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Google Health Connect

Community
win4r
google_health_connect

The user's synced Google Health Connect data from their Android device: daily metrics (steps, distance, calories, heart rate, HRV, VO2max), sleep sessions (stages, quality, efficiency), and workouts.

Overview

Publisherwin4r
RepositoryMuseAI-Skills
Skill namegoogle_health_connect
Stars
382
Forks
116
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by win4r on GitHub. Read the source before you install it.

Installation

Install the Google Health Connect AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/win4r/MuseAI-Skills/tree/main/opt/hatch/skills/google-health-connect
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/win4r/MuseAI-Skills.git /tmp/MuseAI-Skills
mkdir -p .claude/skills
cp -r /tmp/MuseAI-Skills/opt/hatch/skills/google-health-connect .claude/skills/google_health_connect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Google Health Connect in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Google Health Connect on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Google Health Connect is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Health Connect

Read the user's synced Google Health Connect data with the health-cli binary. Every command below takes --provider healthconnect.

This skill serves Android devices only. If the user's paired device is not an Android device, it has no data for them — check the platform field returned by device.list when you are unsure.

When to Use

When the user asks about their synced Health Connect data, sync status, or sources:

  • All-day metrics/vitals: steps, distance, calories, heart rate, HRV, VO2max.
  • Sleep sessions: stages, quality, efficiency, awakenings.
  • Workouts: type, duration, calories, distance, HR.

Tooling

Binary: health-cli. --provider healthconnect is required on every command below — it is not optional and has no default; omitting it is a usage error. Commands group by data shape under query:

  • query metrics --provider healthconnect — all-day metric rollups
  • query samples --provider healthconnect — raw, unbucketed data points (intraday HR/steps, GPS, sleep stages)
  • query sessions --provider healthconnect — discrete sessions: sleep, workout
  • status --provider healthconnect — data-sync status
  • auth connect --provider healthconnect, auth disconnect --provider healthconnect
  • delete — erase stored records (destructive; see below)

Multi-device (rare): when a window spans >1 device the envelope adds "multi_node": true — query metrics returns per-device node_groups; query samples/sessions add a node_id to each record (samples CSV gains a leading node_id column). Single-device output is unchanged; never sum or double-count across devices.

query metrics — all-day metric rollups

bash
health-cli query metrics --provider healthconnect \
  --start-date <YYYY-MM-DD> [--end-date <YYYY-MM-DD>] \
  [--interval hourly|daily|weekly] [--fields a,b,c] [--timeout-secs N]

daily-metrics is the only domain so there is no --category. One row per --interval bucket (default daily). --start-date required; --end-date defaults to today. Output is an envelope { "coverage": {...}, "records": [...] } (see Coverage below).

--list-fields reads the metric field names from the synced data → { ok, provider, category, observed_count, fields: [{ name, observed, count? }] }. observed: true (with a row count) means the name is present in this user's data — those are exactly the names --fields matches. observed: false means the build knows the field but nothing has synced yet. If the field set can't be read the response carries degraded: true and every observed is null.

query sessions — discrete sessions

bash
health-cli query sessions --provider healthconnect --category sleep|workout \
  --start-date <YYYY-MM-DD> [--end-date <YYYY-MM-DD>] [--fields a,b,c]
  • sleep — one row per sleep session.
  • workout — one row per workout/activity.

Sessions are never bucketed (no --interval). Discovery: --list-categories → { ok, provider, categories }; --list-fields (needs --category) → { ok, provider, category, fields } (e.g. workout has is_indoor).

Output: normalized snake_case records; the same concept uses the same field name (e.g. workout average_speed_mps, elevation_gain_meters, hr_average_bpm). Session records carry a provider-prefixed id (healthconnect_…) + start_datetime/end_datetime/timezone; metric buckets carry date/hour/week_start + record_count.

