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-trial-reportinggit 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-trial-reporting)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clinical-biostatistics-trial-reporting"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-biostatistics-trial-reporting/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-trial-reporting"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-biostatistics-trial-reporting.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 | $0.00142 | $0.09464 |
| Opus 5 | $0.00071 | $0.04732 |
| Sonnet 5 | $0.00028 | $0.01893 |
| Haiku 4.5 | $0.00014 | $0.00946 |
Grade A, and why
bio-clinical-biostatistics-trial-reporting 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-trial-reporting — 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 — 478 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: tableone 0.9+, statsmodels 0.14+, scikit-learn 1.4+, pandas 2.1+, numpy 1.26+. R packages cited (essential for current regulatory work): mmrm 0.3+ (Roche/openpharma), rbmi 1.5+ (Roche/Bayer via insightsengineering), gMCP, RBesT.
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.
Trial Reporting Under CONSORT 2025 + ICH E9(R1)
"Prepare a clinical trial statistical report" -> Define the estimand explicitly per ICH E9(R1); execute a covariate-adjusted primary analysis targeting the right summary measure; pre-specify the missing-data strategy and run regulatory-grade sensitivity analyses; structure the output per CONSORT 2025 and the new SPIRIT 2025 alignment.
The Single Most Important Methodological Shift -- The Estimand Comes First
Kahan, Cro, Li, Harhay 2023 Am J Epidemiol 192:987 ("Eliminating Ambiguous Treatment Effects Using Estimands"): 98% of published trial reports do not describe what the reported treatment effect represents. 54% of trials: impossible to deduce the estimand from reported methods. In 74% of trials submitted for regulatory approval 1996-2017, "what-if" hypothetical effects were used but only 2 trials explained this.
The framework: ICH E9(R1) Addendum (November 2019, EMA effective 30 July 2020, FDA May 2021) defines an estimand as the precise specification of what is being estimated, via five attributes:
- Treatment condition -- what is being compared
- Population -- which patients
- Endpoint -- which variable
- Population-level summary measure -- mean diff, OR, HR, RD
- Intercurrent-event (ICE) handling strategy -- one of five
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 · 478 lines · 142 tokens per session scan A ca9cd0f6a208
bio-clinical-biostatistics-trial-reporting is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 142 tokens to every session and 9,464 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bio-clinical-biostatistics-trial-reporting, differing in 12 lines, and is treated as a copy.
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