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feat(skills): add baoyu-infographic skill — 21 layouts × 21 styles #9901

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feat(skills): add fitness-nutrition skill to optional-skills
haileymarshall Apr 14, 2026
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feat(skills): add baoyu-infographic skill — 21 layouts × 21 styles
teknium1 Apr 14, 2026
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docs: add PORT_NOTES.md for baoyu-infographic
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255 changes: 255 additions & 0 deletions optional-skills/health/fitness-nutrition/SKILL.md
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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.
version: 1.0.0
authors:
- haileymarshall
license: MIT
metadata:
hermes:
tags: [health, fitness, nutrition, gym, workout, diet, exercise]
category: health
prerequisites:
commands: [curl, python3]
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="$1"
ENCODED=$(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')}\")
"
```

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

```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" \
| 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.

```bash
# 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()
"
```

```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}" \
| 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` |
100 changes: 100 additions & 0 deletions optional-skills/health/fitness-nutrition/references/FORMULAS.md
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# Formulas Reference

Scientific references for all calculators used in the fitness-nutrition skill.

## BMI (Body Mass Index)

**Formula:** BMI = weight (kg) / height (m)²

| Category | BMI Range |
|-------------|------------|
| Underweight | < 18.5 |
| Normal | 18.5 – 24.9 |
| Overweight | 25.0 – 29.9 |
| Obese | 30.0+ |

**Limitation:** BMI does not distinguish muscle from fat. A muscular person
can have a high BMI while being lean. Use body fat % for a better picture.

Reference: Quetelet, A. (1832). Keys et al., Int J Obes (1972).

## TDEE (Total Daily Energy Expenditure)

Uses the **Mifflin-St Jeor equation** — the most accurate BMR predictor for
the general population according to the ADA (2005).

**BMR formulas:**

- Male: BMR = 10 × weight(kg) + 6.25 × height(cm) − 5 × age + 5
- Female: BMR = 10 × weight(kg) + 6.25 × height(cm) − 5 × age − 161

**Activity multipliers:**

| Level | Description | Multiplier |
|-------|--------------------------------|------------|
| 1 | Sedentary (desk job) | 1.200 |
| 2 | Lightly active (1-3 days/wk) | 1.375 |
| 3 | Moderately active (3-5 days) | 1.550 |
| 4 | Very active (6-7 days) | 1.725 |
| 5 | Extremely active (2x/day) | 1.900 |

Reference: Mifflin et al., Am J Clin Nutr 51, 241-247 (1990).

## One-Rep Max (1RM)

Three validated formulas. Average of all three is most reliable.

- **Epley:** 1RM = w × (1 + r/30)
- **Brzycki:** 1RM = w × 36 / (37 − r)
- **Lombardi:** 1RM = w × r^0.1

All formulas are most accurate for r ≤ 10. Above 10 reps, error increases.

Reference: LeSuer et al., J Strength Cond Res 11(4), 211-213 (1997).

## Macro Splits

Recommended splits based on goal:

| Goal | Protein | Fat | Carbs | Calorie Offset |
|-------------|---------|------|-------|----------------|
| Fat loss | 40% | 30% | 30% | −500 kcal |
| Maintenance | 30% | 30% | 40% | 0 |
| Lean bulk | 30% | 25% | 45% | +400 kcal |

Protein targets for muscle growth: 1.6–2.2 g/kg body weight per day.
Minimum fat intake: 0.5 g/kg to support hormone production.

Conversion: Protein = 4 kcal/g, Fat = 9 kcal/g, Carbs = 4 kcal/g.

Reference: Morton et al., Br J Sports Med 52, 376–384 (2018).

## Body Fat % (US Navy Method)

**Male:**

BF% = 86.010 × log₁₀(waist − neck) − 70.041 × log₁₀(height) + 36.76

**Female:**

BF% = 163.205 × log₁₀(waist + hip − neck) − 97.684 × log₁₀(height) − 78.387

All measurements in centimeters.

| Category | Male | Female |
|--------------|--------|--------|
| Essential | 2-5% | 10-13% |
| Athletic | 6-13% | 14-20% |
| Fitness | 14-17% | 21-24% |
| Average | 18-24% | 25-31% |
| Obese | 25%+ | 32%+ |

Accuracy: ±3-5% compared to DEXA. Measure at the navel (waist),
at the Adam's apple (neck), and widest point (hip, females only).

Reference: Hodgdon & Beckett, Naval Health Research Center (1984).

## APIs

- wger: https://wger.de/api/v2/ — AGPL-3.0, exercise data is CC-BY-SA 3.0
- USDA FoodData Central: https://api.nal.usda.gov/fdc/v1/ — public domain (CC0 1.0)
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