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在 OpenClaw 中安装
/install fitness
功能描述
Auto-learns your fitness patterns. Absorbs data from wearables, conversations, and achievements.
使用说明 (SKILL.md)
Auto-Adaptive Fitness Tracking
This skill auto-evolves. Fills in as you learn how the user trains and what affects their performance.
Rules:
- Absorb fitness mentions from ANY source (wearables, conversations, race results, gym apps)
- Detect user profile: beginner (needs guidance) vs experienced (wants data)
- Proactivity scales inversely with experience — beginners need more, athletes need less
- Never guilt missed workouts — adapt and move forward
- Check
sources.mdfor data integrations,profiles.mdfor user types,coaching.mdfor support patterns
Memory Storage
User preferences and learned data persist in: ~/fitness/memory.md
Format for memory.md:
### Sources
\x3C!-- Where fitness data comes from. Format: "source: reliability" -->
\x3C!-- Examples: apple-health: synced daily, strava: runs + races, conversation: workout mentions -->
### Schedule
\x3C!-- Detected training patterns. Format: "pattern" -->
\x3C!-- Examples: MWF strength 7am, Sat long run, Sun rest -->
### Correlations
\x3C!-- What affects their performance. Format: "factor: effect" -->
\x3C!-- Examples: sleep \x3C6h: skip day, coffee pre-workout: +intensity, alcohol: -next day -->
### Preferences
\x3C!-- How they want fitness tracked. Format: "preference" -->
\x3C!-- Examples: remind before workouts, no rest day lectures, weekly summary only -->
### Flags
\x3C!-- Signs to watch for. Format: "signal" -->
\x3C!-- Examples: "too tired", missed 3+ days, injury mention, "legs are dead" -->
### Achievements
\x3C!-- PRs, milestones, events. Format: "achievement: date" -->
\x3C!-- Examples: bench 100kg: 2024-03, first marathon: 2024-10, 30 day streak: 2024-11 -->
Empty sections = no data yet. Observe and fill.
安全使用建议
Before installing, consider: (1) Where and how will this skill get wearable and app data? Ask the publisher how connectors are authorized and whether you'll need to provide API tokens — the skill currently documents none. (2) The skill will write a local file at ~/fitness/memory.md containing health-related details; inspect this file, know where it lives, and confirm whether it is encrypted or included in backups. (3) 'Absorb data from ANY source' is broad — decide which sources you want the skill to use (e.g., only your watch, not your messages) and restrict it accordingly. (4) Ask about retention and deletion: how to remove all stored data and revoke access. (5) If you require stronger guarantees (explicit auth flows, audited connectors, server-side policy), request those from the skill author before enabling autonomous use. If you cannot get satisfactory answers, avoid installing or only enable it in a restricted testing context.
功能分析
Type: OpenClaw Skill
Name: fitness
Version: 1.0.1
The skill bundle is designed for auto-adaptive fitness tracking and coaching. All files (SKILL.md, coaching.md, profiles.md, sources.md) contain instructions and documentation solely focused on this purpose. SKILL.md instructs the agent to absorb fitness data, persist user preferences and learned data in a local file (`~/fitness/memory.md`), and adapt its coaching style. There are no instructions for unauthorized shell execution, network calls to external endpoints for exfiltration, reading sensitive system files, or any form of prompt injection designed to subvert the agent's core function for malicious purposes. The references to external data sources in `sources.md` are descriptive of potential data inputs, not commands to perform unauthorized integrations or data fetches.
能力评估
Purpose & Capability
The skill claims to integrate with wearables, race results, gym apps and conversational signals, but declares no required credentials, APIs, or connectors. A legitimate integration with services like Apple Health, Strava, Garmin, Whoop, or Oura normally requires explicit auth/config; that absence is inconsistent with the stated capabilities.
Instruction Scope
Runtime instructions instruct the agent to 'Absorb fitness mentions from ANY source' and to persist learned data in ~/fitness/memory.md. 'ANY source' is intentionally broad and grants the agent wide discretion to read whatever inputs it considers relevant (conversations, exports, possibly local files) without clear limits or consent flows described.
Install Mechanism
This is an instruction-only skill with no install spec and no code files, so nothing is automatically written to disk by an installer. That minimizes install-time risk.
Credentials
The skill requests no environment variables or credentials even though it references many third-party services. Either it relies on existing agent connectors (not documented) or it expects ad-hoc access to user-provided tokens; the lack of declared required secrets is disproportionate to the described integrations.
Persistence & Privilege
The skill will store persistent user data at ~/fitness/memory.md (explicit in SKILL.md). Persisting preferences locally is reasonable, but you should be aware this file will accumulate potentially sensitive health and lifestyle data and the skill does not describe encryption, retention, or deletion policies.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install fitness - 安装完成后，直接呼叫该 Skill 的名称或使用
/fitness触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.1
1.0.1: Preferences now persist across skill updates
v1.0.0
Initial release
元数据
常见问题
Fitness 是什么？
Auto-learns your fitness patterns. Absorbs data from wearables, conversations, and achievements. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 2261 次。
如何安装 Fitness？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install fitness」即可一键安装，无需额外配置。
Fitness 是免费的吗？
是的，Fitness 完全免费（开源免费），可自由下载、安装和使用。
Fitness 支持哪些平台？
Fitness 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Fitness？
由 Iván（@ivangdavila）开发并维护，当前版本 v1.0.1。
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