weekly-load-review

Weekly Training Load Review

Goal

Pull the last 14 days of Strava activities and compute weekly training load summaries for this week (Mon–Sun) and the prior week (Mon–Sun). Compare against the 4-week rolling baseline stored in state/strava-weekly-baselines.json (create if missing).

Steps

1. Call mcp__strava__check-strava-connection. If disconnected, alert the operator via the configured channel and stop. If no channel is configured, pipe the disconnection into .hermit/bin/hermitd-run task note .hermit <id> only inside an open record's turn (otherwise skip the note) and stop.
2. Call mcp__strava__get-recent-activities with perPage: 30 to cover 14+ days.
3. Determine the two week boundaries: this week is the most recent Mon–Sun period ending today (Sunday); the prior week is the Mon–Sun before that. Compute the exact date ranges from today's date.
4. For each week compute:
   • Run: total distance (km), total elevation (m), session count
   • Bike: total distance (km), session count
   • Strength: session count, total duration in minutes as strength_minutes (sum moving_time over WeightTraining/Workout activities: already in the Step 2 payload, no extra call; moving_time is in seconds, so divide the sum by 60)
   • Total active days Also compute load-adjusted run distance (adjusted_km): for each run activity, derive elev_gain_per_km = elevation_gain_m / distance_km and apply a heuristic multiplier to account for the higher mechanical load of gradient (descents especially): < 10 m/km → 1.0× · 10 to < 25 m/km → 1.2× · 25 to < 40 m/km → 1.35× · ≥ 40 m/km → 1.5×. Sum the adjusted distances to get the week's adjusted_km.
5. Read state/strava-weekly-baselines.json and state/activity-notes.json (or {} if absent) in the same turn. The baselines file is used in step 6; the activity notes will be used in step 8.
6. Compare this week's adjusted_km to the 4-week rolling average of adjusted_km from the baseline file. If any historical week lacks adjusted_km, use that week's raw km as its adjusted value for the rolling average.

   • 25% above average → 🔴 "Load spike: [Y]km (adj) vs [avg]km average"

   • 25% below average → 🟡 "Load dip: [Y]km (adj) vs [avg]km average"

   • Within range → 🟢 "Consistent load"
7. Update state/strava-weekly-baselines.json: append this week's totals, including adjusted_km alongside the existing raw km, and strength_minutes. Keep only the last 8 weeks.
8. From the activity list fetched in step 2, collect the IDs of this week's activities. Filter the activity-notes.json read in step 5 to those IDs. If 2 or more RPE entries exist, compute the average (one decimal place) and prepare the line: 💬 Avg RPE: X.X/10 (N=<count>). Otherwise prepare no RPE line.