Doctorg

How It Works

1. Parse Query & Detect Topic Category

Classify the question into one of:

• Nutrition/Supplements (examine.com gets priority)
• Exercise/Training (PubMed + ACSM get priority)
• Sleep (focus sleep-specific databases)
• Disease/Condition (condition-specific orgs + clinical guidelines)
• Medication/Treatment (FDA, EMA, Cochrane get priority)
• Mental Health (APA, mental health orgs)
• General Wellness (broad search across all tiers)

2. Search Evidence Sources (Tiered)

Search sources in priority order. See references/sources.md for complete domain list.

Tier 1 -- Primary Research (highest weight):

• PubMed/PMC, Cochrane Library, WHO, ClinicalTrials.gov

Tier 2 -- Clinical/Institutional (high weight):

• Mayo Clinic, Hopkins Medicine, Cleveland Clinic, Harvard Health
• Condition-specific: AHA, ACS, ADA, Alzheimer's Association

Tier 3 -- Expert Analysis (medium weight):

• Examine.com, STAT News, Health News Review
• Consensus.app, Epistemonikos

Tier 4 -- Quality Journalism (context/framing):

• The Atlantic, NYT, NPR, Guardian, FiveThirtyEight

Search Strategy by Depth

Quick (default):

WebSearch(query, allowed_domains=[Tier 1 + Tier 2 domains])
WebSearch(query + "systematic review OR meta-analysis", allowed_domains=[Tier 1])

Deep (--deep): All Quick searches PLUS:

tavily-search(query, include_domains=[Tier 1-3])
WebSearch(query + "expert opinion OR position statement", allowed_domains=[Tier 2-3])
WebSearch(query + "risks OR side effects OR contraindications")

Full (--full): All Deep searches PLUS:

firecrawl-research for top 2-3 most relevant results from Tier 1
WebSearch for competing/contrarian viewpoints
WebSearch(query + "retracted OR debunked OR misleading")

3. Pull Personal Health Context (unless --no-personal)

Query Apple Health database for relevant metrics:

python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json vitals
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json daily
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json sleep --days 7
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json workouts --days 30

Select ONLY metrics relevant to the query:

• Exercise question -> recent workouts, activity, resting HR, VO2 max
• Sleep question -> sleep data, HRV
• Nutrition question -> weight trends, activity level
• Heart question -> HR, HRV, resting HR, blood pressure

4. Synthesize with Evidence Grading

Rate each claim using simplified GRADE scale:

5. Format Output

# [Topic Title]

**Short answer**: [1-2 sentence direct answer]

## [Expert/Position A] (if comparing viewpoints)
- Key claim 1
- Key claim 2
- Has **evolved stance**: [if applicable]

## [Expert/Position B]
- Key claim 1
- Key claim 2

## Where They Actually Agree (if comparing)
- Agreement point 1
- Agreement point 2

## What Research Shows

| Claim | Evidence Strength |
|-------|------------------|
| Claim 1 | **Strong** |
| Claim 2 | **Weak** (reason) |
| Claim 3 | **Contested** |

## For You Specifically (if --personal context available)

[Personalized interpretation based on user's health data]

[Specific actionable recommendation]

## Sources
- [Source 1 title](url) -- Tier, year
- [Source 2 title](url) -- Tier, year

## Limitations
- [Any caveats about the evidence or this analysis]

Output rules:

• NEVER give medical diagnoses or replace professional advice
• ALWAYS include disclaimer: "This is research synthesis, not medical advice"
• When evidence is Weak or Minimal, explicitly say so
• When claims are Contested, present both sides fairly
• Prefer recent sources (last 5 years) over older ones
• Flag if key studies have been retracted or challenged
• Include the "For You Specifically" section only when health data adds meaningful context

6. Disclaimer (always append)

---
*Research synthesis, not medical advice. Consult a healthcare provider for personal decisions.*

Examples

Quick

/doctorg Is 10000 steps a day backed by science?

Deep (comparing experts)

/doctorg --deep Huberman vs Attia on fasted training

Full (controversial topic)

/doctorg --full Safety profile of long-term melatonin supplementation

Integration with Other Skills

• health-data: Pulls Apple Health metrics for personalization
• tavily-search: Deep research at Tier 1-3 sources
• firecrawl-research: Full-text extraction from primary sources
• fact-checker: Can be chained for verification of specific claims