From grimoire
Guides training load decisions using daily HRV measurements compared to a 7-day rolling average, with evidence-based thresholds for adjusting intensity and volume.
How this skill is triggered — by the user, by Claude, or both
Slash command
/grimoire:apply-hrv-monitoringThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Measure resting heart rate variability each morning and compare it against your 7-day rolling average — using daily deviations of ≥10% below baseline to reduce intensity or volume, and sustained high HRV trends to identify windows for breakthrough training.
Measure resting heart rate variability each morning and compare it against your 7-day rolling average — using daily deviations of ≥10% below baseline to reduce intensity or volume, and sustained high HRV trends to identify windows for breakthrough training.
Adopted by: Standard in elite cycling (Team Sky/INEOS, Jumbo-Visma), elite triathlon, and professional football clubs. NSCA Essentials of Strength and Conditioning (2016) includes HRV as a recommended readiness monitoring tool. Used by Olympic coaches across swimming, rowing, and track cycling. HRV4Training (100,000+ active users) and Elite HRV (used by 200,000+ athletes and coaches) are the dominant consumer implementations. Tim Ferriss, Peter Attia, and Andrew Huberman have popularized it for non-elite training. Impact: Plews et al. (2012) IJSPP: Elite rowers who trained according to HRV guidance showed significantly better performance outcomes than those following fixed periodization plans. Kiviniemi et al. (2007, IJSPP): HRV-guided training group improved 5km run time by 7.6% vs 3.5% in conventional training group over 4 weeks. Buchheit (2014) Sports Medicine comprehensive review: HRV correlates with training load, overreaching, illness onset, and competition readiness — making it the most actionable non-invasive readiness marker available. The mechanism: HRV reflects parasympathetic (vagal) tone; low HRV = sympathetic dominance = inadequate recovery; high HRV = parasympathetic dominance = ready for load. Why best: Training by fixed schedule (Monday: hard, Wednesday: hard, Friday: hard) ignores day-to-day variation in recovery. Subjective "how do I feel?" is notoriously unreliable — athletes systematically underestimate accumulated fatigue. HRV provides an objective, quantified signal from the autonomic nervous system. The alternative (RPE-based training) requires significant experience to self-assess accurately and is subject to motivation bias. HRV-guided training reduces overreaching episodes and prevents the performance-suppressing cycle of training hard when under-recovered.
Sources: Buchheit (2014) Sports Medicine; Plews et al. (2012, 2013, 2014) IJSPP; Kiviniemi et al. (2007) IJSPP; NSCA Essentials of Strength and Conditioning
Minimum equipment: a chest strap heart rate monitor (Polar H10 is the validated standard; finger-based apps introduce more noise) and an HRV app:
Validated options:
HRV4Training — camera-based (phone camera), good for >3 min morning protocol
Elite HRV — supports chest strap; good for 1-min morning reading
Polar Flow — built-in if using Polar devices
Whoop — continuous monitoring (higher cost, wrist-based)
Garmin — built-in nightly HRV tracking on Fenix/Forerunner 945+
The metric: most apps report rMSSD (root mean square of successive differences) — the standard HRV metric for short recordings. RMSSD reflects parasympathetic activity and is most relevant for daily readiness.
Measure at the same time every morning:
□ Immediately upon waking, before standing
□ Lie supine or sit quietly — consistent position each day
□ Do not check phone or stimulate stress response before measuring
□ Measure before coffee, food, or exercise
□ Duration: 1 minute minimum (HRV4Training camera method), 3–5 min preferred
□ Breathe normally — do not control breathing during measurement
First 7–14 days = baseline period: take daily readings but do not alter training. The app builds your personal baseline. Individual HRV values are meaningless in isolation — a reading of 65 ms rMSSD is high for one person and low for another; only deviation from your own baseline matters.
After 7–14 days of baseline:
Green — HRV within ±10% of 7-day average: proceed as planned
Yellow — HRV 10–20% below 7-day average: reduce intensity or volume by 20–30%
Red — HRV >20% below 7-day average OR lowest reading in 2+ weeks: replace
hard session with easy/active recovery or rest
Also note trend direction (not just single-day value):
Green day → execute planned session as written
Yellow day → replace high-intensity intervals with tempo/moderate; reduce total volume 20%
Red day → active recovery (easy walk, mobility work, zone 1 cardio ≤30 min) or rest
Do NOT try to push through a red day — depressed HRV after a hard session
followed by another hard session is the overtraining mechanism
Review weekly HRV trend (most apps show a 7-day chart):
Healthy training week: HRV depresses after hard days, recovers by next hard day
Overreaching pattern: HRV depresses and stays depressed 3–5+ days without recovery
Undertrained pattern: HRV consistently above baseline — increase training load
Illness onset signal: Sudden HRV drop without preceding hard training session
HRV is affected by factors besides training. Note these daily to avoid false alarms:
| Factor | Effect on HRV |
|---|---|
| Alcohol (even 1–2 drinks) | Depresses HRV 20–40% next morning |
| Poor sleep (<6h) | Depresses HRV |
| Travel / time zones | Depresses HRV 1–3 days |
| Illness onset | Sudden drop before subjective symptoms |
| High stress / anxiety | Depresses HRV |
When HRV drops coincide with known confounders (alcohol the night before), treat as non-training recovery signal — do not infer overtraining.
npx claudepluginhub jeffreytse/grimoire --plugin grimoireUse when a coach or athlete needs to objectively measure an athlete's recovery status before a training session — to decide whether to train as planned, reduce load, or rest based on physiological and subjective readiness indicators.
Analyzes fitness logs to identify exercise patterns, track progress in running/strength/endurance/flexibility, correlate with health metrics like blood pressure/weight/blood sugar, and generate personalized training suggestions.
Processes and analyzes physiological biosignals (ECG, EEG, EDA, RSP, PPG, EMG, EOG) using the NeuroKit2 Python toolkit. Supports HRV, event-related potentials, complexity measures, and multi-modal integration for psychophysiology research.