Epidemiology

Community

beita6969

epidemiology

Performs epidemiological analyses including disease modeling (SIR/SEIR), outbreak investigation, risk factor identification, incidence/prevalence estimation, and causal inference from observational data; trigger when users discuss disease spread, public health data, or population-level health patterns.

Overview

Publisher	beita6969
Repository	`ScienceClaw`
Skill name	`epidemiology`
Stars	950
Forks	106
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by beita6969 on GitHub. Read the source before you install it.

Installation

Install the Epidemiology AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/beita6969/ScienceClaw/tree/main/skills/epidemiology
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/beita6969/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/skills/epidemiology .claude/skills/epidemiology

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Epidemiology in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Epidemiology on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Epidemiology is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

When to Trigger

Activate this skill when the user mentions:

SIR, SEIR, compartmental models, R0, reproduction number
Outbreak investigation, contact tracing, epidemic curves
Incidence, prevalence, mortality rates, case-fatality ratio
Risk factors, odds ratio, relative risk, hazard ratio
Cohort studies, case-control studies, cross-sectional surveys
DAGs (directed acyclic graphs), causal inference, confounding
Vaccine efficacy, herd immunity, attack rate

Step-by-Step Methodology

Define the epidemiological question - Specify the disease/condition, population, time period, and geographic scope. Determine if descriptive, analytic, or modeling approach is needed.
Data characterization - Identify data source (surveillance, registry, survey). Assess case definitions (confirmed, probable, suspected). Check completeness and reporting biases.
Descriptive epidemiology - Characterize by person (age, sex, demographics), place (geographic distribution, mapping), and time (epidemic curves, secular trends, seasonality).
Measure calculation - Compute incidence rate (person-time denominator), prevalence (point or period), attack rate, case-fatality ratio. Report with 95% confidence intervals.
Analytic methods - For causal questions: draw a DAG to identify confounders and colliders. Use appropriate regression (logistic for OR, Poisson/negative binomial for rates, Cox for time-to-event). Apply propensity score methods if needed.
Disease modeling - Build SIR/SEIR compartmental models. Estimate R0 from early epidemic growth rate or next-generation matrix. Conduct sensitivity analysis on key parameters (transmission rate, recovery rate, latent period).
Interpretation and communication - Translate findings into public health actions. Present results with absolute and relative measures. Discuss Hills criteria for causation assessment.

Key Databases and Tools

WHO Global Health Observatory - International health statistics
CDC WONDER / MMWR - US disease surveillance data
Our World in Data - Pandemic and health metrics
GBD (Global Burden of Disease) - Comprehensive disease burden estimates
EpiEstim / R0 package - R0 estimation tools
DAGitty - DAG drawing and analysis

Output Format

Epidemic curves with proper time axis (onset date, not report date when possible).
Measures of association as tables: measure, point estimate, 95% CI, p-value.
Compartmental model diagrams with parameter definitions and values.
Geographic maps with rates (not raw counts) and appropriate denominators.

Quality Checklist

Case definition explicitly stated
Denominators appropriate (person-time for rates, population for prevalence)
Confidence intervals provided for all estimates
Confounders identified via DAG and adjusted for
Selection bias and information bias discussed
Model assumptions stated and sensitivity analysis performed
Absolute and relative measures both reported
Temporal relationship between exposure and outcome verified

Frequently asked questions

What does the Epidemiology AI skill do?

Why use Epidemiology on TypingMind?

Because you install it once and use it with any model. Epidemiology is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Epidemiology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/epidemiology. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Epidemiology?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Epidemiology?

As many as you like. As long as a model supports skills, you can use Epidemiology with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Epidemiology AI skill free?

Yes. It is published on GitHub by beita6969 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Landing Report

garrytan

CommunityPopular

Read-only queue dashboard for workspace-aware ship. (gstack)

Gstack Openclaw Ceo Review

garrytan

CommunityPopular

Use when asked to review a plan, challenge a proposal, run a CEO review, poke holes in an approach, think bigger about scope, or decide whether to expand or reduce the plan.

Caveman Stats

Show recorded output and cache-read token usage and mode attribution for the current Claude Code session, or locate the host's native usage report. Trigger: /caveman-stats.

Ci Metrics

Query PyTorch CI, GitHub Actions, HUD, Grafana, and infrastructure metrics. Use when users ask about CI duration, job failures, queue times, workflow trends, runner health, dashboard data, or PyTorch…

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to Trigger

Step-by-Step Methodology

Key Databases and Tools

Output Format

Quality Checklist

Frequently asked questions

Related AI Skills

Set up your own AI workspace now