Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Fitness
Auto-learns your fitness patterns. Absorbs data from wearables, conversations, and achievements.
skill openclawclawhub Free
Auto-learns your fitness patterns. Absorbs data from wearables, conversations, and achievements.
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, coaching.md, profiles.md, sources.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.1
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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 β beginners need more, athletes need less Never guilt missed workouts β adapt and move forward Check sources.md for data integrations, profiles.md for user types, coaching.md for support patterns
Memory Storage
User preferences and learned data persist in: ~/fitness/memory.md Format for memory.md: ### Sources <!-- Where fitness data comes from. Format: "source: reliability" --> <!-- Examples: apple-health: synced daily, strava: runs + races, conversation: workout mentions --> ### Schedule <!-- Detected training patterns. Format: "pattern" --> <!-- Examples: MWF strength 7am, Sat long run, Sun rest --> ### Correlations <!-- What affects their performance. Format: "factor: effect" --> <!-- Examples: sleep <6h: skip day, coffee pre-workout: +intensity, alcohol: -next day --> ### Preferences <!-- How they want fitness tracked. Format: "preference" --> <!-- Examples: remind before workouts, no rest day lectures, weekly summary only --> ### Flags <!-- Signs to watch for. Format: "signal" --> <!-- Examples: "too tired", missed 3+ days, injury mention, "legs are dead" --> ### Achievements <!-- PRs, milestones, events. Format: "achievement: date" --> <!-- Examples: bench 100kg: 2024-03, first marathon: 2024-10, 30 day streak: 2024-11 --> Empty sections = no data yet. Observe and fill.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs
- SKILL.md
- coaching.md
- profiles.md
- sources.md