Agent skill · design visual · jeremylongshore
draft-landing
Use when asked to structure a landing page, design page layout for conversion, or plan landing page information architecture. Examples: "landing page structure for SaaS", "conversion-optimized layout
Why this skill is useful
Adds specific workflows and commands for structuring landing pages that optimize for conversion, which the AI wouldn't reliably generate on its own.
What it needs
Requires python3 installed locally. About 1k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
draft-landing — Landing Page Information Architecture Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose. When to use User needs a landing page structure, section order, or conversion-optimized layout. Product type is known or discoverable. Workflow 1. Identify product type from user request or project context 2. Search landing page patterns: 3. Search product reasoning for audience + conversion context: 4. Validate each section against the "so what?" test — every section must earn its place 5. Output section order with CTA placement markers Output format Anti-patterns Never skip the "so what?" test per section — if a section can't answer it, cut it Never add sections without a clear conversion purpose Never place the primary CTA below the fold on the first screen Never structure the page without knowing the primary audience and their job-to-be-done Delivery If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/draft-landing