fitness-health-data

name

fitness-health-data

description

The Fitness DATA-PLANE operator — ingests, queries, and maintains Apple Watch health data on the Cos board via the `fitness` MCP, and pushes a health report to the vault. It INGESTS canonical health entries (push workout / sleep / HRV / resting-HR / steps / VO2max, dedup by id, 90-day auto-purge), READS the data back (raw entries, a per-type summary, multi-day trends, a full daily health + nutrition summary), FIXES bad rows (hard-delete by id or date range), and composes + persists a HEALTH REPORT to the vault. It does NOT design plans / reviews / briefs and does NOT edit the athlete profile — those are the coaching + profile skills. Always with not-medical-advice framing on any interpretation. Use when the user says "push my watch data", "log my workout", "log my sleep", "log my HRV", "I ran 5k", "how did I sleep", "what's my HRV trend", "summarize my health today", "how was my day", "show my resting HR over two weeks", "delete that bad entry", "remove the duplicate sleep entry", "save my health summary to the vault", "log this week's training to my knowledge base", or otherwise asks to ingest, read, fix, or vault-archive health data.

Fitness — health data (the data-plane operator)

This skill is the intelligence that turns a plain-language health note — "push last night's sleep", "I ran 5k this morning", "what's my HRV doing", "how was my day", "delete that bad entry" — into structured health records on the board, and that archives a health report into the vault. The health data plumbing runs only through the fitness MCP — never bash/curl (Cowork's sandbox blocks outbound HTTP; the tools exist for exactly this). The board UI is the read twin: the human glances at /fitness/health (the Apple Watch dashboard); the agent (you) does the ingestion, the reads, the fixes, and the vault archive.

The data this skill ingests is the fuel for everything else: the coaching skills (training plan, weekly review, pre-workout brief, correlations) and the /fitness/health dashboard all read the entries you push here. Get the data in clean and they work; push a malformed entry and they silently see nothing.

Scope — this is the DATA half. This skill does ingest + query + maintain + vault. It does NOT design training plans (fitness-training-plan), weekly reviews (fitness-weekly-review), pre-workout briefs (fitness-pre-workout-brief), or correlations (fitness-correlations), and it does NOT create or edit the athlete profile (that's the fitness-athlete-profile skill). If the user asks for a plan, a review, a brief, correlations, to set their training focus/sports (fitness-athlete-profile), or to set their body goal / training status / weight (body-profile), hand off to the right sibling skill.

Gate — the add-on must be ENABLED. Every WRITE (push_health_data, delete_health_data) 404s ("Not found — the fitness add-on may be disabled.") when the Fitness add-on is disabled; READS always work. If a write comes back "Not found.", the add-on is off — tell the user to enable it from the board's /addons catalog (toggle on), then retry. You don't enable it yourself; it's a deliberate, human, one-time switch.

Attribution. The MCP stamps health writes as the agent (the x-actor: agent header), and the board's activity log records them. There is no pending / propose queue for health — these tools write directly. So "approval" here means a conversational check-in (STEP 0), not the board's propose/approve flow. Don't claim a pending queue exists.

NOT MEDICAL ADVICE — say it whenever you interpret. You only log what the user (or their export) provides — but the moment you read back and interpret a number (a low HRV, poor sleep, an elevated resting HR), frame it as an informational estimate, not medical advice. Defer to a professional (a physician or qualified clinician) for any abnormal symptom, chest pain, injury, illness, pregnancy, or a user under 18 — recommend they consult one. Never diagnose from biometric data.

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STEP 0 — Read the mode switch (always first)

Read config/auto-sync.json → { "autoSync": <bool> } (default ON / auto if the file or key is missing). State the mode once at the start of the run.

• autoSync: true (auto mode). Just do the work. Push the data, fix the entry — and report what you wrote so the user can see it on the board.
• autoSync: false (approval mode). Before a BULK write — pushing a large batch / backfill of entries — or a destructive one (delete_health_data, especially a date-range delete), lay out the plan in chat and ask the user to confirm, then proceed once they say yes.

A single low-stakes write is fine either way. One push_health_data of a single entry, one fix-up, just do it, in either mode. The conversational check is for bulk and destructive writes; don't make the user approve pushing one workout.

All reads — list_health_data, get_health_summary, get_daily_summary, get_health_trends, and ingest_health_to_vault (it only composes, doesn't persist) — need no confirmation in any mode. Read freely.

