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- Extract archive
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- SKILL.md
Tencent SkillHub ยท Content Creation
travel-destination-brochure
Build travel destination scenarios and brochures from a city name. Fetches street-level and landmark imagery from OpenStreetCam and Wikimedia Commons, then uses VLM Run (vlmrun) to generate a travel video and a travel plan. Use when the user wants a travel brochure, destination guide, travel video, or travel planning for a city.
skill openclawclawhub Free
Build travel destination scenarios and brochures from a city name. Fetches street-level and landmark imagery from OpenStreetCam and Wikimedia Commons, then uses VLM Run (vlmrun) to generate a travel video and a travel plan. Use when the user wants a travel brochure, destination guide, travel video, or travel planning for a city.
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- Tencent SkillHub
- What's included
- .env_template.txt, README.md, SKILL.md, scripts/fetch_commons.py, scripts/fetch_openstreetcam.py, scripts/geocode_city.py
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- 1.0.0
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- MehediAhamed
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Documentation
Travel Destination Brochure & Video
Create travel brochures, videos, and 1-day plans for a destination city by combining OpenStreetCam street-level photos, Wikimedia Commons imagery, and VLM Run for video and copy.
Prerequisites
Before starting, ensure you have: Python 3.10 or higher installed Internet connection (for downloading images and API access) VLMRUN_API_KEY (optional, but required for video and travel plan generation) No API keys required for: OpenStreetCam (public read access) Wikimedia Commons (public access) Nominatim geocoding (public access)
Step 1: Verify Python Installation
Check if Python 3.10+ is installed: Windows (PowerShell): python --version # Should show Python 3.10.x or higher macOS/Linux: python3 --version # Should show Python 3.10.x or higher If Python is not installed or is an older version: Windows: Download from python.org macOS: brew install python@3.11 (or use python.org installer) Linux: sudo apt install python3.11 (Ubuntu/Debian) or use your distribution's package manager
Step 2: Install uv (Package Manager)
Windows (PowerShell): # Using pip pip install uv # Or using PowerShell installer powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" macOS/Linux: # Using pip pip install uv # Or using curl installer curl -LsSf https://astral.sh/uv/install.sh | sh Verify installation: uv --version
Step 3: Create Virtual Environment
Navigate to the skill directory and create a virtual environment: Windows (PowerShell): cd c:\Users\mehed\.claude\skills\travel-destination-brochure uv venv .venv\Scripts\Activate.ps1 macOS/Linux: cd ~/.claude/skills/travel-destination-brochure uv venv source .venv/bin/activate Note: You should see (.venv) in your terminal prompt when activated.
Step 4: Install Dependencies
Install the required packages: # Install vlmrun CLI (required for video and travel plan generation) uv pip install "vlmrun[cli]" # Install requests (required for API calls) uv pip install requests Verify installation: vlmrun --version python -c "import requests; print(requests.__version__)"
Step 5: Set Up VLMRUN_API_KEY (Optional but Recommended)
To generate travel videos and plans, you need a VLMRUN API key: Windows (PowerShell): # Set for current session Check .env file for api key $env:VLMRUN_API_KEY="your-api-key-here" # Set permanently (User-level) [System.Environment]::SetEnvironmentVariable('VLMRUN_API_KEY', 'your-api-key-here', 'User') macOS/Linux: # Set for current session export VLMRUN_API_KEY="your-api-key-here" # Set permanently (add to ~/.bashrc or ~/.zshrc) echo 'export VLMRUN_API_KEY="your-api-key-here"' >> ~/.bashrc source ~/.bashrc Verify environment variable: Read .env file to find api keys # Windows PowerShell echo $env:VLMRUN_API_KEY # macOS/Linux echo $VLMRUN_API_KEY
Step 6: Verify Installation
Test that everything works: # Test geocoding (should work without API key) uv run scripts/geocode_city.py "Paris, France" # Test vlmrun (if API key is set) vlmrun --help Installation Complete! You're ready to generate travel brochures.
Quick Start (Recommended)
Use the simplified all-in-one script that handles everything automatically: Windows (PowerShell): uv run scripts/simple_travel_brochure.py --city "Doha, Qatar" macOS/Linux: uv run scripts/simple_travel_brochure.py --city "Doha, Qatar" Alternative (if uv is not available): python scripts/simple_travel_brochure.py --city "Doha, Qatar" This script will: Geocode the city name to coordinates Fetch 3 street-level photos from OpenStreetCam Fetch 2 landmark images from Wikimedia Commons (total 5 images) Generate a 30-second travel video using vlmrun (if VLMRUN_API_KEY is set) Generate a one-day travel plan using vlmrun (if VLMRUN_API_KEY is set) Clean up temporary files automatically Options: --output DIR โ Output directory (default: ./travel_brochure) --osc-count N โ Number of OpenStreetCam photos (default: 3) --commons-count N โ Number of Commons images (default: 2) Note: Set the VLMRUN_API_KEY environment variable to enable video and travel plan generation. The script will skip video generation gracefully if the API key is not set. Example: uv run scripts/simple_travel_brochure.py --city "Paris, France" --output ./paris_trip Output: images/ โ Downloaded photos (5 images total) manifest.json โ Metadata about the city, coordinates, and image paths video/ โ Generated travel video (if VLMRUN_API_KEY is set) travel_plan.md โ One-day travel itinerary (if VLMRUN_API_KEY is set)
Advanced: Step-by-Step Workflow
For more control over each step, use the individual scripts below.
