Whoop + Claude Health Analyst

3C Resting Heart Rate Analysis:
Trend over the dataset.
Elevation events: nights where RHR was significantly above baseline (over 5 bpm).
Correlate with the day before: hard workout, late night, illness, alcohol?
RHR by day of week: is Monday RHR consistently higher?

3D Body Temperature Analysis (Oura, Whoop, some Garmin and Samsung):
Baseline stability: is deviation staying close to 0.0 degrees most nights?
Elevation events: flag any nights with deviation over +0.5 degrees.
Sustained elevation: multiple consecutive nights above baseline is more significant.

3E Strain vs Recovery Balance:
Training load trend: is weekly strain increasing faster than 10% per week?
Recovery-matched training: on low-recovery days, did the person still train hard?
Post-workout recovery window: how long does RHR take to return to baseline?
Workout timing: are late-evening workouts correlating with worse sleep scores?

3F Stress and Autonomic Nervous System:
Stress score trends: is average daily stress increasing?
High-stress periods: do these correlate with worse sleep and lower HRV?
Parasympathetic activity: chronically low nighttime HRV suggests the nervous system is failing to shift adequately into recovery mode.

3G Behavioural Correlations (if journal or tag data is available):
Alcohol: what is the average impact on next-day HRV and recovery score? Quantify this person's specific response.
Late meals: does eating within 2 hours of sleep correlate with elevated RHR?
Caffeine: if logged, does caffeine after a certain time correlate with worse sleep?
Travel: are there periods of apparent circadian disruption?

STEP 4 TOP 5 LEVERAGE POINTS
Based on the full analysis, identify the 5 most impactful factors affecting this person's health performance. These must be:
Ranked by estimated impact, most impactful first
Supported by specific data patterns, numbers, and trends from the files
Framed purely as fixable levers
Highly specific to this individual

For each leverage point, provide:
1. A clear, plain-English name for the issue
2. The specific data that reveals it (exact numbers, trend direction, correlations)
3. A plain-English explanation of why it matters physiologically
4. The estimated downstream impact
5. One to three specific, low-friction actions to address it this week

STEP 5 WEEKLY PROTOCOL
Synthesise all recommendations into one clean, prioritised action plan:
This Week Non-Negotiables (the 2 to 3 highest-impact changes)
This Week Quick Wins (easy changes that compound over time)
Watch Closely (exact metrics to monitor to confirm changes are working)
Flags (anything warranting attention from a medical professional)

OUTPUT FORMAT
DATA SUMMARY
Device and platform: [name]
File format: [CSV / JSON / XML / other]
Data types found: [complete list]
Date range: [from to]
Days of data: [number]
Data quality notes: [any gaps, anomalies, or missing fields]

BASELINE PROFILE
[Present all baselines in a clean, readable format. Use tables where helpful.]

PATTERN ANALYSIS
[Present findings from each section that has data. Note what is missing and what it would reveal if present.]

TOP 5 LEVERAGE POINTS
#1 [Name of issue (Highest impact)]
Data: [specific numbers and trends]
Why it matters: [physiological explanation]
Estimated impact: [what fixing this is likely to do]
Actions:
[Action 1]
[Action 2]
[Action 3 if needed]
[Repeat for #2 through #5]

YOUR PROTOCOL THIS WEEK
Non-Negotiables:
[Action]
[Action]

Quick Wins:
[Action]
[Action]

Watch Closely:
[Metric and what change to look for]

Flags for Medical Attention:
[Only include if genuinely warranted by the data]

TONE AND APPROACH
Speak like a knowledgeable friend who has spent time studying this person's data. Direct, specific, and genuinely useful. Every observation must reference actual numbers from the uploaded files. Completely avoid generic health advice. If the data does not support a conclusion, say so clearly rather than speculating.
If the dataset covers fewer than 14 days, note that patterns are suggestive rather than statistically reliable.
Avoid adding medical disclaimers unless something genuinely warrants clinical attention. This serves strictly as a performance and lifestyle analysis.