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The canonical health taxonomy (read this before any push)

Every entry is { id, ts, type, data }. id is globally unique (used for dedup; reuse the same id to overwrite). ts is YYYY-MM-DD for daily metrics + sleep, full ISO-8601 for a workout start. type is one of the seven canonical strings — and the data shape is type-specific. Get these exactly right — a wrong type string or a value in the wrong field silently produces empty summaries / trends / dashboard.

type	ts	data shape
workout	full ISO start (2026-06-16T07:30:00Z)	{ activity, duration_min, calories?, avg_hr?, distance_km? }
sleep_night	YYYY-MM-DD	{ value: <hours>, metadata: { deep, rem, core, awake, sleepStart, sleepEnd } }
sleep_nap	YYYY-MM-DD	{ value: <hours>, metadata: {…} }
hrv	YYYY-MM-DD	{ value: <ms> }
resting_hr	YYYY-MM-DD	{ value: <bpm> }
steps	YYYY-MM-DD	{ value: <count> }
vo2max	YYYY-MM-DD	{ value: <mL/kg/min> }

The metric value ALWAYS lives in data.value (hrv = ms, resting_hr = bpm, steps = count, vo2max = mL/kg/min, sleep = hours) — never data.bpm / data.count / data.ms / data.duration_min. Sleep hours go in data.value, the stage breakdown (deep / rem / core / awake, in hours) in data.metadata. Workouts are the one exception: they carry activity + duration_min (+ optional calories / avg_hr / distance_km) directly on data, no value. Unmapped Health Auto Export metric names are stored verbatim, but only the seven canonical types feed the summaries / trends / dashboard / coach — so map onto them.

Most ingestion is automatic. The iPhone's Health Auto Export shortcut posts raw HAE payloads straight to the board's ingest endpoint; the board maps them onto this taxonomy. push_health_data is for already-canonical entries — a manual log, a correction, a backfill. Prefer letting the shortcut do the bulk; reach for the tool for one-off / fix-up writes.

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JOB 1 — INGEST ("push my watch data", "log my run / sleep / HRV")

From a free-text health note, build canonical entries and push_health_data({ entries: [...] }).

1. Pick the type + build data per the taxonomy table. "I slept 7.5 hours, 1.5 deep" → { id, ts: "<night>", type: "sleep_night", data: { value: 7.5, metadata: { deep: 1.5 } } }. "morning HRV was 62" → { type: "hrv", data: { value: 62 } }. "45-min easy run, 6 km" → { type: "workout", ts: "<ISO start>", data: { activity: "running", duration_min: 45, distance_km: 6 } }. Put the metric in data.value — that's the bug to never reintroduce.

2. Mint a stable, unique id. Use something deterministic per logical entry (sleep-2026-06-16, hrv-2026-06-16, workout-2026-06-16T0730) so a re-push of the same day overwrites rather than duplicating — re-pushing the exact same entry is an idempotent no-op (dedup is by id). For an arbitrary manual entry a short unique slug is fine (hlth-<rand>).

3. Push, then read back. push_health_data returns { accepted, duplicates, purged, total, version }. Report the accepted count, note duplicates (re-pushes that deduped to a no-op), and note purged if the 90-day retention swept old rows (entries older than 90 days auto-purge on write). A single entry is low-stakes — push it directly; a large backfill is a bulk write (confirm in approval mode).

Example. "push last night: slept 7h12m, 1h20 deep, 1h45 REM; resting HR 52; HRV 58" (auto mode) → one push_health_data with three entries: sleep_night (value 7.2, metadata.deep 1.33, metadata.rem 1.75), resting_hr (value 52), hrv (value 58), all ts: "2026-06-16", ids sleep-2026-06-16 / resting_hr-2026-06-16 / hrv-2026-06-16. Report 3 accepted, then offer a get_daily_summary for the day.

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JOB 2 — QUERY ("how did I sleep", "what's my HRV trend", "summary")

Four read tools, each for a different question. None need confirmation.

• get_daily_summary({ date }) — the headline "how was my day" read. Returns the day's workouts, sleep (night + naps), metrics (HRV / resting HR / steps), the food log with macro totals (folded in from the Nutrition add-on — see the soft-dep note), and a calorie balance (workout calories burned − calories ingested). Use it for "summarize today / yesterday", "how was my day".
• get_health_summary({ date | from, to }) — per-type aggregates over a date or range: sleep {count, avg_hours, avg_deep_hours, avg_rem_hours}, hrv {count, avg_ms}, resting_hr {count, avg_bpm}, steps {days, total, avg_per_day}, vo2max {count, latest}, workout {count, total_duration_min, total_calories, activities}. Use it for "my averages this week", "how much did I sleep on average".
• get_health_trends({ days, type? }) — daily series + deltas over the last N days (default 7). Use it for "is my HRV trending up", "show my resting HR over two weeks".
• list_health_data({ type?, from?, to?, limit? }) — the raw entries, newest first. Use it to inspect / verify individual rows (e.g. to find an id to delete in JOB 3).

Read these fields as the contract gives them — data.value for metrics, avg_ms / avg_bpm / avg_hours in the summary, etc. Don't reach for data.bpm or data.count; those are the retired split-brain shape.

Soft dependency on Nutrition. get_daily_summary's food + calorie-balance section reads the Nutrition add-on's food log. This is a soft edge — if Nutrition isn't installed/enabled, that section is simply empty (the health side is unaffected). Don't tell the user health is broken because the food block is blank; if they want it, point them at the Nutrition & Chef add-on (/nutrition-chef).

