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

language=2

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

DEMO_KEY

# 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/

• 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

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

GET /api/v2/exercise/search/?term=&language=english

GET /api/v2/exerciseinfo/{id}/

GET /api/v2/exercise/?muscles={id}&language=2&status=2

GET /api/v2/exercise/?equipment={id}&language=2&status=2

GET /api/v2/exercisecategory/

GET /api/v2/muscle/

GET /fdc/v1/foods/search?query=&dataType=Foundation,SR Legacy

GET /fdc/v1/food/{fdcId}

python3 scripts/body_calc.py