--- layout: tap site_name: weather tap_name: forecast description: "Weather forecast from wttr.in (defaults to auto-detected location)" intent: read columns: - day - location - temp_c - temp_f - feels_like_c - condition - humidity - wind_kph - wind_dir - precip_mm - uv_index args: - name: location type: string description: "City name or location (e.g. 'London', 'New+York')" args_json: | {"location":{"type":"string","default":"","description":"City name or location (e.g. 'London', 'New+York')"}} health_json: | {"min_rows":1,"non_empty":[]} example_args: "" source_url: https://github.com/LeonTing1010/tap-skills/blob/main/community/weather/forecast.plan.json license: MIT ---

What it does

Weather forecast from wttr.in (defaults to auto-detected location)

Install Taprun once

Taprun ships as a single MCP server exposing a catalog of compiled taps. One-time setup on macOS / Linux:

brew install LeonTing1010/tap/taprun
tap mcp connect

Or drop this into your claude_desktop_config.json (works identically in Claude Code, Cursor, Cline, Windsurf — any MCP host):

{
  "mcpServers": {
    "tap": {
      "command": "tap",
      "args": ["mcp", "start"]
    }
  }
}

Call weather/forecast

Terminal, once installed:

tap run weather/forecast

From the MCP host — exact same compiled plan, deterministic replay, zero LLM tokens:

tap.run({ site: "weather", name: "forecast" })

Why compile it once

This plan was forged once — the AI read weather, picked stable structural addresses (JSON-LD, ARIA, RSS, or declared API endpoints, in that priority order), and saved them to a .plan.json. Every replay since then has used zero LLM tokens. When weather ships a site change that breaks the extraction, tap verify surfaces it before your data goes stale — not after your pipeline silently writes garbage for a week.