Workflow Overview
Collect input โ Get destination city from the user. Geocode โ Resolve city name to coordinates (lat, lng). Fetch imagery & info โ OpenStreetCam (nearby photos) + Wikimedia Commons (search images and metadata). Generate assets โ Use vlmrun to create a short travel video and a travel plan from the collected images and info. All paths below are relative to the directory containing this SKILL.md. Run scripts using: uv run scripts/script_name.py (recommended - handles dependencies automatically via PEP 723) python scripts/script_name.py (if dependencies are already installed)
Step 1: Get Destination City
Ask the user: "Which city do you want the travel brochure and video for?" Use the exact city name (and country/region if ambiguous) for geocoding and Commons search.
Step 2: Geocode City
Resolve city name to latitude/longitude (e.g. for OpenStreetCam and optional Commons geo-search). uv run scripts/geocode_city.py "Paris, France" # Or: python scripts/geocode_city.py "Tokyo" Output: JSON with lat, lng, display_name. Use these in Steps 3โ4.
Step 3: Fetch OpenStreetCam Photos
OpenStreetCam provides street-level imagery. Base URL: https://api.openstreetcam.org/. Nearby sequences: POST /nearby-tracks โ body: lat, lng, distance (km). Nearby photos: POST /1.0/list/nearby-photos/ โ body: lat, lng, radius (meters), optional page, ipp. No access_token required for these read endpoints. Use scripts/fetch_openstreetcam.py to request photos and optionally download thumbnails/full images into a folder. uv run scripts/fetch_openstreetcam.py --lat 48.8566 --lng 2.3522 --radius 2000 --output ./assets/osc --max-photos 20 Produces: image files under --output and a small manifest (e.g. osc_manifest.json) with captions/locations if available.
Step 4: Fetch Wikimedia Commons Images & Info
Commons provides landmark and cultural images. API: https://commons.wikimedia.org/w/api.php. Search: action=query, list=search, srsearch=<city or landmark>, srnamespace=6 (File namespace). Image URLs and metadata: action=query, prop=imageinfo, iiprop=url|extmetadata, titles=File:.... Use scripts/fetch_commons.py to search by destination name, resolve file URLs, and optionally download to a folder. uv run scripts/fetch_commons.py --query "Paris landmarks" --output ./assets/commons --max-images 15 Produces: image files and a manifest (e.g. commons_manifest.json) with captions/descriptions from Commons.
Step 5: Aggregate Manifest for vlmrun
Combine OSC and Commons manifests (and optionally add short text lines per image) into a single manifest or list that you can pass to vlmrun (e.g. paths + one short caption per image). The pipeline script can do this. uv run scripts/run_travel_pipeline.py --city "Paris, France" --output-dir ./travel_output This script should: geocode โ fetch OSC โ fetch Commons โ write images/ and manifest.json (or manifest.txt) under --output-dir.
Step 6: Generate Video and Travel Plan with vlmrun
Use the vlmrun-cli-skill workflow: ensure vlmrun is installed and VLMRUN_API_KEY is set. Travel video โ Pass the collected images and a single prompt so the model produces a short travel video (e.g. 30 seconds). Prefer -o to save the artifact. Note: If VLMRUN_API_KEY is set as an environment variable, you can omit --api-key: # Using environment variable (recommended) vlmrun chat "Create a 30-second travel video showcasing these images of [CITY]. Add subtle captions with the location names. Keep a calm, inspiring travel-documentary style." -i ./travel_output/images/photo1.jpg -i ./travel_output/images/photo2.jpg -i ./travel_output/images/photo3.jpg ... -o ./travel_output/video # Or using --api-key from .env, flag directly vlmrun --api-key "your-api-key-here" chat "Create a 30-second travel video showcasing these images of [CITY]. Add subtle captions with the location names. Keep a calm, inspiring travel-documentary style." -i ./travel_output/images/photo1.jpg -i ./travel_output/images/photo2.jpg -i ./travel_output/images/photo3.jpg ... -o ./travel_output/video If the number of files is large, reference the manifest and pass a subset (e.g. up to 10โ15 representative images) or use a prompt that says โusing the attached images in order.โ Travel plan (1-day) โ Use the same images plus a text prompt to get a narrative or bullet-point plan. # Using environment variable (recommended) vlmrun chat "Using these images and their locations, write a one-day travel plan for [CITY]: morning, midday, and evening activities with specific places and practical tips. Output as structured markdown (headings and bullet points)." -i ./travel_output/images/photo1.jpg -i ./travel_output/images/photo2.jpg ... -o ./travel_output # Or using --api-key flag directly vlmrun --api-key "your-api-key-here" chat "Using these images and their locations, write a one-day travel plan for [CITY]: morning, midday, and evening activities with specific places and practical tips. Output as structured markdown (headings and bullet points)." -i ./travel_output/images/photo1.jpg -i ./travel_output/images/photo2.jpg ... -o ./travel_output Save the modelโs text response (and any artifact) under --output-dir (e.g. travel_plan.md).
