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 LeoLin990405/r-analytics-skill --skill r-ml-survivalgit clone --depth 1 https://github.com/LeoLin990405/r-analytics-skillWrote 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/leolin990405/r-analytics-skill/r-ml-survival)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/r-ml-survival"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/r-ml-survival/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/leolin990405/r-analytics-skill/r-ml-survival"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/r-ml-survival.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 | $0.00027 | $0.00889 |
| Opus 5 | $0.00014 | $0.00445 |
| Sonnet 5 | $0.00005 | $0.00178 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
r-ml-survival scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
R Survival Analysis
Time-to-event analysis.
survival Package
library(survival)
# Create survival object
surv_obj <- Surv(time = df$time, event = df$status)
# Kaplan-Meier estimate
km_fit <- survfit(surv_obj ~ 1)
summary(km_fit)
plot(km_fit)
# KM by group
km_fit <- survfit(surv_obj ~ group, data = df)
plot(km_fit, col = 1:2)
# Median survival
km_fit
# Cox proportional hazards
cox_model <- coxph(Surv(time, status) ~ age + sex + treatment, data = df)
summary(cox_model)
# Hazard ratios
exp(coef(cox_model))
exp(confint(cox_model))
# Test proportional hazards assumption
cox.zph(cox_model)
plot(cox.zph(cox_model))
# Stratified Cox
cox_model <- coxph(Surv(time, status) ~ age + strata(sex), data = df)
survminer (Visualization)
library(survminer)
# Kaplan-Meier plot
km_fit <- survfit(Surv(time, status) ~ group, data = df)
ggsurvplot(km_fit,
data = df,
pval = TRUE,
conf.int = TRUE,
risk.table = TRUE,
risk.table.col = "strata",
ggtheme = theme_minimal(),
palette = c("#E7B800", "#2E9FDF")
)
# Customization
ggsurvplot(km_fit,
data = df,
pval = TRUE,
pval.method = TRUE,
log.rank.weights = "1",
surv.median.line = "hv",
legend.title = "Group",
legend.labs = c("Control", "Treatment"),
xlab = "Time (months)",
ylab = "Survival probability",
break.time.by = 12,
xlim = c(0, 60)
)
# Forest plot for Cox model
ggforest(cox_model, data = df)
# Cumulative hazard
ggsurvplot(km_fit, fun = "cumhaz")
# Event plot
ggsurvplot(km_fit, fun = "event")
Parametric Models
library(survival)
# Weibull
weibull_model <- survreg(Surv(time, status) ~ age + sex, data = df, dist = "weibull")
summary(weibull_model)
# Exponential
exp_model <- survreg(Surv(time, status) ~ age + sex, data = df, dist = "exponential")
# Log-normal
lognorm_model <- survreg(Surv(time, status) ~ age + sex, data = df, dist = "lognormal")
# Predictions
predict(weibull_model, type = "response") # Expected time
predict(weibull_model, type = "quantile", p = 0.5) # Median
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 · 149 lines · 27 tokens per session scan A 5a6a0d1b6cad
r-ml-survival is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 6mo ago), licensed MIT. It adds 27 tokens to every session and 889 once invoked, about $0.0001 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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