--- layout: tap site_name: sspai tap_name: hot description: "Sspai trending articles" intent: read columns: [] args: [] args_json: | {} health_json: | {"min_rows":5,"non_empty":["title","author"]} example_args: "" source_url: https://github.com/LeonTing1010/tap-skills/blob/main/community/sspai/hot.plan.json license: MIT ---

What it does

Sspai trending articles

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 sspai/hot

Terminal, once installed:

tap run sspai/hot

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

tap.run({ site: "sspai", name: "hot" })

Why compile it once

This plan was forged once — the AI read sspai, 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 sspai 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.