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 dralkh/seerai --skill scikit-survivalgit clone --depth 1 https://github.com/dralkh/seeraiWrote 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/dralkh/seerai/scikit-survival)<a href="https://agentmods.dev/skills/dralkh/seerai/scikit-survival"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/scikit-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/dralkh/seerai/scikit-survival"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/scikit-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 | $0.00091 | $0.03616 |
| Opus 5.5 | $0.00036 | $0.01446 |
| Sonnet 5.5 | $0.00018 | $0.00723 |
| Haiku 4.5 | $0.00009 | $0.00362 |
Grade A, and why
scikit-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 1mo 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
92% identical to scikit-survival — 10 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 — 397 lines — stays where its author put it; the contents beside it link to each section on GitHub.
scikit-survival: Survival Analysis in Python
Overview
scikit-survival is a Python library for survival analysis built on top of scikit-learn. It provides specialized tools for time-to-event analysis, handling the unique challenge of censored data where some observations are only partially known.
Survival analysis aims to establish connections between covariates and the time of an event, accounting for censored records (particularly right-censored data from studies where participants don't experience events during observation periods).
When to Use This Skill
Use this skill when:
- Performing survival analysis or time-to-event modeling
- Working with censored data (right-censored, left-censored, or interval-censored)
- Fitting Cox proportional hazards models (standard or penalized)
- Building ensemble survival models (Random Survival Forests, Gradient Boosting)
- Training Survival Support Vector Machines
- Evaluating survival model performance (concordance index, Brier score, time-dependent AUC)
- Estimating Kaplan-Meier or Nelson-Aalen curves
- Analyzing competing risks
- Preprocessing survival data or handling missing values in survival datasets
- Conducting any analysis using the scikit-survival library
Core Capabilities
1. Model Types and Selection
scikit-survival provides multiple model families, each suited for different scenarios:
Cox Proportional Hazards Models
Use for: Standard survival analysis with interpretable coefficients
CoxPHSurvivalAnalysis: Basic Cox modelCoxnetSurvivalAnalysis: Penalized Cox with elastic net for high-dimensional dataIPCRidge: Ridge regression for accelerated failure time models
See: references/cox-models.md for detailed guidance on Cox models, regularization, and interpretation
Ensemble Methods
Use for: High predictive performance with complex non-linear relationships
RandomSurvivalForest: Robust, non-parametric ensemble methodGradientBoostingSurvivalAnalysis: Tree-based boosting for maximum performanceComponentwiseGradientBoostingSurvivalAnalysis: Linear boosting with feature selectionExtraSurvivalTrees: Extremely randomized trees for additional regularization
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.
- 1mo ago First seen · 397 lines · 91 tokens per session scan A 6bda7fa1ee40
scikit-survival is a skill published in the GitHub repository dralkh/seerai (94 stars, last pushed 19d ago), licensed MIT. It adds 91 tokens to every session and 3,616 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to scikit-survival, differing in 10 lines, and is treated as a copy.
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