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 effect-measuresgit 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/effect-measures)<a href="https://agentmods.dev/skills/gptomics/bioskills/effect-measures"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/effect-measures/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/effect-measures"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/effect-measures.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 | $0.00098 | $0.07633 |
| Opus 5 | $0.00049 | $0.03816 |
| Sonnet 5 | $0.00020 | $0.01527 |
| Haiku 4.5 | $0.00010 | $0.00763 |
Grade A, and why
bio-clinical-biostatistics-effect-measures 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-effect-measures — 97% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 384 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+, numpy 1.26+, pandas 2.1+, matplotlib 3.8+, marginaleffects (Python) 0.0.13+ / (R) 0.20+. R packages cited: ratesci, exact2x2, marginaleffects, riskCommunicator, RobinCar.
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.
Treatment Effect Measures for Clinical Trials
"Compute treatment effect sizes" -> Estimate the population-level treatment contrast (OR, RR, RD, HR, NNT) with a confidence interval calibrated to sample size and a clear declaration of whether the estimand is marginal or conditional under ICH E9(R1).
Algorithmic Taxonomy
| Measure | Scale | Collapsible? | Best CI method | When to use | Fails when |
|---|---|---|---|---|---|
| OR | Log-odds ratio | NO (non-collapsible) | Profile likelihood; Wald acceptable for n>100 per arm | Case-control (only valid measure); logistic regression default | Outcome prevalence > 10% (OR overstates RR); Hauck-Donner pathology near boundary |
| RR | Log-risk ratio | YES | Miettinen-Nurminen score; MOVER-R | Cohort, RCT with common outcomes | Sparse strata; one or both p near 0 (Wald log-RR breaks) |
| RD (absolute risk difference) | Linear probability | YES | Newcombe-Wilson hybrid; Miettinen-Nurminen | Clinically interpretable absolute scale; FDA-preferred for binary | Predictions outside [0,1] from linear models |
| HR (hazard ratio) | Log-hazard ratio | NO | Wald with profile likelihood for small n | Time-to-event with PH | PH violation (see clinical-biostatistics/survival-analysis) |
| NNT/NNH | 1/RD | n/a (derived from RD) | Bender 2001 CCT 22:102 (Altman 1998 base) | Communicating absolute benefit to clinicians | RD CI crosses zero (NNT becomes NNTB-infinity-NNTH) |
| Difference in RMST | Time scale | YES | Wald with delta method; pseudo-observation regression | Time-to-event with PH violation | Different max follow-up across arms (truncation tau ambiguous) |
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 · 384 lines · 98 tokens per session scan A 86ae272c177c
bio-clinical-biostatistics-effect-measures is a skill published in the GitHub repository GPTomics/bioSkills (1,199 stars, last pushed 25d ago), licensed MIT. It adds 98 tokens to every session and 7,633 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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