A professional-grade Evidence-Based Medicine tool for calculating critical clinical diagnostic metrics and biostatistics.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ebm-calculator
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ebm-calculator using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is EBM Calculator?
The EBM Calculator is a specialized tool within the Openclaw Skills ecosystem designed for medical professionals, researchers, and students. It streamlines the complex process of evaluating diagnostic test accuracy and clinical trial results by automating biostatistical formulas. By integrating this skill into your workflow, you can quickly derive clinical significance from raw data without manual calculation errors.
This utility focuses on accuracy and speed, providing essential data for clinical reasoning. Whether you are analyzing a research paper or evaluating a new diagnostic test in a clinical setting, this Openclaw Skills contribution ensures your conclusions are backed by rigorous mathematical evidence.
EBM Calculator Use Cases
- Analyzing diagnostic test performance using true/false positive and negative counts.
- Determining the clinical impact of an intervention by calculating the Number Needed to Treat (NNT).
- Adjusting Predictive Values (PPV/NPV) based on specific population prevalence.
- Converting pre-test probabilities to post-test probabilities using Likelihood Ratios.
- Educating medical students and residents on biostatistics and clinical decision-making frameworks.
How EBM Calculator Works
- The user selects a calculation mode such as diagnostic, nnt, or probability via the CLI.
- Input parameters like true positives, false negatives, or event rates are passed to the script.
- The skill processes the raw numerical data using standardized Evidence-Based Medicine formulas.
- It calculates secondary metrics including sensitivity, specificity, and likelihood ratios.
- The tool generates a structured JSON response containing the results and a brief clinical interpretation.
- Results are either displayed in the terminal or saved to a designated output file in the workspace.
EBM Calculator Setup
To get started with this tool from the Openclaw Skills collection, ensure you have a functional Python environment. No external dependencies are required.
Clone the repository and run the script directly:
# Example of running a diagnostic calculation
python ebm_calc.py --mode diagnostic --tp 80 --fn 20 --tn 90 --fp 10 --prevalence 0.1
# Example of calculating Number Needed to Treat (NNT)
python ebm_calc.py --mode nnt --control-rate 0.25 --experimental-rate 0.15
EBM Calculator Data Schema & Taxonomy
The skill produces a structured JSON output to ensure compatibility with other automated workflows. Below is the schema for diagnostic results:
| Field | Type | Description |
|---|---|---|
| sensitivity | float | The probability that a test is positive, given that the person has the disease. |
| specificity | float | The probability that a test is negative, given that the person does not have the disease. |
| ppv | float | Positive Predictive Value adjusted for prevalence. |
| npv | float | Negative Predictive Value adjusted for prevalence. |
| lr_positive | float | Likelihood ratio for a positive test result. |
| interpretation | string | A plain-text summary of the clinical findings. |
EBM Calculator Advanced Features
- Prevalence adjustment for predictive values to tailor results to specific clinical environments.
- Multi-mode execution supporting diagnostic metrics, treatment effects (NNT), and probability conversions.
- Structured JSON output for seamless integration with larger medical data pipelines or AI agents.
- Automated interpretation strings to provide immediate clinical context to raw numbers.
- File-based output options for logging and auditing clinical research data.
SKILL.md
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