Overview
- Skill Key
- harrey401/lofy-fitness
- Author
- harrey401
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/harrey401/lofy-fitness
- Latest Commit SHA
- c60b01e8f28b1fa19a6326a622e6ad7485bcd232
Fitness accountability for the Lofy AI assistant — workout logging from natural language, meal tracking with calorie/protein estimates, PR detection with Epley formula, gym reminders based on weekly targets, and progress reports. Use when logging workouts, meals, tracking fitness PRs, or generating weekly fitness summaries.
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Status
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Visibility
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直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 lofy-fitness 技能。 若已安装,则直接安装 lofy-fitness 技能。
# Fitness Tracker — Workout & Health Accountability
Tracks workouts, meals, PRs, and fitness consistency. An accountability layer that keeps the user honest through natural conversation.
## Data File: `data/fitness.json`
```json
{
"profile": { "goal": "", "weight_log": [], "start_date": null },
"workouts": [],
"meals": [],
"prs": {},
"weekly_summary": [],
"current_week": { "workout_count": 0, "target": 0, "workouts": [] }
}
```
### Workout Entry Format
```json
{
"date": "2026-02-07",
"type": "strength",
"muscle_groups": ["chest", "triceps"],
"exercises": [
{ "name": "Bench Press", "sets": [{"weight": 185, "reps": 5}] }
],
"duration_min": 60,
"notes": ""
}
```
### Meal Entry Format
```json
{
"date": "2026-02-07",
"meal": "lunch",
"description": "Chicken bowl with rice",
"estimated_calories": 650,
"estimated_protein_g": 45,
"time": "12:30"
}
```
## Parsing Natural Language
### Workouts
- "bench 185x5 185x4" → Bench Press, 2 sets: 185×5, 185×4
- "tricep pushdowns 50x12 x3" → 3 sets of 50×12
- "went for a 5k run, 28 minutes" → cardio, running, 5km, 28min
- "did legs" (no details) → log muscle group, note "details not provided", still counts
### Meals
- "had chipotle for lunch" → estimate ~650 cal, ~40g protein
- "protein shake after gym" → estimate ~200 cal, ~30g protein
- "skipped breakfast" → note it; if 3+ day pattern, gently mention
### PR Detection
After parsing workouts, check each exercise against stored PRs:
- Epley 1RM = weight × (1 + reps/30)
- If new 1RM exceeds stored PR: update and celebrate
- Only celebrate PRs, not every workout
## Instructions
1. Always read `data/fitness.json` before responding about fitness
2. Update the JSON immediately after any fitness conversation
3. Keep responses short — log confirmation + one comment
4. Nudge logic: max 1 gym reminder per day, only if behind weekly target
5. Track consistency over intensity — showing up matters more
6. If user mentions injury or pain, suggest...
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