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 GPTomics/bioSkills --skill logistic-regressiongit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/logistic-regression)<a href="https://agentmods.dev/skills/gptomics/bioskills/logistic-regression"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/logistic-regression/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/gptomics/bioskills/logistic-regression"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/logistic-regression.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 | $0.00100 | $0.07334 |
| Opus 5 | $0.00050 | $0.03667 |
| Sonnet 5 | $0.00020 | $0.01467 |
| Haiku 4.5 | $0.00010 | $0.00733 |
Grade A, and why
bio-clinical-biostatistics-logistic-regression 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- bio-clinical-biostatistics-logistic-regression — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: statsmodels 0.14+, scipy 1.12+, numpy 1.26+, pandas 2.1+, firthmodels 0.3+, marginaleffects 0.0.13+ (Python) / 0.20+ (R). R packages cited: RobinCar, marginaleffects, brant, MASS, VGAM.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_name
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Logistic Regression for Clinical Outcomes
"Model clinical outcomes with logistic regression" -> Estimate the marginal or conditional treatment effect on a binary or ordinal endpoint using a model that respects randomisation stratification, declares its estimand, and survives covariate misspecification.
Conditional vs marginal (the non-collapsibility subtlety): the OR is non-collapsible. The conditional OR from logistic regression is a different parameter than the marginal OR, even when there is NO confounding and randomisation is perfect. This is mathematical, not statistical bias. FDA 2023 favours marginal RD (via g-computation) for primary reporting to avoid parameter ambiguity. See clinical-biostatistics/effect-measures and Permutt 2020.
Algorithmic Taxonomy
| Approach | Estimand | Inference | Strength | Fails when |
|---|---|---|---|---|
| Unadjusted logistic / chi-square | Marginal OR | Wald or LR | Simple; transparent | Loses efficiency vs adjusted (Senn 2013); inflates SE under stratified randomisation (Kahan-Morris 2012) |
| Logistic with covariates (ML, Wald CI) | Conditional log-OR | Wald | Standard; widely available | Conditional OR != marginal OR due to non-collapsibility (Permutt 2020); not the FDA 2023 primary estimand |
| Logistic + g-computation / standardisation | Marginal RD/RR/OR | Influence function or bootstrap SE | FDA 2023 recommended primary estimand for binary | Requires correct outcome model AND post-fit standardisation; needs robust SE machinery |
| Targeted Maximum Likelihood (TMLE) | Marginal RD/RR/OR | Influence function | Provably efficient; doubly robust in observational | Implementation heavier; mostly R (tmle, tmle3); rare in confirmatory submissions |
| Modified Poisson with sandwich SE | Marginal RR | HC1/HC3 sandwich | Direct RR estimation when prevalence >10% | Slightly less efficient than log-binomial when log-binomial converges |
| Log-binomial regression | Marginal RR | Wald | Direct RR estimation | Frequent convergence failure when predicted risk near 1 |
| Firth penalised logistic | Conditional OR (penalised) | Penalised LR test preferred | Handles separation, rare events (<5% prevalence) | Wald CI/p liberal; must use PLR test (Heinze-Schemper 2002) |
| Ordinal logistic (proportional odds) | Common conditional OR across cut-points | Wald or LR | Preserves ordering information | Proportional odds assumption violation (Brant test) |
| Partial proportional odds | PO holds for some covariates, not others | Hybrid | Salvages ordinal model when PO fails for one predictor | Increased complexity; harder interpretation |
| Multinomial logistic | Per-category log-OR | Wald | No PO assumption needed | Loses efficiency; harder communication |
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.
- 6d ago First seen · 374 lines · 100 tokens per session scan A 969c31d99374
bio-clinical-biostatistics-logistic-regression is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 100 tokens to every session and 7,334 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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