---
name: activity-deep-dive
description: Per-activity coaching analysis from Strava. Computes zone breakdown, pace/HR efficiency, cardiac drift, recovery estimate; saves a compiled artifact and returns a compact summary. Run after a workout or to retro-analyze a specific activity.
allowed-tools:
  - Read
  - Write
  - mcp__strava__check-strava-connection
  - mcp__strava__get-athlete-zones
  - mcp__strava__get-recent-activities
  - mcp__strava__get-activity-details
  - mcp__strava__get-activity-laps
  - mcp__strava__get-activity-streams
---

# Activity Deep-Dive

Produces a standardised per-activity coaching note: zone breakdown, pace/HR efficiency, cardiac drift, recovery estimate, and a coaching note. Saves a compiled artifact and returns a compact summary.

## Usage

```
/claude-code-fitness-hermit:activity-deep-dive <activity-id>
/claude-code-fitness-hermit:activity-deep-dive latest
```

## Steps

1. Call `mcp__strava__check-strava-connection` — abort if disconnected.
2. Fetch athlete zones via `mcp__strava__get-athlete-zones` (needed for zone calculations).
3. Resolve activity:
   - If `"latest"`: call `mcp__strava__get-recent-activities` with limit 1, extract the activity ID.
   - Otherwise: use the provided activity ID directly.
4. Issue all four calls in a single turn so they execute concurrently:
   - `mcp__strava__get-activity-details` — name, type, date, distance, duration, avg/max HR, avg pace, elevation
   - `mcp__strava__get-activity-laps` — lap splits
   - `mcp__strava__get-activity-streams` with keys `heartrate,velocity_smooth,altitude,cadence` (add `watts` if sport type is Ride — use `altitude,cadence` as the baseline keys and add `watts` only when sport type is known to be Ride)
   - `mcp__strava__get-recent-activities` with `perPage: 5` — for the pace/HR efficiency comparison below (filter by sport type after all results arrive)
5. Compute metrics:

   **Zone breakdown** — from HR stream vs athlete zone boundaries. Calculate % of stream datapoints in each zone (Z1–Z5). If HR stream absent: note "HR data unavailable".

   **Pace/HR efficiency** — average pace (min/km) divided by average HR. Lower = more efficient. From the `get-recent-activities` result fetched in Step 4: filter for activities of the same sport type, exclude the current activity ID, take up to the 4 most recent. Compute the same ratio for each. Report delta vs prior mean.

   **Cardiac drift** — compare average HR in first 20% of HR stream vs last 20%. Flag if difference > 10 bpm at similar pace (± 15 sec/km). Report: `drift: +N bpm`.

   **Recovery estimate** (scale 1–5):
   - 1 = easy (< 5% Z3+, avg HR < Z3 floor, < 60 min)
   - 2 = moderate (5–20% Z3, < 60 min)
   - 3 = quality (20–50% Z3, or any Z4, < 90 min)
   - 4 = hard (> 50% Z3 or > 10% Z4, or > 90 min hard)
   - 5 = race-level (> 20% Z4+, or peak HR > 95% max)
   Include recommended recovery window: 1→24h, 2→36h, 3→48h, 4→72h, 5→5–7 days.

   **Coaching note** — 2–3 sentences grounded in the numbers. Highlight what was executed well and one concrete thing to monitor or adjust next time. Reference specific metrics (e.g. "cardiac drift of +14 bpm suggests pacing started too hot").

6. Format output (8–10 lines):
```
Activity: <name> | <date> | <distance>km in <duration>
Zones: Z1 N% / Z2 N% / Z3 N% / Z4 N% / Z5 N%
Pace/HR efficiency: X.XX min·km⁻¹·bpm⁻¹ (vs prior 4: ±X%)
Cardiac drift: +N bpm (flag if > 10 bpm)
Recovery: N/5 — recommended rest: Xh
Coaching: <2–3 sentences>
```

7. Save compiled artifact to `.claude-code-hermit/compiled/activity-<id>-<YYYY-MM-DD>.md`:
```yaml
---
title: "Activity Note — <name> <date>"
type: activity-note
created: <ISO 8601>
session: <current session ID from SHELL.md>
source: manual
tags: [activity-analysis]
activity_id: <id>
sport_type: <Run|Ride|WeightTraining|…>
---
```
Body: the full 8–10 line output above.

8. Return the formatted output to the caller.