A comprehensive digital guide to San Francisco designed for AI agents to assist visitors, residents, and tech professionals.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install san-francisco
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 san-francisco 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 San Francisco City Guide?
The San Francisco skill for Openclaw Skills provides a multi-dimensional intelligence layer for the city, enabling AI agents to act as expert local concierges. It covers everything from granular neighborhood comparisons to the nuances of the local tech industry. Whether a user is a startup founder scouting for talent or a visitor looking for the best dim sum in the Richmond district, this repository of Openclaw Skills offers structured, actionable data to drive informed decisions.
This skill is specifically optimized for agents to parse complex urban environments, synthesizing information on cost of living, transport logistics, and safety. By integrating this into your Openclaw Skills library, you provide users with a robust framework for navigating one of the world's most complex and expensive cities with confidence.
San Francisco City Guide Use Cases
- Planning 1, 3, or 7-day visitor itineraries including Alcatraz booking reminders and hidden gem discoveries.
- Evaluating neighborhoods for relocation based on budget, commute preferences, and specific lifestyle profiles.
- Researching the tech industry landscape for software engineers and entrepreneurs looking for startup hubs.
- Navigating local safety concerns and identifying specific blocks to avoid in areas like the Tenderloin.
- Optimizing transportation choices by comparing BART, Muni, and rideshare efficiency.
How San Francisco City Guide Works
- The AI agent first identifies the user's specific context, such as being a tourist, prospective resident, or tech professional.
- The system accesses the Openclaw Skills directory to load relevant thematic markdown files based on the query category.
- It applies core rules regarding local safety and seasonal weather patterns to ensure the advice is practical and grounded in reality.
- The agent cross-references current market data for rent prices and salary ranges to provide accurate financial guidance.
- A synthesized response is generated, prioritizing authentic local experiences while steering users away from common tourist traps.
San Francisco City Guide Setup
To integrate this skill into your workflow, ensure your agent has access to the skill directory within your Openclaw Skills collection.
# Navigate to your skills directory
cd path/to/openclaw/skills
# Pull the latest San Francisco skill updates
git pull origin main
# Ensure the agent can access the neighborhood and career sub-files
ls san-francisco/*.md
San Francisco City Guide Data Schema & Taxonomy
The skill organizes data into a hierarchy of specialized markdown files to ensure the AI agent retrieves only the most relevant context. This structure is a hallmark of high-quality Openclaw Skills.
| Data Category | Primary Files | Key Metadata |
|---|---|---|
| Tourism | visitor-attractions.md, visitor-lodging.md | Must-see lists, booking timelines |
| Neighborhoods | neighborhoods-index.md, neighborhoods-central.md | Comparison tables, vibe profiles |
| Living Costs | cost.md, resident.md | Rent ranges, utility estimates |
| Industry | tech.md, startup.md | Salary bands, networking hubs |
| Practical | safety.md, transport.md, climate.md | Danger zones, transit maps, weather myths |
San Francisco City Guide Advanced Features
- Contextual neighborhood matching that filters areas based on user personas like 'Young Professional' or 'Budget-conscious'.
- Strategic safety mapping providing block-by-block guidance for high-traffic zones like SoMa.
- Technical career pathing with specific total compensation (TC) data for the SF tech ecosystem.
- Micro-climate correction logic to advise users on the 'Karl the Fog' phenomenon during summer months.
- Comprehensive 'Tourist Trap' filter to optimize visitor time toward high-value activities.
SKILL.md
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