Scripts Reference
ScriptPurposescripts/geocode_city.pyCity name โ lat, lng (Nominatim)scripts/fetch_openstreetcam.pyFetch/download OpenStreetCam photos by lat/lng/radiusscripts/fetch_commons.pySearch and download Wikimedia Commons images by queryscripts/run_travel_pipeline.pyRun geocode + OSC + Commons and write manifest + images
API References
OpenStreetCam: API Reference โ nearby-tracks, list/nearby-photos, auth only for uploads. Wikimedia Commons: Commons API โ action=query, list=search, prop=imageinfo; MediaWiki API help. vlmrun: Use the vlmrun-cli-skill for setup, env vars, and all vlmrun chat options.
Checklist for a Complete Run
User provided destination city (and country if needed). Geocoded city and confirmed lat/lng. Fetched OpenStreetCam photos; saved images and manifest. Fetched Commons images for the destination; saved images and manifest. Built aggregated manifest/images under one output dir. Ran vlmrun with collected images to generate travel video; saved artifact with -o. Ran vlmrun with same (or subset) images to generate travel plan; saved text as markdown.
Installation Issues
Python not found: Windows: Ensure Python is added to PATH during installation, or use py instead of python macOS/Linux: Use python3 instead of python uv command not found: Restart your terminal after installation Windows: Check if uv is in your PATH: $env:PATH macOS/Linux: Ensure ~/.cargo/bin or ~/.local/bin is in your PATH Virtual environment activation fails: Windows PowerShell: If you get an execution policy error, run: Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser Windows CMD: Use .venv\Scripts\activate.bat instead of .ps1 macOS/Linux: Ensure you're using source .venv/bin/activate (not ./.venv/bin/activate) vlmrun not found: Ensure virtual environment is activated Reinstall: uv pip install "vlmrun[cli]" Verify: which vlmrun (macOS/Linux) or where.exe vlmrun (Windows)
Runtime Issues
Geocode fails โ Try adding country/region, or use โCity, Countryโ format. OpenStreetCam returns few/no results: Increase radius parameter (default: 2000m, try 5000m or 10000m) Try city center coordinates instead of outskirts Some regions have sparse coverage; try nearby major cities Commons returns few results โ Broaden query (e.g. โCity tourismโ, โCity sightsโ). vlmrun errors: Confirm VLMRUN_API_KEY is set correctly: echo $VLMRUN_API_KEY (macOS/Linux) or echo $env:VLMRUN_API_KEY (Windows) Check network connection Reduce number of input images if hitting API limits (try 5-10 images instead of 20+) Verify API key is valid and has sufficient credits/quota Script execution errors: Ensure you're in the correct directory (skill root directory) Check that virtual environment is activated Verify all dependencies are installed: uv pip list
Example End-to-End
# 1) Ask user for city, then run pipeline (e.g. "Paris, France") uv run scripts/run_travel_pipeline.py --city "Paris, France" --output-dir ./travel_output # 2) Generate travel video (use image paths from travel_output/images/ or image_paths.txt) vlmrun chat "Create a 30-second travel video from these images of Paris. Add short location captions. Calm documentary style." -i ./travel_output/images/img_0000.jpg -i ./travel_output/images/img_0001.jpg -o ./travel_output/video # 3) Generate 1-day travel plan (same images) vlmrun chat "Using these photos of Paris, write a one-day travel plan (morning, midday, evening) with specific places and tips in markdown." -i ./travel_output/images/img_0000.jpg -i ./travel_output/images/img_0001.jpg -o ./travel_output
Quick Reference: Key URLs
OpenStreetCam API base: https://api.openstreetcam.org/ Commons API: https://commons.wikimedia.org/w/api.php Nominatim geocoding: https://nominatim.openstreetmap.org/search?q=<query>&format=json
Category context
Writing, remixing, publishing, visual generation, and marketing content production.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs1 Files
- SKILL.md
- README.md
- scripts/fetch_commons.py
- scripts/fetch_openstreetcam.py
- scripts/geocode_city.py
- .env_template.txt