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Fitness Nutrition

Gym workout planner and nutrition tracker. Search 690+ exercises by muscle, equipment, or category via wger. Look up macros and calories for 380,000+ foods via USDA FoodData Central. Compute BMI, TDEE, one-rep max, macro splits, and body fat — pure Python, no pip installs. Built for anyone chasing gains, cutting weight, or just trying to eat better.

Skill metadata

Reference: full SKILL.md

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:

Muscles:

Equipment:

Step 3 — Fetch and present results:

# Search exercises by name
QUERY="$1"
ENCODED=$(python3 -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" \
  | python3 -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" \
  | python3 -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" \
  | python3 -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=$(python3 -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$FOOD")
curl -s "https://api.nal.usda.gov/fdc/v1/foods/search?api_key=${API_KEY}&query=${ENCODED}&pageSize=5&dataType=Foundation,SR%20Legacy" \
  | python3 -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}" \
  | python3 -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:

• python3 scripts/body_calc.py bmi <weight_kg> <height_cm>
• python3 scripts/body_calc.py tdee <weight_kg> <height_cm> <age> <M|F> <activity 1-5>
• python3 scripts/body_calc.py 1rm <weight> <reps>
• python3 scripts/body_calc.py macros <tdee_kcal> <cut|maintain|bulk>
• python3 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