Best use case
clinical is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Teams using clinical should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/clinical/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How clinical Compares
| Feature / Agent | clinical | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
This skill provides specific capabilities for your AI agent. See the About section for full details.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Clinical Research ## Overview Clinical study design, statistical analysis, and regulatory compliance for medical research. ## Study Designs | Design | Level of Evidence | Best For | |--------|------------------|----------| | RCT | I | Treatment efficacy | | Cohort (prospective) | II | Risk factors, prognosis | | Cohort (retrospective) | III | Exposure-outcome associations | | Case-control | III | Rare diseases, risk factors | | Cross-sectional | IV | Prevalence, correlations | | Case report/series | V | Novel observations | ## Common Analyses - **Survival analysis**: Kaplan-Meier curves, Log-rank test, Cox regression - **Diagnostic accuracy**: Sensitivity, specificity, ROC curve, AUC - **Meta-analysis**: Fixed/random effects, forest plot, heterogeneity (I-squared) - **Propensity score matching**: For observational study confounding - **Nomogram**: Predictive model visualization for clinical use ## Sample Size Calculation - Define: alpha (usually 0.05), power (usually 0.80), effect size, outcome type - Tools: G*Power, R `pwr` package, Python `statsmodels` - Report: formula used, assumptions, expected dropout rate ## Regulatory Compliance - **IRB/Ethics committee**: Required for all human subjects research - **Informed consent**: Written, voluntary, comprehensive - **ClinicalTrials.gov**: Registration before enrollment (ICMJE requirement) - **GDPR/HIPAA**: Data privacy for patient information - **GCP (Good Clinical Practice)**: ICH E6 guidelines ## Reporting Guidelines | Guideline | Study Type | |-----------|-----------| | CONSORT | Randomized controlled trials | | STROBE | Observational studies | | PRISMA | Systematic reviews / meta-analyses | | STARD | Diagnostic accuracy | | TRIPOD | Prediction models | | SPIRIT | Study protocols |
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