Coverage: queries return an envelope { "coverage": {...}, "records": [...] }. coverage.complete is false when days in the window aren't synced from the device; coverage.warning then holds the exact backfill_data_source command to run. Surface that warning to the user / act on it — results are partial until the backfill completes.

query samples — raw, unbucketed data points

bash
health-cli query samples --provider healthconnect --start-date <YYYY-MM-DD> \
  [--end-date <YYYY-MM-DD>] [--start-time <HH:MM[:SS]>] [--end-time <HH:MM[:SS]>] \
  [--fields <type1,...>] [--limit <n>] [--format stdout|csv] [--output <path>]
health-cli query samples --provider healthconnect --list-fields   # discover sample types

Individual samples — finer than the query metrics rollups (intraday HR/steps, GPS, sleep-stage timelines; query sessions gives stage totals only). --fields selects sample types (not output columns) — run --list-fields to see the types this user has synced. GPS is location (raw lat/lon — don't infer place names from it). For dense reads use --output <path>: it writes CSV into the agent filesystem for a later script step and prints only a summary, keeping thousands of rows out of context.

status — data-sync status

bash
health-cli status --provider healthconnect [--timeout-secs N] \
  [--start-date <YYYY-MM-DD>] [--end-date <YYYY-MM-DD>] [--check-missing-entries]

Returns { categories: [{ name, record_count, earliest_datetime, latest_datetime }] }. With --check-missing-entries (requires --start-date): a deep per-30-min-interval gap report (unsynced_dates, missing_intervals, …).

auth connect

bash
health-cli auth connect --provider healthconnect [--timeout-secs N]

Run this and follow the instruction to connect to Health Connect data.

auth disconnect

bash
health-cli auth disconnect --provider healthconnect [--timeout-secs N]

Health Connect cannot be disconnected through this CLI or an in-chat widget. Tell the user to open Settings → Connectors → Health Connect → Manage, then revoke the app's Health Connect permissions in Android settings.

delete — erase stored Health Connect records

bash
health-cli delete --provider healthconnect --all             # every Health Connect record
health-cli delete --provider healthconnect --device NODE_ID  # one paired device
health-cli delete --provider healthconnect --start-date 2026-03-01 --end-date 2026-03-31

Use for any request to delete, remove, erase, wipe, or clear Health Connect data. Never improvise it by moving files, disconnecting the connector, or writing to the database — those neither remove records nor leave an audit trail. A delete never spans providers; to clear both, run it once per provider.

--all takes no other filter; --device/--start-date/--end-date are used instead of it. All record kinds go at once — no per-category or per-record-ID delete. Dates are YYYY-MM-DD in JARVIS_USER_TIMEZONE or epoch seconds; a record is in range when it starts before the end and ends after the start, so a session straddling a bound is included. --start-date alone deletes from that day onward, --end-date alone up to and including it.

Destructive and irreversible, behind a fresh one-time approval showing the normalized filters. Run it once per request — settle provider, device and dates in conversation first. Denial exits code 3 with nothing deleted; that is final, so report it rather than retrying other filters or the other provider.

Zero is a success, not a miss — nothing matched, so say so and stop rather than widening dates or re-running with --all. Report deleted.records, not total_rows, which also counts child value rows. If Health Connect on the device still holds the records, a later sync can bring them back.

Auth

Device-synced; no login. Data availability depends on the Health Connect permission being approved on the user's Android device + sync. Use status to see synced ranges and the backfill_data_source device action (surfaced in query coverage.warning) to pull a range.

Operating Rules

  1. Pick the command by data shape: query metrics (all-day rollups), query samples (raw points — only when individual samples matter; prefer metrics for trends), or query sessions --category sleep|workout (discrete events).
  2. Always pass --provider healthconnect and --start-date; --end-date defaults to today. Use current-date context for relative asks ("today", "last week"); use sample time bounds for narrow intraday windows.
  3. Coverage: if a query metrics/sessions returns coverage.complete: false, tell the user the data is partial and relay/act on coverage.warning (run the backfill_data_source action, then re-query).
  4. query metrics defaults to --interval daily; sleep spans midnight (include both evening start and morning end dates).
  5. On empty results, the range likely has no data; widen it, or check status / coverage and backfill.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Google Health Connect AI skill do?

The user's synced Google Health Connect data from their Android device: daily metrics (steps, distance, calories, heart rate, HRV, VO2max), sleep sessions (stages, quality, efficiency), and workouts.

Why use Google Health Connect on TypingMind?

Because you install it once and use it with any model. Google Health Connect is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Google Health Connect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/win4r/MuseAI-Skills/tree/main/opt/hatch/skills/google-health-connect. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Google Health Connect?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Google Health Connect?

As many as you like. As long as a model supports skills, you can use Google Health Connect with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Google Health Connect AI skill free?

It is published on GitHub by win4r. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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