doctorg

Synthesize health evidence from tiered sources with explicit strength ratings.

345|52|Updated Oct 25, 2025
One-click install
npx skills add https://github.com/glebis/claude-skills --skill doctorg
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: doctorg
Source: https://github.com/glebis/claude-skills/tree/main/doctorg
Command: npx skills add https://github.com/glebis/claude-skills --skill doctorg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Synthesize evidence for health questions from tiered sources with explicit strength ratings.

Core Features & Use Cases

  • Evidence grading with GRADE-inspired strength ratings across nutrition, exercise, sleep, and wellness topics.
  • Apple Health integration for personalized context when user data is available.
  • Depth-aware research modes (Quick, Deep, Full) with progressively exhaustive source gathering.
  • Structured output with sources, caveats, and actionable recommendations.
  • Integrates with health-data, tavily-search, firecrawl-research, and fact-check workflows for end-to-end health research.

Quick Start

Ask a health question and optionally enable depth with --deep or --full to get an evidence-based answer.

Frequently Asked Questions about doctorg

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I get evidence-based health research with strength ratings for nutrition and exercise questions?

Evidence-based health research applies GRADE-inspired strength ratings to tiered clinical sources, synthesizing findings for nutrition, exercise, sleep, and wellness questions into structured output with explicit source citations and limitations.

What's the best way to research health questions using different depth levels?

Research health questions using Quick, Deep, or Full modes to control source gathering exhaustiveness. Quick provides fast answers, while Deep and Full modes progressively increase the breadth and depth of clinical source synthesis.

Can I use Apple Health data to personalize evidence-based health research?

Apple Health integration personalizes evidence-based research by incorporating your available personal health data as context, tailoring the synthesized clinical evidence and actionable recommendations to your specific wellness profile.

How does evidence grading work for health and wellness research?

Evidence grading uses GRADE-inspired methodology to assign explicit strength ratings to findings from tiered clinical sources, helping you distinguish strong evidence from weaker claims when evaluating nutrition or exercise research.

Do I need specific search tools to gather clinical sources for health research?

The skill integrates with tavily-search, firecrawl-research, health-data, and fact-check workflows to gather and validate tiered clinical sources, requiring no external tool setup to produce synthesized evidence-based answers.

What limitations should I expect when synthesizing evidence for health questions?

Structured output explicitly includes limitations and disclaimers alongside sources and recommendations, ensuring transparency about evidence quality gaps and the boundaries of applying synthesized clinical findings to personal health decisions.