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.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
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.
[{"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)
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:
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
