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

Source	Optional — install with hermes skills install official/health/fitness-nutrition
Path	optional-skills/health/fitness-nutrition
Version	1.0.0
License	MIT
Tags	health, fitness, nutrition, gym, workout, diet, exercise

Reference: full SKILL.md

info

The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

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="$1"
ENCODED=$(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

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	python3 scripts/body_calc.py