gtapps/activity-deep-dive

Activity Deep-Dive

Produces a standardised per-activity coaching note. All deterministic statistics (session-kind detection, terrain, zones, cadence, efficiency, cardiac drift, VAM/GAP, recovery estimate) are computed by scripts/fitness-lab.ts — this skill runs it once, interprets the JSON, and writes the coaching narrative. Raw Strava streams never enter context: the script fetches and reduces them; the skill only sees the reduced metrics.

Interval sessions get work-interval HR progression and between-bout recovery quality; steady sessions get pace/HR efficiency and cardiac drift. Trail sessions swap pace/HR efficiency for VAM and a grade-adjusted-pace estimate, reframe cardiac drift against the altitude profile, and extend the recovery window for descent load. Both get zone breakdown, 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. Resolve subjective RPE. Read .claude-code-hermit/state/activity-notes.json. If it exists and has an entry for the resolved activity ID (or, for latest, hold the check until step 2 returns the ID), keep rpe and notes for the output template and artifact frontmatter. The script does NOT read RPE — this is the skill's job.

2. Run the analysis script. Issue a single Bash call:

   bun ${CLAUDE_PLUGIN_ROOT}/scripts/fitness-lab.ts analyze <activity-id|latest>
   

   The script fetches details, laps, streams, athlete zones, and the recent summary activities (for the efficiency baseline), then emits one JSON object. Handle the error contract:

   • {"error":"strava_auth","message":"…"} (exit 1) → the token is missing or expired. Relay the message verbatim to the operator and stop. Do not compute anything.
   • {"error":"fetch","message":"…"} (exit 1) → report the message and stop.
   • Success (exit 0) → parse the JSON and continue.

3. Interpret the JSON. The object has these fields (see thresholds in the payload for the exact cutoffs behind each classification — cite them in coaching prose without hardcoding a second copy):

   • meta — name, date, sport_type, distance_km, moving_time_s, avg_hr, max_hr, total_elevation_gain_m, elev_gain_per_km.
   • session_kind — "interval" or "steady". session_detail carries cycles, differential_bpm, work_bouts, avg_bout_min for the Interval (N×~Xmin) header, plus work_segment_hrs — the ordered per-work-bout average HR (from laps when there were ≥3, else from the HR-stream windows the classifier used). This is the authoritative source for the I1 → IN progression, so it renders even on lap-sparse activities.
   • terrain — "road" or "trail".
   • zones — [{zone, pct}] (Z1–Z5), or null when HR/zone data is absent (render "HR data unavailable").
   • cadence — {avg, sd, cv, flags} or null. flags is ["over-striding"] and/or ["high-variability"]; it is empty on trail (road-calibrated thresholds are suppressed). Omit the cadence line entirely when null.
   • efficiency — {current, prior_mean, delta_pct, priors_used}. Cite on steady + road only. delta_pct is the signed % vs the prior mean (negative = more efficient). Skip the line when priors_used is 0.
   • cardiac_drift_bpm / cardiac_drift_flagged — signed int (rising HR = positive), and whether it cleared the flag threshold. Cite on steady sessions only — on an interval session the first-20%/last-20% split straddles work and recovery bouts, so the figure is noise; skip the drift line. null on trail (use the hr_altitude field instead — see below). flagged is already pace-guarded (a negative split won't trip it), so relay it as-is.
   • hr_altitude — trail only (null on road, and null on trail when HR/altitude streams are too short or flat to correlate). {corr, tracks}: corr is the Pearson r of HR vs altitude (rounded); tracks is "tracks" (r ≥ 0.3 — HR broadly rose on climbs / fell on descents, expected) or "decoupled" (HR did not follow the terrain — worth flagging). Render the HR/altitude: line from this; when null, state the coupling couldn't be assessed rather than inventing one.
   • vam (m/h) and gap_per_km (seconds/km) — trail only; null on road. Render GAP as M:SS/km.
   • laps — [{index, avg_hr, max_hr, distance_km, moving_time_s}]. For interval sessions, build the I1 → IN progression from session_detail.work_segment_hrs (above), not the raw laps — it already sequences the work bouts whether laps or HR-windows fed the classifier. Use laps (and meta.max_hr as HRmax, never a zone floor) for the between-bout recovery note and the peak-bout callout; when laps is empty, ground those from the progression alone.
   • recovery — {band, hours, window, trail_extended}. band is 1–5; window is the rendered rest recommendation (already includes any (+trail vert) extension).
   • warnings — degraded-metric notes (short/absent streams). Surface anything material in the coaching note rather than silently dropping it.

