القائمة
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/NousResearch/hermes-agent --skill fitness-nutritionيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
| name | fitness-nutrition |
| description | Workout planning, macros, and body metrics via wger/USDA. |
| platforms | ["linux","macos","windows"] |
| version | 1.0.0 |
| author | Hailey Marshall (haileymarshall), Hermes Agent |
| authors | ["haileymarshall"] |
| license | MIT |
| metadata | {"hermes":{"tags":["health","fitness","nutrition","gym","workout","diet","exercise"],"category":"health","prerequisites":{"commands":["curl","python"]}}} |
| 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}] |
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):
DEMO_KEY works instantly; free signup for higher limits.Offline calculators (pure stdlib Python):
Trigger this skill when the user asks about:
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:
/api/v2/exercise/?muscles={id}&language=2&status=2&format=json/api/v2/exercise/?category={id}&language=2&status=2&format=json/api/v2/exercise/?equipment={id}&language=2&status=2&format=json/api/v2/exercise/search/?term={query}&language=english&format=json/api/v2/exerciseinfo/{exercise_id}/?format=jsonStep 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="$1"
ENCODED=$(python -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" \
| python -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" \
| python -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" \
| python -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',[])}\")
"
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=$(python -c "import urllib.parse,sys; print(urllib.parse.quote(sys.argv[1]))" "$FOOD")
curl -s "https://api.nal.usda.gov/fdc/v1/foods/search?api_key=${API_KEY}&query=${ENCODED}&pageSize=5&dataType=Foundation,SR%20Legacy" \
| python -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}" \
| python -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','')}\")
"
Use the helper scripts in scripts/ for batch operations,
or run inline for single calculations:
python scripts/body_calc.py bmi <weight_kg> <height_cm>python scripts/body_calc.py tdee <weight_kg> <height_cm> <age> <M|F> <activity 1-5>python scripts/body_calc.py 1rm <weight> <reps>python scripts/body_calc.py macros <tdee_kcal> <cut|maintain|bulk>python 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.
language=2 for Englishstatus=2 to only get approved exercisesDEMO_KEY has 30 req/hour — add sleep 2 between batch requests or get a free keyexercise/search endpoint uses term not query as the parameter nameAfter 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).
| 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 | python scripts/body_calc.py |