fitness-nutrition✓ تایید شده

محتوای 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
---

# 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:**

```bash
# 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')}\")
"
```

```bash
# 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','')}\")
"
```

```bash
# 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',[])}\")
"
```

### 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.

```bash
# 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()
"
```

```bash
# 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','')}\")
"
```

### Offline Calculators

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.

---

## 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 | `python scripts/body_calc.py` |

مجوز

مجوز اعلام‌شده: MIT

MIT License

Copyright (c) 2025 Hailey Marshall (haileymarshall), Hermes Agent

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

مشاهدهٔ مجوز در مخزن منبع — نسخهٔ منتشرشده در آن‌جا مرجع است.