StylePilot is an AI-powered wardrobe assistant that digitizes your clothing collection to provide context-aware outfit suggestions and travel packing lists.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install stylepilot
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install stylepilot using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is StylePilot?
StylePilot functions as a professional personal styling consultant integrated directly into your workflow. By utilizing Openclaw Skills, it allows users to build a comprehensive digital inventory of their physical wardrobe. The agent identifies clothing attributes like category, color, season, and style from photos or descriptions, storing them in a structured local database.
The primary value of StylePilot lies in its ability to solve the daily what to wear dilemma. It synthesizes environmental factors such as weather, temperature, and specific social occasions to recommend the most appropriate combinations from your actual collection. It ensures data privacy by keeping all images and database records stored locally on your machine.
StylePilot Use Cases
- Generating daily outfit recommendations based on real-time weather and work schedules.
- Creating optimized packing lists for multi-day trips and vacations using the travel scene logic.
- Cataloging a physical wardrobe into a searchable digital database with metadata tagging.
- Receiving styling suggestions for specific high-stakes events like interviews, dates, or formal ceremonies.
How StylePilot Works
- The user uploads a photo or describes a clothing item to the agent.
- StylePilot extracts attributes such as color, season, and occasion, then saves the metadata and local image path to an SQLite database.
- When an outfit is requested, the agent verifies the intended scene (e.g., commute, travel) and weather conditions.
- The system queries the local database to find items that match the seasonal and situational constraints.
- An optimized outfit scheme is generated, providing a visual and descriptive guide for the user.
StylePilot Setup
To get started with this wardrobe assistant, initialize your local database and start adding items using the following commands:
# Initialize the SQLite database
./run.sh init
# Add a clothing item to your digital wardrobe
./run.sh add --name "Navy Blazer" --category "Outerwear" --color "Navy" --season "All" --style "Business" --image "/path/to/blazer.jpg"
# View your current wardrobe inventory
./run.sh list --limit 50 --json
StylePilot Data Schema & Taxonomy
StylePilot organizes your fashion data using a structured SQLite database and local file storage. This ensures your Openclaw Skills data remains private.
| Table | Description |
|---|---|
| clothing_items | Stores id, name, category, color, season, style, occasion, and local image_path. |
| outfit_records | Maintains a history of generated recommendations for future reference. |
Images are stored locally in the data/images/ directory to avoid reliance on external cloud hosting.
StylePilot Advanced Features
- Smart travel packing logic that calculates necessary items based on the number of days and destination weather.
- Explicit feedback weighting that allows the AI to learn your style preferences through like or dislike commands.
- JSON-compatible output for developers to build custom frontends or integrations on top of the wardrobe engine.
- Contextual clarification prompts that ensure the agent never recommends seasonal clothing that is inappropriate for the current temperature.
SKILL.md
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