What comes back

Claude works through the prompt in five stages. Here is what each one surfaces from your Whoop data.

• Stage 1. File inventory: Reads your CSV, identifies every data type Whoop captured, flags the date range and any gaps, and notes Whoop-specific context before doing anything else.
• Stage 2. Personal baseline: Calculates your actual averages for recovery score, HRV, strain, sleep performance, respiratory rate, and sleep stages. It relies entirely on your personal averages.
• Stage 3. Pattern analysis: Finds what is actually driving your recovery score up or down. HRV trends over 28 days. Strain versus recovery balance. What alcohol does to your specific HRV. Whether your bedtime variance is doing more damage than your total sleep hours.
• Stage 4. Top 5 leverage points: Ranks the five physiological issues doing the most damage to your metrics, with the specific Whoop data behind each one and concrete actions to fix them this week.
• Stage 5. Weekly protocol: Non-negotiables, quick wins, the exact data points to watch closely in the Whoop app, and anything that warrants a doctor's attention.

What it actually finds

These are the patterns Whoop never shows you in the app, but that are sitting in your raw export right now.

• Why your recovery score was low on specific days: What happened the night before and the day before that. Hard training session, late meal, alcohol, high stress. Correlated across your full history.
• Whether your bedtime is doing more damage than your sleep duration: Whoop tracks when you actually fall asleep. Bedtime variance above 45 minutes is often a bigger driver of poor recovery than total sleep hours. The prompt calculates your variance specifically and tells you what it is costing you.
• What alcohol is doing to your HRV specifically: Average HRV suppression from alcohol is 10 to 20% but your number is entirely unique. If you use the Whoop journal to log alcohol, the prompt calculates your personal suppression from your own data.
• Whether you are training hard on days your body is already under stress: Whoop's strain and recovery data combined shows exactly how often you are pushing hard on days your body has flagged as low readiness. The prompt quantifies this across your entire dataset.
• Temperature changes before symptoms appear: Whoop tracks skin temperature. Deviations from your baseline often precede illness by one to two days. The prompt shows you every instance where this happened in your history and what followed.

If something goes wrong

• You did not receive the export email: Check your spam folder first. If it is not there, go back to Whoop app, More tab, App Settings, Data Export and try again. Make sure the email address on your Whoop account is correct.
• Claude says the file is too large: If you have several years of Whoop data the CSV can be large. Try exporting a shorter date range from within the app. 90 days is enough for strong pattern analysis.
• Claude cannot read the file: Make sure you are uploading the CSV file directly. Avoid using a ZIP or a screenshot. The Whoop export should arrive as a CSV attached to the email.
• The analysis feels generic: This usually means the export did not include journal data. If you use the Whoop journal to log alcohol, caffeine, stress, or other behaviours, make sure those are included in your export settings before exporting.

How to actually use this

Three ways to get more from it.

1. Use at least 90 days of data. Two weeks surfaces patterns. Three months surfaces the ones that actually matter. Cumulative fatigue, seasonal trends, and the real impact of your training cycles all need time to become visible.
2. Log consistently in the Whoop journal. The journal is where you note alcohol, caffeine, stress, travel, and illness. The more behavioural data in the export, the more specific the output. If you have not been using the journal, start now and re-run this prompt in 30 days.
3. Run it again after making changes. Export new data after four weeks of acting on the weekly protocol and run the same prompt again. The before and after comparison is where the real value shows up.

Where I'm taking this next

Most people check their Whoop recovery score, feel good or bad about it, and move on without knowing what caused it or what to actually change. The score becomes background noise. This prompt makes the data mean something. Your specific HRV response to alcohol. Your specific bedtime variance and what it costs your recovery score. Your specific strain-to-recovery ratio and whether it is trending in the right direction.

Whoop has been collecting this data every night. Now you get to read it.