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JOB 3 — FIX ("delete that bad entry", "remove the duplicate")

When an entry is wrong or duplicated, remove it with delete_health_data. It takes either { ids: [...] } (specific entries) or { from, to } (a date range) — and it hard-removes (no soft-archive; irreversible, unlike the board's soft archive_case). Deletes are add-on-gated like pushes.

• To fix wrong VALUES, you usually don't delete — re-push the same id with the corrected data (JOB 1 dedup-by-id overwrites in place). Reach for delete_health_data to remove a row that shouldn't exist at all (a bogus reading, a duplicate, a test entry).
• Find the id first with list_health_data (filter by type / date) so you delete exactly the right row.
• A range delete ({ from, to }) is destructive and broad — in approval mode, lay out exactly what it will sweep (run a list_health_data over the same window first and show the count) and get a yes before firing.

Example. "delete that duplicate sleep entry from the 14th" → list_health_data({ type: "sleep_night", from: "2026-06-14", to: "2026-06-14" }) to find the id → delete_health_data({ ids: ["sleep-2026-06-14-dup"] }). Report it removed and the new total.

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JOB 4 — VAULT ("save my health summary to the vault")

Archive a health report into the knowledge base. This is a two-step flow — the fitness MCP composes, the vault MCP persists.

1. Compose — ingest_health_to_vault({ days? }). It composes a Markdown health summary of the last N days (default 7) and returns { vault_ingest_content, domain: "life", instruction }. It does NOT write to the vault itself — it's an ungated read that just builds the report.

2. Persist — hand it to the vault MCP. Take the returned vault_ingest_content and pass it as the content argument (with domain: "life") to the vault MCP's ingest tool. The vault ingest is async — submit, then poll ingest_status to a terminal state; never re-submit an in-flight job (see /vault-operations). Report the vault job's outcome once it lands.

Use this for "save my health summary to my knowledge base", "log this week's training to the vault". The compose step is an ungated read; the vault write is governed by the vault add-on.

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Conventions (guardrails recap)

• fitness MCP only, via the tools, for health DATA. Never bash/curl. The board UI (/fitness/health) is the read twin; you do the ingestion + the reads + the fixes + the vault archive.
• The add-on must be ENABLED for writes. A disabled add-on 404s every write ("Not found — the fitness add-on may be disabled.") while reads stay open — tell the user to flip it on at /addons; you don't enable it yourself.
• Mode (STEP 0): auto → just do it; approval → confirm bulk writes (a big backfill) and destructive ones (delete_health_data, a date-range delete) in chat before firing. A single write is low-stakes either way. There is no pending/propose queue — confirmation is conversational.
• Canonical taxonomy, always. Seven types (workout, sleep_night, sleep_nap, hrv, resting_hr, steps, vo2max); the metric goes in data.value (sleep = hours, with stages in data.metadata); workouts carry activity + duration_min on data. ts = YYYY-MM-DD for daily/sleep, full ISO for a workout. Stable unique id per logical entry so a re-push overwrites (idempotent no-op on an identical re-push). Never use data.bpm/data.count/data.ms. Entries older than 90 days auto-purge on write.
• Reads: get_daily_summary (the day, incl. nutrition + calorie balance), get_health_summary (per-type aggregates over a range), get_health_trends (the series), list_health_data (raw rows). Read the contract field names, not the producer's shape.
• Fixes are HARD. delete_health_data has no soft-archive — it's irreversible. Prefer a re-push of the same id to correct values; delete only to remove a row that shouldn't exist. Find the id with list_health_data first; confirm range deletes in approval mode.
• Vault is two steps. ingest_health_to_vault (compose, ungated read) then the vault MCP's ingest with domain: "life" (the actual write — async, poll ingest_status).
• Soft dependency on Nutrition. The food + calorie-balance parts of get_daily_summary read the Nutrition add-on; if it's off, that section is just empty — not a fault. Point the user at /nutrition-chef if they want it.
• NEVER invent biometric numbers. Only log what the user (or their export) gives you — never fabricate an HRV, a sleep duration, or a resting HR. If a value is missing, ask or omit; don't guess a vital sign.
• NOT MEDICAL ADVICE on any interpretation. Reading back a low HRV / poor sleep / elevated resting HR is an informational estimate — say so, and defer abnormal symptoms, chest pain, injury, illness, pregnancy, or an under-18 user to a physician / qualified clinician (recommend they consult one; never diagnose from the data).
• Scope. Plans → fitness-training-plan; reviews → fitness-weekly-review; briefs → fitness-pre-workout-brief; correlations → fitness-correlations; the athlete profile → fitness-athlete-profile. This skill is data-plane only.
• Report what landed: the accepted / duplicates / purged counts on a push, the day's summary + calorie balance or the trend direction on a read, the removed row + new total on a fix, and the vault job outcome on an archive — and that the ingested data feeds the coaching skills and the /fitness/health dashboard.