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.

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Repository: lukemcqueen/hermes-cortex
Last source activity: August 1, 2026 at 18:57
Detected SKILL.md language: English
Stars: 4
Forks: 1

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name	fitness-nutrition
description	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.
platforms	["linux","macos","windows"]
version	1.0.0
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

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

# 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',[])}\")
"

# 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','')}\")
"

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`