Doctorg
This is a health research assistant that searches tiered medical sources and grades evidence strength using a GRADE-inspired scale. It has three depth modes: quick uses WebSearch for basic questions, deep adds Tavily for nuanced topics, and full includes Firecrawl for controversial subjects requiring primary sources. It prioritizes PubMed and Cochrane over Mayo Clinic over quality journalism, and pulls your Apple Health data to personalize answers unless you flag it not to. The evidence grading is the useful part here: claims get rated Strong, Moderate, Weak, Minimal, or Contested so you know when you're looking at meta-analyses versus expert opinion. Always disclaims it's not medical advice, which is the only responsible way to build something like this.
- skills.sh installs
- 264
- GitHub stars
- 412
- last updated
- Aug 21, 2026
Install
$ npx -y skills add glebis/claude-skills --skill doctorg --agent claude-codeInstalls into .claude/skills of the current project.
Files
Doctor G -- Evidence-Based Health Research
Answer health and wellness questions using only trusted, evidence-based sources with explicit evidence strength ratings.
Usage
# Quick answer (WebSearch only, ~30s)
/doctorg Is creatine safe for daily use?
# Deep research (WebSearch + Tavily, ~90s)
/doctorg --deep Huberman vs Attia on fasted training
# Full investigation (WebSearch + Tavily + Firecrawl, ~3min)
/doctorg --full What does current evidence say about GLP-1 agonists for non-diabetic weight loss?
# Without personal health context
/doctorg --no-personal Best stretching protocol for lower back pain
Depth Levels
| Level | Flag | Tools | Time | Use When |
|---|---|---|---|---|
| Quick | (default) | WebSearch | ~30s | Simple factual questions |
| Deep | --deep | WebSearch + Tavily | ~90s | Competing claims, nuanced topics |
| Full | --full | WebSearch + Tavily + Firecrawl | ~3min | Controversial topics, need primary sources |
How It Works
1. Parse Query & Detect Topic Category
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- Repository
- github.com/glebis/claude-skills
- Category
- Data Science & ML
- First seen
- Jun 3, 2026
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