# old-workspace

> This skill enables your AI agent to become a personal fashion stylist for their human. The agent uses their deep understanding of the human's preferences, lifestyle, and personality to curate outfits and provide styling advice.

- Author: Keith (MAIOS)
- Repository: kjnicholson/dress-your-human
- Version: 20260208002748
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-08
- Source: https://github.com/kjnicholson/dress-your-human
- Web: https://mule.run/skillshub/@@kjnicholson/dress-your-human~old-workspace:20260208002748

---

---
name: dress-your-human
description: Styling intelligence for agents. Get outfit recommendations, trend data, and purchase links for the humans you style.
homepage: https://dressyourhuman.com
version: 1.0.0
---

# Dress Your Human — Agent Skill

**For agents styling humans.**

This skill provides styling intelligence through a simple API. Any agent (like you) can call this service to get outfit recommendations for your human.

## Quick Start

### 1. Call the Styling API

```python
from dress_your_human.api.engine import style_human

result = style_human(
    human_profile={
        "style_vibe": "business_casual",  # minimalist, creative, formal, casual
        "budget_tier": "mid",             # budget, mid, premium
        "location": "nyc"                 # city for trend data
    },
    context={
        "weather": {"temp": 45, "conditions": "cloudy"},
        "occasion": "office",             # office, evening, weekend, meeting
        "time_of_day": "morning"
    }
)
```

### 2. Present to Your Human

```
Good morning! It's 45°F and cloudy in NYC. 

For your day at the office, I'm thinking:
• Navy crew sweater
• White button-down shirt  
• Dark jeans
• Chelsea boots
• Wool overcoat (optional)

This look is professional but approachable for your work context. Right now in NYC: Minimalist layering, oversized silhouettes, neutral palettes are trending.

[View purchase links] [Save to wardrobe] [Try something else]
```

### 3. Capture Feedback

```python
# After human responds
feedback = {
    "outfit_id": result["outfit"]["id"],
    "liked": True,  # or False
    "notes": "Sweater was too warm",  # optional
    "worn": True    # track what they actually wore
}

# Store in your human's profile for learning
```

## API Reference

### `style_human(human_profile, wardrobe, context)`

**Returns outfit recommendation with purchase links.**

**Parameters:**
- `human_profile` (dict):
  - `style_vibe`: "minimalist", "business_casual", "creative", "formal", "casual"
  - `budget_tier`: "budget", "mid", "premium"
  - `location`: City name for trend data
  - `body_type`: Optional (slim, athletic, etc.)
  
- `context` (dict):
  - `weather`: {"temp": int, "conditions": str}
  - `occasion`: "office", "meeting", "evening", "weekend", "dinner", etc.
  - `calendar_events`: List of events today
  - `time_of_day`: "morning", "afternoon", "evening"

**Returns:**
```json
{
  "outfit": {
    "id": "biz-casual-001",
    "name": "Modern Professional",
    "pieces": [...],
    "trend_colors": ["charcoal", "navy", "cream"],
    "trend_notes": "Minimalist layering..."
  },
  "purchase_links": [...],
  "reasoning": "It's cold out, so I prioritized warmth...",
  "styling_tips": ["Roll sleeves for relaxed look", ...],
  "metadata": {
    "confidence": 0.85,
    "timestamp": "2026-02-04T18:20:00Z"
  }
}
```

### `get_trends(location)`

**Get current trend report for a location.**

```python
from dress_your_human.api.engine import get_trends

trends = get_trends("nyc")
# Returns trend colors, key pieces, style notes for current season
```

## Best Practices

### 1. Capture Context

The more context you provide, the better the recommendation:
- ✅ Weather (temp + conditions)
- ✅ Calendar events ("investor meeting at 10am")
- ✅ Occasion context ("first date", "client presentation")
- ✅ Time of day

### 2. Learn From Feedback

Track what your human likes/dislikes:
```python
# Store in human's profile
human_preferences = {
    "liked_outfits": ["biz-casual-001", "creative-003"],
    "disliked_outfits": ["formal-002"],
    "preferred_colors": ["navy", "gray"],
    "avoid": ["bright_colors", "logos"]
}
```

### 3. Proactive Styling

Set up morning briefings:
```python
# In your agent's morning routine
if time == "7:00 AM":
    weather = get_weather(human_location)
    calendar = get_calendar(human_id)
    
    outfit = style_human(
        human_profile=human_profile,
        context={
            "weather": weather,
            "calendar_events": calendar,
            "time_of_day": "morning"
        }
    )
    
    send_message(human_id, format_outfit_briefing(outfit))
```

### 4. Build Wardrobe History

Track what your human owns and wears:
```python
wardrobe = {
    "owned_items": [...],      # What they have
    "frequently_worn": [...],  # What they wear most
    "outfit_history": [...]    # Past recommendations + feedback
}
```

## Monetization (Future)

**Free Tier:**
- 5 outfit recommendations/day
- Basic trend data
- Standard purchase links

**Pro Tier ($9/agent/month):**
- Unlimited outfits
- Real-time trend alerts
- Affiliate commission sharing
- Priority API access

**Enterprise:**
- White-label styling
- Custom outfit databases
- Dedicated support

## Integration Example

```python
class FashionAgent:
    def __init__(self, human_id):
        self.human_id = human_id
        self.profile = load_profile(human_id)
    
    async def morning_briefing(self):
        """Send daily outfit recommendation"""
        # Get context
        weather = await get_weather(self.profile["location"])
        calendar = await get_calendar(self.human_id)
        
        # Get outfit
        from dress_your_human.api.engine import style_human
        
        result = style_human(
            human_profile=self.profile,
            context={
                "weather": weather,
                "occasion": self._infer_occasion(calendar),
                "time_of_day": "morning"
            }
        )
        
        # Format and send
        message = self._format_briefing(result, weather, calendar)
        await self.send_message(message)
    
    def _format_briefing(self, result, weather, calendar):
        outfit = result["outfit"]
        pieces = "\n".join([f"• {p['item'].replace('_', ' ').title()}" for p in outfit["pieces"][:4]])
        
        return f"""Good morning! It's {weather['temp']}°F and {weather['conditions']}.

For your day{self._format_events(calendar)}, I'm thinking:
{pieces}

{result['reasoning']}

Styling tip: {result['styling_tips'][0]}

[View details] [Save outfit] [Try different]"""
```

## Data Sources

- **Outfit templates:** Curated by fashion experts
- **Trend data:** Aggregated from runway, retail, and social signals
- **Brand database:** Affiliate partnerships (in development)

## Version History

**v1.0.0** (2026-02-04)
- Initial release
- 5 outfit templates
- Trend data for NYC, SF, Chicago
- Basic weather matching

## Support

Questions? Feature requests?
- API docs: https://docs.dressyourhuman.com
- Discord: https://discord.gg/dressyourhuman
- Email: agents@dressyourhuman.com

---

**Dress Your Human — Style intelligence for every agent.**