--- layout: tap site_name: macos tap_name: scroll-capture description: "Scrolling screenshots of frontmost macOS app (pixel-precise, max 3 screens)" intent: read columns: - screen - file args: - name: screens type: int default: 1 description: "Number of screens to capture (1-3)" - name: header type: int default: 0 description: "Fixed header px to skip when scrolling" - name: app type: string description: "App to activate (empty = frontmost)" args_json: | {"screens":{"type":"int","default":1,"description":"Number of screens to capture (1-3)"},"header":{"type":"int","default":0,"description":"Fixed header px to skip when scrolling"},"app":{"type":"string","default":"","description":"App to activate (empty = frontmost)"}} health_json: | {"min_rows":1,"non_empty":[]} example_args: "" source_url: https://github.com/LeonTing1010/tap-skills/blob/main/community/macos/scroll-capture.plan.json license: MIT ---

## What it does

Scrolling screenshots of frontmost macOS app (pixel-precise, max 3 screens)

## 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 `macos/scroll-capture`

Terminal, once installed:

    tap run macos/scroll-capture

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

    tap.run({ site: "macos", name: "scroll-capture" })

## Why compile it once

This plan was forged once — the AI read `macos`, 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 `macos` 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.