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 zamushwani/biomedical-ai-skills --skill survival-analysisgit clone --depth 1 https://github.com/zamushwani/biomedical-ai-skillsWrote 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/zamushwani/biomedical-ai-skills/survival-analysis)<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/survival-analysis"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/survival-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/zamushwani/biomedical-ai-skills/survival-analysis"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/survival-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 | $0.00000 | $0.04698 |
| Opus 5 | $0.00000 | $0.02349 |
| Sonnet 5 | $0.00000 | $0.00940 |
| Haiku 4.5 | $0.00000 | $0.00470 |
Grade A, and why
survival-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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Survival Analysis
Time-to-event analysis for cancer clinical data. Covers Kaplan-Meier, Cox proportional hazards, competing risks, restricted mean survival time, and optimal cutpoint selection using the survival, ggsurvfit, tidycmprsk, and survRM2 packages.
When to Use This Skill
Activate when the user requests:
- Kaplan-Meier survival curves with risk tables
- Log-rank or stratified log-rank tests between groups
- Cox proportional hazards modeling (univariable or multivariable)
- Proportional hazards assumption checking
- Competing risks analysis (cause-specific or Fine-Gray)
- Restricted mean survival time (RMST) comparisons
- Optimal biomarker cutpoint selection for survival
- Forest plots for multivariate Cox models
Inputs
| Data Type | Format | Source |
|---|---|---|
| Clinical | Tabular (time, event, covariates) | TCGA GDC via TCGAbiolinks, cBioPortal |
| Time variable | Days/months to event or last follow-up | days_to_death, days_to_last_follow_up |
| Event indicator | Binary (0 = censored, 1 = event) | Derived from vital_status |
| Covariates | Categorical or continuous | Age, stage, gene expression, mutations |
Preparing TCGA Survival Data
library(TCGAbiolinks)
library(SummarizedExperiment)
query <- GDCquery(
project = "TCGA-GBM",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts"
)
GDCdownload(query, directory = "GDCdata")
se <- GDCprepare(query, directory = "GDCdata")
clinical <- as.data.frame(colData(se))
# Construct survival endpoint from TCGA clinical fields
# TCGA stores time in days. Convert to months for readability.
clinical$os_time <- ifelse(!is.na(clinical$days_to_death),
as.numeric(clinical$days_to_death),
as.numeric(clinical$days_to_last_follow_up))
clinical$os_event <- ifelse(clinical$vital_status == "Dead", 1, 0)
clinical$os_months <- clinical$os_time / 30.44
# Drop samples with missing survival data
clinical <- clinical[!is.na(clinical$os_months) & clinical$os_months > 0, ]
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.
- 12d ago First seen · 452 lines · 0 tokens per session scan A f4deb87031fc
survival-analysis is a skill published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 13d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,698 tokens. 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 2026-08-31.
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