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Withings

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win4r
withings

Use when linking Withings or reading Withings body measurements, activity, sleep, workout, heart, and intraday data.

Overview

Publisherwin4r
RepositoryMuseAI-Skills
Skill namewithings
Stars
382
Forks
116
Bundled files
2
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.

  • 2 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 Withings 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/withings
  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/withings .claude/skills/withings
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Withings 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 Withings 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 Withings 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.

Withings

Query Withings body measurements, activity, sleep, and workout data via the bundled withings CLI.

When to Use

Activate when the user asks about their Withings data:

  • Body metrics (weight, BMI, body fat, blood pressure, heart rate)
  • Daily activity (steps, calories, distance, active duration)
  • Sleep sessions (score, stages, duration, breathing rate)
  • Workout sessions (run/walk/cycle/etc.)

Tooling

Three subcommands cover most needs:

SubcommandPurpose
withings statusConnection state ({ok, status, connect_url?, disconnect_url?, reason?}).
withings list-fields --category <CAT>Field list for a category (snake_case, units in the name).
withings query --category <CAT> --start-date <YYYY-MM-DD> [--end-date --interval --fields]Typed snake_case records. Returns a JSON array.

Categories

  • daily-metrics — Daily rollup of Activity (steps/calories/distance/HR) + Measures (weight/BP/body composition). Supports --interval hourly|daily|weekly (defaults to daily). Aggregation rules: sum for counters (step_count, active_energy_burned_kcal, distance_walking_running_meters), avg for hr_average_bpm, max for hr_max_bpm, last-of-bucket for body measurements (weight/BP/body fat etc.).
  • sleep — One row per sleep session (score, stages, duration, HR, breathing).
  • workout — One row per workout session (translated workout_type name, duration, distance, calories, HR).

Use list-fields to discover available fields per category.

Output

JSON to stdout. Datetimes are local YYYY-MM-DD HH:MM:SS per record's timezone. Session rows include id (prefixed withings_<id>), start_datetime, end_datetime, timezone. daily-metrics buckets include date / hour / week_start + record_count instead of id.

Examples

bash
# Recent workouts
withings query --category workout --start-date 2026-05-01

# Weight trend (last reading per day)
withings query --category daily-metrics --start-date 2026-04-01 --fields body_mass_kg

# Weekly step totals
withings query --category daily-metrics --start-date 2026-04-01 --interval weekly --fields step_count

Auth

Before any Withings API call:

  1. Run withings status.
  2. If status is not_connected, share [Connect Withings](<connect_url>) exactly — never paste the raw URL.
  3. After the user completes the callback, re-run withings status and proceed only when connected.

For disconnect: run withings disconnect and share [Disconnect Withings](<disconnect_url>) if present.

Never print tokens or credentials.

Operating Rules

  1. Treat linking as one-time onboarding — don't re-prompt for auth unless calls keep failing.
  2. Prefer query --category <CAT> over the legacy commands. Field names are snake_case with units in the name (body_mass_kg, step_count, hr_average_bpm, sleep_total_duration_sec) — never expose Withings's numeric meas-type IDs to the user.
  3. If unsure of a field name, run withings list-fields --category <CAT> BEFORE composing query --fields. Withings-native names (calories, distance, hr_average, weight) are NOT valid — they're translated to snake_case with units (energy_burned_kcal, distance_meters, hr_average_bpm, body_mass_kg). An empty filter result usually means the field name was wrong, not that the data is missing.
  4. Default to the last 7 days when the user asks for recent trends without specifying dates.
  5. For broad trends, use --category daily-metrics --interval weekly instead of pulling every reading. The --interval flag already aggregates per the rules above — do NOT apply additional aggregation (sum/avg) to the bucketed output client-side.
  6. If query returns [], say so clearly — never invent values. If the user is asking for a metric not surfaced by list-fields, drop to the legacy escape hatch (measures --meas-types <ID> or a more specific subcommand like heart-list; see references/commands.md for the meas-type ID table).

Legacy / Advanced Commands

Pre-query per-endpoint passthroughs that return raw {ok, status, body: <Withings native>} envelopes. Use only as an escape hatch for data not exposed by query — e.g. a meas-type missing from the translation table (withings measures --meas-types 130 for AFib ECG result), high-frequency sleep sensor data (withings sleep), or ECG signals (withings heart-list / heart-get).

Quick reference for the other passthroughs:

  • withings activity — raw daily Activity rows.
  • withings sleep-summary — raw nightly sleep summaries with Withings-native field names.
  • withings intraday — minute-resolution activity sensor data.
  • withings devices — list paired Withings devices.

Full command matrix, meas-type IDs, and Withings → Muse field mapping: references/commands.md.

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 Withings AI skill do?

Use when linking Withings or reading Withings body measurements, activity, sleep, workout, heart, and intraday data.

Why use Withings on TypingMind?

Because you install it once and use it with any model. Withings 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 Withings in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/win4r/MuseAI-Skills/tree/main/opt/hatch/skills/withings. 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 Withings?

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 Withings?

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

Is the Withings 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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