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 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/survival)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/survival"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/survival.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.00980 |
| Opus 5 | $0.00013 | $0.00490 |
| Sonnet 5 | $0.00005 | $0.00196 |
| Haiku 4.5 | $0.00003 | $0.00098 |
Grade A, and why
survival scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
survival
Core survival analysis functions.
Survival Objects
library(survival)
# Create survival object
Surv(time, event)
Surv(time, time2, event) # Interval censoring
Surv(time, event, type = "right")
# Example
surv_obj <- Surv(df$time, df$status)
Kaplan-Meier Estimation
# Fit KM curve
km_fit <- survfit(Surv(time, status) ~ 1, data = df)
# By group
km_fit <- survfit(Surv(time, status) ~ group, data = df)
# Summary
summary(km_fit)
summary(km_fit, times = c(30, 60, 90))
# Median survival
km_fit
# Plot
plot(km_fit)
plot(km_fit, conf.int = TRUE)
Cox Proportional Hazards
# Fit Cox model
cox_fit <- coxph(Surv(time, status) ~ age + sex + treatment, data = df)
# Summary
summary(cox_fit)
# Hazard ratios
exp(coef(cox_fit))
exp(confint(cox_fit))
# Test proportional hazards
cox.zph(cox_fit)
plot(cox.zph(cox_fit))
Stratified Cox Model
# Stratify by variable
cox_fit <- coxph(Surv(time, status) ~ age + strata(sex), data = df)
# Multiple strata
cox_fit <- coxph(Surv(time, status) ~ age + strata(sex, center), data = df)
Time-Varying Covariates
# Create time-varying dataset
tvc_data <- tmerge(df, df, id = id,
tstop = time,
event = event(time, status))
# Add time-varying covariate
tvc_data <- tmerge(tvc_data, covariate_df, id = id,
trt = tdc(start_time, treatment))
# Fit model
cox_fit <- coxph(Surv(tstart, tstop, event) ~ trt + age, data = tvc_data)
Parametric Models
# Weibull
weibull_fit <- survreg(Surv(time, status) ~ age + sex,
data = df, dist = "weibull")
# Exponential
exp_fit <- survreg(Surv(time, status) ~ age + sex,
data = df, dist = "exponential")
# Log-normal
lnorm_fit <- survreg(Surv(time, status) ~ age + sex,
data = df, dist = "lognormal")
# Log-logistic
llog_fit <- survreg(Surv(time, status) ~ age + sex,
data = df, dist = "loglogistic")
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.
- 4d ago First seen · 168 lines · 26 tokens per session scan A be79c2b92f52
survival is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 980 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.
Other skills, from other repositories
bio-applied-bio-data-formats
Parse/write FASTA, FASTQ, SAM/BAM, VCF, BED, GFF/GTF with pysam and pure Python; decode SAM FLAG/CIGAR; reconcile 0-based vs 1-based coordinates. Use for custom format parsers or off-by-one coordinate bugs.
bio-applied-mageck-gene-essentiality
Run MAGeCK count/test on pooled CRISPR sgRNA screens, scoring gene essentiality via RRA, FDR, and log2 fold-change. Use when analyzing CRISPR screen FASTQ/count data, calling essential or drug-resistance genes, or benchmarking vs DepMap.
bio-applied-molecular-evolution
Test Hardy-Weinberg equilibrium, simulate Wright-Fisher drift/selection, and compute dN/dS, Tajima's D, and Fst with NumPy/SciPy. Use for neutral theory, molecular clock divergence time, selection scans, or effective population size (Ne) questions.
bio-applied-network-modules
Detect PPI/co-expression modules with NetworkX/python-louvain/leidenalg (Louvain, Leiden, modularity Q) and WGCNA eigengenes. Use when clustering a gene network, computing WGCNA modules, or testing DEG/pathway enrichment on network communities.
bio-applied-ribo-seq
Ribo-seq: cutadapt/bowtie2 adapter+rRNA removal, plastid P-site calibration, 3-nt periodicity QC, RiboCode/ribotricer ORF calling, translation efficiency. Use when user has ribosome profiling or footprint data.
bio-core-computational-genetics
Translate DNA per-frame, score codon usage bias (RSCU/CAI), simulate restriction digests/ORFs, and test three-point-cross mapping and Hardy-Weinberg equilibrium. Use for CAI, virtual digests, crossover mapping, or HWE tests.