255 Instructions 843f3544

Doctor G -- Implementation Instructions

Invocation

/doctorg [--deep|--full] [--no-personal] <question>

Parse the argument to extract:

• depth: "quick" (default), "deep" (--deep), or "full" (--full)
• personal: true (default), false (--no-personal)
• question: everything after flags

Step-by-Step Execution

Step 1: Classify the Question

Determine question type to select source priorities:

Pattern matching:

• Contains supplement/vitamin/mineral names -> nutrition_supplements
• Contains exercise/training/workout/lifting -> exercise_training
• Contains sleep/insomnia/circadian -> sleep
• Contains heart/cardio/blood pressure -> cardiovascular
• Contains cancer/tumor/oncology -> oncology
• Contains diabetes/insulin/glucose/blood sugar -> diabetes
• Contains anxiety/depression/mental/therapy -> mental_health
• Contains drug/medication/prescription -> medication
• Contains weight/fat loss/body comp/BMI -> body_composition
• Contains "vs" or "versus" or "compared to" -> expert_comparison (add flag)
• Default -> general_wellness

Expert comparison detection: If question contains "vs", "versus", or names two known health figures (Huberman, Attia, Rhonda Patrick, Greger, etc.), set is_comparison = true.

Step 2: Build Search Queries

For each depth level, construct search queries.

All levels -- base queries:

queries = [
    f"{question}",
    f"{question} systematic review OR meta-analysis",
]

Deep -- additional queries:

queries += [
    f"{question} risks OR side effects OR limitations",
    f"{question} expert consensus OR position statement",
    f"{question} evidence strength",
]

Full -- additional queries:

queries += [
    f"{question} retracted OR debunked OR criticism",
    f"{question} recent 2025 2026 update",
    f"{question} contrarian view OR counterargument",
]

Step 3: Execute Searches

Quick depth:

1. WebSearch(query=base_query, allowed_domains=[Tier1 + Tier2 + topic-specific Tier3])
2. WebSearch(query=systematic_review_query, allowed_domains=[Tier1])

Deep depth (run searches in parallel where possible):

1. All Quick searches
2. WebSearch(query=risks_query, allowed_domains=[Tier1 + Tier2])
3. WebSearch(query=expert_query, allowed_domains=[Tier2 + Tier3])
4. Use tavily-search skill: tavily-search "{question}" --include-domains {Tier1+Tier2+Tier3 comma-separated}
5. If is_comparison: search each expert's name + topic separately

Full depth:

1. All Deep searches
2. WebSearch(query=contrarian_query) (no domain filter -- find opposing views)
3. WebSearch(query=retraction_query, allowed_domains=[Tier1])
4. Use firecrawl-research to extract full text from top 2-3 most relevant Tier 1 results
5. If comparison: firecrawl-research on each expert's primary source (podcast transcript, blog post, paper)

Step 4: Pull Personal Health Context

Skip if --no-personal flag set.

Run relevant health queries based on topic:

# Always pull (baseline context)
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json vitals

# Topic-specific
# exercise/training:
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json workouts --days 30
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json activity --days 7

# sleep:
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json sleep --days 14

# body composition/nutrition:
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json weekly --weeks 4

# cardiovascular:
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json vitals
# + custom query for BP if available:
python ~/ai_projects/claude-skills/health-data/scripts/health_query.py --format json query "SELECT AVG(value), unit FROM health_records WHERE record_type LIKE '%BloodPressure%' AND start_date >= date('now', '-30 days')"

Health context formatting: Extract only the relevant numbers. Don't dump raw JSON into the response. Example: "Based on your recent data: resting HR 62 bpm, HRV 45ms, avg 9,200 steps/day, 4 strength workouts/week"

Step 5: Synthesize Evidence

Evidence grading rules:

1. Start with study design level (see sources.md grading table)
2. Upgrade if: large effect size, dose-response, multiple independent replications
3. Downgrade if: industry funding without replication, small sample, high heterogeneity, indirect evidence
4. Strong: 2+ systematic reviews/meta-analyses agree, or 3+ large RCTs consistent
5. Moderate: 1 systematic review or 2+ well-designed studies, some limitations
6. Weak: Limited studies, small samples, or conflicting results
7. Minimal: Expert opinion only, animal studies, case reports
8. Contested: Credible evidence on both sides, active scientific debate

For expert comparisons:

1. State each expert's position clearly with their key claims
2. Find where they actually agree (usually more than expected)
3. Show what research supports/contradicts each position
4. Avoid declaring a "winner" -- let evidence speak

Step 6: Format Output

Follow the template in SKILL.md exactly. Key formatting rules:

1. Short answer first -- 1-2 sentences, direct
2. Evidence table -- every major claim gets a strength rating
3. Personal section -- only if health data adds meaningful context
4. Sources -- list with tier label and year
5. Disclaimer -- always append

Table formatting:

| Claim | Evidence Strength |
|-------|------------------|
| Creatine improves strength output | **Strong** |
| Creatine causes hair loss | **Weak** (single small study, not replicated) |
| 5g/day is optimal dose | **Strong** |
| Loading phase is necessary | **Weak** (not needed, just takes longer to saturate) |

Personal section example:

## For You Specifically

Your recent data shows 4 strength sessions/week with avg resting HR of 62.
Given your training volume, 5g creatine monohydrate daily would be well-supported.
Your HRV of 45ms suggests good recovery capacity.

**One consideration**: Your sleep data shows avg 6.2h -- optimizing sleep to 7+ hours
would likely provide more performance benefit than any supplement.

Error Handling

Database not found (health.db):

• Skip personal context section
• Note: "Personal health data unavailable -- using general recommendations"

No relevant search results from Tier 1:

• Expand to Tier 2-3
• Note: "Limited primary research found -- evidence grading reflects available sources"

Question outside health domain:

• Politely redirect: "This skill focuses on evidence-based health questions. For [topic], try [alternative]."

Highly personalized medical question (e.g., "should I take this medication"):

• Provide evidence but emphasize: "This specific decision requires your healthcare provider who knows your full history"

Performance Notes

• Quick depth should return in <30s (2 WebSearch calls)
• Deep depth: ~60-90s (5-6 search calls + tavily)
• Full depth: ~2-3min (8+ searches + firecrawl)
• Health data queries: ~2-3s each (local SQLite)
• Parallelize independent search calls where possible