tonone-ai 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
- Identify product type from user request or project context
- Search landing page patterns:
python3 -m draft_agent.uiux search --domain landing --query "{product_type}" --limit 3 - Search product reasoning for audience + conversion context:
python3 -m draft_agent.uiux search --domain product --query "{product_type}" --limit 3 - Validate each section against the "so what?" test — every section must earn its place
- Output section order with CTA placement markers
Output format
┌─ Landing Page IA — {product_type} ──────────────────────────────────┐
│ # │ Section │ Purpose │ CTA? │
├────┼────────────────────┼────────────────────────────┼───────────────┤
│ 1 │ {section_name} │ {purpose} │ Primary CTA │
│ 2 │ {section_name} │ {purpose} │ — │
│ 3 │ {section_name} │ {purpose} │ Secondary CTA │
│ … │ … │ … │ … │
└────┴────────────────────┴────────────────────────────┴───────────────┘
Conversion strategy: {strategy}
CTA copy guidance: {cta_guidance}
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.