fitness-nutrition

name fitness-nutrition description Workout planning, macros, and body metrics via wger/USDA. platforms ["linux","macos","windows"] version 1.0.0 author Hailey Marshall (haileymarshall), Hermes Agent authors ["haileymarshall"] license MIT metadata {"hermes":{"tags":["health","fitness","nutrition","gym","workout","diet","exercise"],"category":"health","prerequisites":{"commands":"[Truncated]"}}} required_environment_variables [{"name":"USDA_API_KEY","prompt":"USDA FoodData Central API key (free)","help":"Get one free at https://fdc.nal.usda.gov/api-key-signup/ — or skip to use DEMO_KEY with lower rate limits","required_for":"higher rate limits on food/nutrition lookups (DEMO_KEY works without signup)","optional":true}] Fitness & Nutrition Expert fitness coach and sports nutritionist skill. Two data sources plus offline calculators — everything a gym-goer needs in one place. Data sources (all free, no pip dependencies): wger ( https://wger.de/api/v2/ ) — open exercise database, 690+ exercises with muscles, equipment, images. Public endpoints need zero authentication. USDA FoodData Central ( https://api.nal.usda.gov/fdc/v1/ ) — US government nutrition database, 380,000+ foods. DEMO_KEY works instantly; free signup for higher limits. Offline calculators (pure stdlib Python): BMI, TDEE (Mifflin-St Jeor), one-rep max (Epley/Brzycki/Lombardi), macro splits, body fat % (US Navy method) When to Use Trigger this skill when the user asks about: Exercises, workouts, gym routines, muscle groups, workout splits Food macros, calories, protein content, meal planning, calorie counting Body composition: BMI, body fat, TDEE, caloric surplus/deficit One-rep max estimates, training percentages, progressive overload Macro ratios for cutting, bulking, or maintenance Procedure Exercise Lookup (wger API) All wger public endpoints return JSON and require no auth. Always add format=json and language=2 (English) to exercise queries. Step 1 — Identify what the user wants: By muscle → use /api/v2/exercise/?muscles={id}&language=2&status=2&format=json By category → use /api/v2/exercise/?category={id}&language=2&status=2&format=json By equipment → use /api/v2/exercise/?equipment={id}&language=2&status=2&format=json By name → use /api/v2/exercise/search/?term={query}&language=english&format=json Full details → use /api/v2/exerciseinfo/{exercise_id}/?format=json Step 2 — Reference IDs (so you don't need extra API calls): Exercise categories: ID Category 8 Arms 9 Legs 10 Abs 11 Chest 12 Back 13 Shoulders 14 Calves 15 Cardio Muscles: ID Muscle ID Muscle 1 Biceps brachii 2 Anterior deltoid 3 Serratus anterior 4 Pectoralis major 5 Obliquus externus 6 Gastrocnemius 7 Rectus abdominis 8 Gluteus maximus 9 Trapezius 10 Quadriceps femoris 11 Biceps femoris 12 Latissimus dorsi 13 Brachialis 14 Triceps brachii 15 Soleus Equipment: ID Equipment 1 Barbell 3 Dumbbell 4 Gym mat 5 Swiss Ball 6 Pull-up bar 7 none (bodyweight) 8 Bench 9 Incline bench 10 Kettlebell Step 3 — Fetch and present results: # Search exercises by name QUERY= " $1 " ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" " $QUERY " ) curl -s "https://wger.de/api/v2/exercise/search/?term= ${ENCODED} &language=english&format=json" \ | python -c " import json,sys data=json.load(sys.stdin) for s in data.get('suggestions',[])[:10]: d=s.get('data',{}) print(f\" ID {d.get('id','?'):>4} | {d.get('name','N/A'):<35} | Category: {d.get('category','N/A')}\") " # Get full details for a specific exercise EXERCISE_ID= " $1 " curl -s "https://wger.de/api/v2/exerciseinfo/ ${EXERCISE_ID} /?format=json" \ | python -c " import json,sys,html,re data=json.load(sys.stdin) trans=[t for t in data.get('translations',[]) if t.get('language')==2] t=trans[0] if trans else data.get('translations',[{}])[0] desc=re.sub('<[^>]+>','',html.unescape(t.get('description','N/A'))) print(f\"Exercise : {t.get('name','N/A')}\") print(f\"Category : {data.get('category',{}).get('name','N/A')}\") print(f\"Primary : {', '.join(m.get('name_en','') for m in data.get('muscles',[])) or 'N/A'}\") print(f\"Secondary : {', '.join(m.get('name_en','') for m in data.get('muscles_secondary',[])) or 'none'}\") print(f\"Equipment : {', '.join(e.get('name','') for e in data.get('equipment',[])) or 'bodyweight'}\") print(f\"How to : {desc[:500]}\") imgs=data.get('images',[]) if imgs: print(f\"Image : {imgs[0].get('image','')}\") " # List exercises filtering by muscle, category, or equipment # Combine filters as needed: ?muscles=4&equipment=1&language=2&status=2 FILTER= " $1 " # e.g. "muscles=4" or "category=11" or "equipment=3" curl -s "https://wger.de/api/v2/exercise/? ${FILTER} &language=2&status=2&limit=20&format=json" \ | python -c " import json,sys data=json.load(sys.stdin) print(f'Found {data.get(\"count\",0)} exercises.') for ex in data.get('results',[]): print(f\" ID {ex['id']:>4} | muscles: {ex.get('muscles',[])} | equipment: {ex.get('equipment',[])}\") " Nutrition Lookup (USDA FoodData Central) Uses USDA_API_KEY env var if set, otherwise falls back to DEMO_KEY . DEMO_KEY = 30 requests/hour. Free signup key = 1,000 requests/hour. # Search foods by name FOOD= " $1 " API_KEY= " ${USDA_API_KEY:-DEMO_KEY} " ENCODED=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" " $FOOD " ) curl -s "https://api.nal.usda.gov/fdc/v1/foods/search?api_key= ${API_KEY} &query= ${ENCODED} &pageSize=5&dataType=Foundation,SR%20Legacy" \ | python -c " import json,sys data=json.load(sys.stdin) foods=data.get('foods',[]) if not foods: print('No foods found.'); sys.exit() for f in foods: n={x['nutrientName']:x.get('value','?') for x in f.get('foodNutrients',[])} cal=n.get('Energy','?'); prot=n.get('Protein','?') fat=n.get('Total lipid (fat)','?'); carb=n.get('Carbohydrate, by difference','?') print(f\"{f.get('description','N/A')}\") print(f\" Per 100g: {cal} kcal | {prot}g protein | {fat}g fat | {carb}g carbs\") print(f\" FDC ID: {f.get('fdcId','N/A')}\") print() " # Detailed nutrient profile by FDC ID FDC_ID= " $1 " API_KEY= " ${USDA_API_KEY:-DEMO_KEY} " curl -s "https://api.nal.usda.gov/fdc/v1/food/ ${FDC_ID} ?api_key= ${API_KEY} " \ | python -c " import json,sys d=json.load(sys.stdin) print(f\"Food: {d.get('description','N/A')}\") print(f\"{'Nutrient':<40} {'Amount':>8} {'Unit'}\") print('-'*56) for x in sorted(d.get('foodNutrients',[]),key=lambda x:x.get('nutrient',{}).get('rank',9999)): nut=x.get('nutrient',{}); amt=x.get('amount',0) if amt and float(amt)>0: print(f\" {nut.get('name',''):<38} {amt:>8} {nut.get('unitName','')}\") " Offline Calculators Use the helper scripts in scripts/ for batch operations, or run inline for single calculations: python scripts/body_calc.py bmi <weight_kg> <height_cm> python scripts/body_calc.py tdee <weight_kg> <height_cm> <age> <M|F> <activity 1-5> python scripts/body_calc.py 1rm <weight> <reps> python scripts/body_calc.py macros <tdee_kcal> <cut|maintain|bulk> python scripts/body_calc.py bodyfat <M|F> <neck_cm> <waist_cm> [hip_cm] <height_cm> See references/FORMULAS.md for the science behind each formula. Pitfalls wger exercise endpoint returns all languages by default — always add language=2 for English wger includes unverified user submissions — add status=2 to only get approved exercises USDA DEMO_KEY has 30 req/hour — add sleep 2 between batch requests or get a free key USDA data is per 100g — remind users to scale to their actual portion size BMI does not distinguish muscle from fat — high BMI in muscular people is not necessarily unhealthy Body fat formulas are estimates (±3-5%) — recommend DEXA scans for precision 1RM formulas lose accuracy above 10 reps — use sets of 3-5 for best estimates wger's exercise/search endpoint uses term not query as the parameter name Verification After running exercise search: confirm results include exercise names, muscle groups, and equipment. After nutrition lookup: confirm per-100g macros are returned with kcal, protein, fat, carbs. After calculators: sanity-check outputs (e.g. TDEE should be 1500-3500 for most adults). Quick Reference Task Source Endpoint Search exercises by name wger GET /api/v2/exercise/search/?term=&language=english Exercise details wger GET /api/v2/exerciseinfo/{id}/ Filter by muscle wger GET /api/v2/exercise/?muscles={id}&language=2&status=2 Filter by equipment wger GET /api/v2/exercise/?equipment={id}&language=2&status=2 List categories wger GET /api/v2/exercisecategory/ List muscles wger GET /api/v2/muscle/ Search foods USDA GET /fdc/v1/foods/search?query=&dataType=Foundation,SR Legacy Food details USDA GET /fdc/v1/food/{fdcId} BMI / TDEE / 1RM / macros offline python scripts/body_calc.py