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Survival Analysis

Checked
aj-geddes·vlatest·other·MIT

Predicts time until events occur based on data, enabling proactive risk mitigation and optimized resource allocation.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it Survival Analysis, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill "Survival Analysis" -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

aj-geddes

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned aj-geddes/useful-ai-prompts at 3f5182cfd7.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3f5182cfd7
Manifest
skills/survival-analysis/SKILL.md
Scanned
3 files, 322 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to perform survival analysis, a statistical method used to analyze the expected duration of time until one or more events happen, such as patient death or machine failure. It specifically handles censored data where the event of interest has not yet occurred for some subjects within the observation period.

When to use it

  • Analyzing time-to-event data in clinical trials where patient follow-up periods vary.
  • Predicting equipment maintenance schedules based on historical failure logs with incomplete records.
  • Evaluating customer churn risks when exit dates are unknown for active accounts.
  • Assessing reliability engineering metrics for systems under continuous monitoring.

Key capabilities

  • Processing right-censored, left-censored, and interval-censored datasets.
  • Calculating Kaplan-Meier survival curves to estimate survival probabilities over time.
  • Performing Cox proportional hazards regression to identify factors influencing event occurrence.
  • Generating hazard rate plots to visualize risk changes throughout the observation window.

Example prompts

"Analyze this dataset of server crash times, treating servers still running at the end of the month as censored data." "Calculate the median survival time and generate a Kaplan-Meier curve for patients in Group A versus Group B." "Run a Cox regression to determine which variables significantly increase the hazard rate of customer churn in this log."

Tips & gotchas

Ensure your dataset explicitly marks observations as 'event occurred' or 'censored' before analysis begins. Standard mean calculations will yield incorrect results if censored data is not handled specifically by survival functions.