4. Write the 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").

5. Format output (8–10 lines):

   Steady session (road):

   Activity: <name> | <date> | <distance>km in <duration>
   Session type: Steady
   Zones: Z1 N% / Z2 N% / Z3 N% / Z4 N% / Z5 N%
   Cadence: N spm avg (CV: X%) [⚠ over-striding | ⚠ high variability]   ← omit line when cadence absent or non-running; show ⚠ only on a tripped flag
   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
   Subjective: RPE N/10 — <notes>          ← include only when RPE data exists from step 1
   Coaching: <2–3 sentences>
   

   Steady session (trail):

   Activity: <name> | <date> | <distance>km in <duration> | <elevation>m gain
   Session type: Steady · Trail
   Zones: Z1 N% / Z2 N% / Z3 N% / Z4 N% / Z5 N%
   Cadence: N spm avg (CV: X%)   ← reference only on trail (no ⚠ flags); omit line when cadence absent or non-running
   Trail: VAM N m/h | GAP ~M:SS/km (est, vs actual P:SS/km)
   HR/altitude: HR <tracked / decoupled from> the climb/descent profile (r=X.XX)   ← from hr_altitude; if null, "coupling not assessable (stream too short)"
   Recovery: N/5 — recommended rest: Xh[(+trail vert)]
   Subjective: RPE N/10 — <notes>          ← include only when RPE data exists from step 1
   Coaching: <2–3 sentences>
   

   Interval session (road):

   Activity: <name> | <date> | <distance>km in <duration>
   Session type: Interval (N×~Xmin)
   Zones: Z1 N% / Z2 N% / Z3 N% / Z4 N% / Z5 N%
   Cadence: N spm avg (CV: X%) [⚠ over-striding | ⚠ high variability]   ← omit line when cadence absent or non-running; show ⚠ only on a tripped flag
   Intervals: I1 NNNbpm → IN NNNbpm — <progressive ✓ / regressive / flat>; peaked at NNNbpm on IN
   Between-bout recovery: HR to ~NNNbpm — <adequate / incomplete>
   Recovery: N/5 — recommended rest: Xh
   Subjective: RPE N/10 — <notes>          ← include only when RPE data exists from step 1
   Coaching: <2–3 sentences>
   

   Interval session (trail):

   Activity: <name> | <date> | <distance>km in <duration> | <elevation>m gain
   Session type: Interval (N×~Xmin) · Trail
   Zones: Z1 N% / Z2 N% / Z3 N% / Z4 N% / Z5 N%
   Cadence: N spm avg (CV: X%)   ← reference only on trail (no ⚠ flags); omit line when cadence absent or non-running
   Trail: VAM N m/h | GAP ~M:SS/km (est, vs actual P:SS/km)
   Intervals: I1 NNNbpm → IN NNNbpm — <progressive ✓ / regressive / flat>; peaked at NNNbpm on IN
   Between-bout recovery: HR to ~NNNbpm — <adequate / incomplete>
   Recovery: N/5 — recommended rest: Xh[(+trail vert)]
   Subjective: RPE N/10 — <notes>          ← include only when RPE data exists from step 1
   Coaching: <2–3 sentences>
   

6. Save compiled artifact to .claude-code-hermit/compiled/activity-<id>-<YYYY-MM-DD>.md:

---
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|TrailRun|Ride|WeightTraining|…>
terrain: <road|trail>
session_kind: <interval|steady>
rpe: <int>                               # include only when RPE data exists from step 1
subjective_notes: "<string>"             # include only when notes exist from step 1
cardiac_drift_bpm: <int>                 # include only when drift was computed from step 3
cardiac_drift_flagged: <bool>            # include only when drift was computed from step 3
---

Body: the full output above.

CRITICAL — cardiac_drift_bpm must be a bare signed integer (6, -5), not a formatted value like +6 bpm. weekly-coaching-patterns reads this field to build its trend series; renaming the key or writing a non-numeric value breaks the trend detector. It falls back to parsing the body's Cardiac drift: line only for notes written before this field existed.

7. Write signal-only coaching observations to .claude-code-hermit/sessions/SHELL.md Findings.

   From the computed metrics and coaching note, derive 0–N observations that carry a coaching signal worth tracking across sessions: a flagged cardiac drift, a zone-distribution anomaly, a recovery estimate that conflicts with subjective RPE, an efficiency regression vs the prior mean, a flagged cadence (low average or high within-run variability), a notable VAM value, or a trail recovery extension. Do NOT write routine confirmations ("session completed normally") unless they represent a pattern break. If nothing clears the signal bar, skip this step.

   First Read .claude-code-hermit/sessions/SHELL.md (Edit requires the file in context, and you need its current ## Findings content to dedup). For each qualifying observation, anchor on the HTML comment and append one line:

   old_string: "<!-- Anything unexpected found during work. Proposal-worthy items get their own file. -->"
   new_string:  "<!-- Anything unexpected found during work. Proposal-worthy items get their own file. -->\nCoaching observation [<label>] (activity <id>): <one-line description grounded in a specific metric>"
   

   Skip the append if a line with the same [<label>] (activity <id>) already exists in ## Findings; re-running the deep-dive must not duplicate observations. If the anchor comment is absent (operator edited SHELL.md), append directly under the ## Findings heading instead.

   Labels are kebab-case and reused across sessions for consistency. Prefer an existing label over inventing a synonym. Seed vocabulary: cooldown-hr-elevated, vo2max-stimulus-confirmed, cardiac-drift-high, interval-pacing-inconsistent, recovery-insufficient, efficiency-regression, cadence-low, cadence-variability-high, trail-recovery-extended, vam-notable. Add a new kebab-case label only when none fit.

   These lines feed reflect's current-session evidence path; the label convention lets reflect recognize recurrence across sessions, and recurring observations graduate to proposals through the normal reflection-judge / proposal-triage gates.

8. Return the formatted output to the caller.