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joaodriessen

Health Training Frontdoor

by Joao Driessen · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install health-training-frontdoor
Description
Narrow first-class front door for live Fitbit/training retrieval via stable JSON actions.
README (SKILL.md)

Health/Training Front Door

Use this when OpenClaw needs live Fitbit/health/training data in a stable, low-ambiguity way.

This is a thin, typed front door over the canonical Fitbit connector tooling.

Why this exists

fitbit_tools.py is the canonical operational backend, but it still requires low-level CLI composition. This front door provides a narrow action contract so agents can call one stable interface instead of assembling raw shell commands every time.

Contract

Run:

  • node skills/health-training-frontdoor/scripts/request.js '{...json...}'

Input JSON:

{
  "action": "latest_recovery"
}

Supported actions:

  • auth_status
  • latest_recovery
  • quality_flags
  • training_status
  • training_window
  • unified_latest

Optional fields:

  • days (integer)
  • ensureFresh (boolean)
  • source (for unified_latest, default best)

Default behavior by action

  • latest_recovery: fetches latest days of hrv_rmssd,resting_hr,sleep_minutes,sleep_score,data_quality; defaults days=3, ensureFresh=true
  • quality_flags: defaults days=7
  • training_status: defaults days=14, ensureFresh=true
  • training_window: defaults days=14, ensureFresh=true
  • unified_latest: defaults days=14, source=best

Notes

  • Output is compact JSON.
  • This surface is read-only.
  • Interpretation/coaching remains outside this skill.

Training Programming Reference

All programming decisions must be grounded in Practical Programming for Strength Training (Rippetoe & Baker, 3rd Ed.).

Always load before giving any programming advice:

  1. reference/practical-programming/INDEX.md — who Joao is, relevant chapters, reading guide
  2. memory/training-continuity.md — current lifts, recent sessions, health signals

For any programming question, read the relevant sections of reference/practical-programming/book.md directly. The book is the authority.

Backend mapping

This front door maps directly to:

  • fitbit-connector/scripts/fitbit_tools.py

Do not use this to bypass auth or write-capable operations.

Usage Guidance
This skill is a small wrapper that calls an existing fitbit_tools.py and returns JSON; that is consistent with its stated purpose. Before installing, verify: 1) where fitbit-connector/scripts/fitbit_tools.py lives in your workspace and that it is trusted; 2) what environment variables or local config that python tool requires (the skill does not declare them but will forward process.env to the child process); 3) the contents of reference/practical-programming/INDEX.md and memory/training-continuity.md mentioned in SKILL.md — they may contain sensitive personal data and the SKILL.md explicitly asks the agent to load them before giving programming advice. If you proceed, ensure only expected credentials are present in the agent environment and audit the referenced fitbit connector code to confirm it is read-only and does not leak data to external endpoints.
Capability Analysis
Type: OpenClaw Skill Name: health-training-frontdoor Version: 1.0.0 The skill acts as a structured wrapper for a local Fitbit data connector. The Node.js script (scripts/request.js) safely executes a Python backend using spawnSync with hardcoded paths and validated arguments, minimizing shell injection risks. The instructions in SKILL.md are consistent with the stated purpose of health data retrieval and RAG-based coaching, with no evidence of malicious intent, data exfiltration, or unauthorized access.
Capability Assessment
Purpose & Capability
Name/description match the code: the Node script is a thin wrapper that invokes fitbit-connector/scripts/fitbit_tools.py and returns JSON. That behavior is coherent with a read-only 'front door' for Fitbit/training retrieval. However the skill does not declare any Fitbit-related environment variables or credentials even though the backend connector almost certainly requires them, which is a proportionality/visibility gap.
Instruction Scope
SKILL.md instructs the agent to "Always load" local content including reference/practical-programming/INDEX.md and memory/training-continuity.md before giving programming advice. Loading memory/training-continuity.md could expose sensitive, user-specific training data; this goes beyond the narrow read-only data-retrieval claim and is a scope creep/conflict (the doc also says "Interpretation/coaching remains outside this skill").
Install Mechanism
There is no install spec (instruction-only plus a small JS script). Nothing is downloaded or written to disk by the skill itself.
Credentials
The wrapper passes process.env through to the child python process and does not declare required env vars. That means any credentials present in the agent environment will be available to the invoked fitbit_tools.py; this may be expected but should be explicit (the skill should document which FITBIT_* or connector-specific env vars it needs).
Persistence & Privilege
always:false and no special persistence or config changes. The skill does spawn a child process but does not request forced always-on privileges or attempt to modify other skills or global agent settings.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install health-training-frontdoor
  3. After installation, invoke the skill by name or use /health-training-frontdoor
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
health-training-frontdoor v1.0.0 - Introduced a typed front door for live Fitbit/health/training data retrieval using stable JSON actions. - Provides a simplified, read-only API over the canonical Fitbit connector, reducing the need for raw CLI command assembly. - Supports stable actions: `auth_status`, `latest_recovery`, `quality_flags`, `training_status`, `training_window`, and `unified_latest` with clear default behaviors and optional fields. - Ensures all programming advice is grounded in "Practical Programming for Strength Training" and references user-specific context before making recommendations. - Guarantees compact JSON outputs and direct mapping to backend tools, without bypassing authentication or allowing data writes.
Metadata
Slug health-training-frontdoor
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Health Training Frontdoor?

Narrow first-class front door for live Fitbit/training retrieval via stable JSON actions. It is an AI Agent Skill for Claude Code / OpenClaw, with 111 downloads so far.

How do I install Health Training Frontdoor?

Run "/install health-training-frontdoor" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Health Training Frontdoor free?

Yes, Health Training Frontdoor is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Health Training Frontdoor support?

Health Training Frontdoor is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Health Training Frontdoor?

It is built and maintained by Joao Driessen (@joaodriessen); the current version is v1.0.0.

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