nano-banana-pro-prompts-recommend-skill
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
ArriveLah Singapore Bus Arrivals
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install abhayjb/buslahInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Natural language bus lookup for Singapore.
bus from <source location> to <destination location>
Examples:
Use web_fetch to resolve the source location to coordinates via OneMap API:
https://www.onemap.gov.sg/api/common/elastic/search?searchVal=<source>&returnGeom=Y&getAddrDetails=Y&pageNum=1
Extract LATITUDE and LONGITUDE from the first result.
Fetch the full Singapore bus stop list and find stops closest to source coordinates:
https://busrouter.sg/data/2/bus-stops.json
This returns a JSON object where each key is a bus stop code, with fields: description, road, lat, lng.
Compute distance using: sqrt((lat2-lat1)^2 + (lng2-lng1)^2) (approximate is fine for short distances).
Pick the 3 nearest stops within ~300m.
Same as Step 1 for the destination. Extract its coordinates.
For each of the 3 nearest source stops, fetch arrivals:
https://arrivelah2.busrouter.sg/?id=<stop_code>
Then for each bus service at those stops, check if it passes near the destination using:
https://busrouter.sg/data/2/routes.json
This maps bus service numbers to arrays of stop codes in order. Cross-reference with bus-stops.json to get coordinates of each stop on the route, and check if any stop is within ~400m of the destination coordinates.
Keep only buses that:
For each matching bus at the source stop, get from the arrivelah2 response:
next.duration_ms → minutes unti...These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# ArriveLah - Singapore Bus Arrivals Natural language bus lookup for Singapore. ## Trigger Format ``` bus from <source location> to <destination location> ``` Examples: - "bus from Silat Road Sikh Temple to Queens condo" - "bus from Tanjong Pagar MRT to VivoCity" - "bus from my office to home" ## Step-by-Step Workflow ### Step 1: Geocode source location Use web_fetch to resolve the source location to coordinates via OneMap API: ``` https://www.onemap.gov.sg/api/common/elastic/search?searchVal=<source>&returnGeom=Y&getAddrDetails=Y&pageNum=1 ``` Extract `LATITUDE` and `LONGITUDE` from the first result. ### Step 2: Find nearest bus stops to source Fetch the full Singapore bus stop list and find stops closest to source coordinates: ``` https://busrouter.sg/data/2/bus-stops.json ``` This returns a JSON object where each key is a bus stop code, with fields: `description`, `road`, `lat`, `lng`. Compute distance using: `sqrt((lat2-lat1)^2 + (lng2-lng1)^2)` (approximate is fine for short distances). Pick the **3 nearest** stops within ~300m. ### Step 3: Geocode destination location Same as Step 1 for the destination. Extract its coordinates. ### Step 4: Find which buses go from source stops toward destination For each of the 3 nearest source stops, fetch arrivals: ``` https://arrivelah2.busrouter.sg/?id=<stop_code> ``` Then for each bus service at those stops, check if it passes near the destination using: ``` https://busrouter.sg/data/2/routes.json ``` This maps bus service numbers to arrays of stop codes in order. Cross-reference with bus-stops.json to get coordinates of each stop on the route, and check if any stop is within ~400m of the destination coordinates. Keep only buses that: 1. Have the source stop **before** the destination stop in their route (correct direction) 2. Pass within ~400m of the destination ### Step 5: Fetch live arrival times For each matching bus at the source stop, get from the arrivelah2 response: - `next.duration_ms` → minutes unti...
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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