3258df6f4e4a
bio-clinical-biostatistics-categorical-tests is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 86 tokens to every session and 6,428 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bio-clinical-biostatistics-categorical-tests, differing in 12 lines, and is treated as a copy.
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