Gym

Log workouts, plan routines, track progress, and get intelligent coaching for any fitness level.

Safety Notice

This listing is from the official public ClawHub registry. Review SKILL.md and referenced scripts before running.

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Install skill "Gym" with this command: npx skills add ivangdavila/gym

Quick Reference

TopicFile
Routines, exercises, templatesworkouts.md
Progress tracking, volume, PRsprogress.md
Injury adaptation, modificationsadaptation.md
Gym nutrition, macros, timingnutrition.md

User Profile

User preferences persist in ~/gym/memory.md. Create on first use:

## Level
<!-- beginner | intermediate | advanced -->

## Goals
<!-- strength | hypertrophy | fat-loss | general-fitness | powerlifting -->

## Schedule
<!-- Days available. Format: "days | frequency" -->
<!-- Examples: Mon/Wed/Fri, 3x/week, daily -->

## Session Duration
<!-- 45min | 60min | 90min -->

## Restrictions
<!-- Injuries, equipment limits, mobility issues -->
<!-- Examples: Lower back injury (no deadlifts), Home gym (no cable machine) -->

Fill on first conversation. Update as goals evolve.

Data Storage

Store workout logs and measurements in ~/gym/:

  • workouts — Session logs (date, exercises, sets, reps, weight)
  • prs — Personal records by exercise
  • measurements — Body measurements, weight trends

Core Rules

  • Always check Restrictions before suggesting exercises
  • Compound movements first in every session (squat, deadlift, press, row, pull-up)
  • Progressive overload: suggest +2.5kg or +1-2 reps when previous session was completed
  • Rest periods: 2-3min for strength, 60-90s for hypertrophy, 30-45s for conditioning
  • Never increase load >10% week-over-week — injury risk
  • Deload week every 4-6 weeks or when user reports persistent fatigue
  • If user misses days, adapt — don't guilt, just recalculate
  • Track RPE when mentioned — use for auto-regulation
  • Warn if training same muscle group <48h apart without recovery strategy

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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