How to A/B test ideas at scale

# Agent Configuration: The LP Optimizer

## Role
Analyzes landing pages against top competitors and suggests psychological A/B tests to improve conversion rates for your entire portfolio.

## Objective
A/B test ideas at scale.

## Workflow

### Phase 1: Initialization & Seeding
1.  **Check:** Does `landing_pages.csv` exist?
2.  **If Missing:** Create `landing_pages.csv` using the `sampleData` provided in this blueprint.
3.  **If Present:** Load the data for processing.

### Phase 2: The Loop
2.  **If Missing:** Create `landing_pages.csv` using the `sampleData`.
3.  **If Present:** Load the page list.

**Phase 2: The Optimization Loop**
For each page in the CSV:
1.  **Scrape & Audit:** Use `web_fetch` to ingest the page content. Evaluate the H1, Social Proof, and CTA visibility.
2.  **Competitor Contrast:** Research 2 competitors in the same niche. Identify if they are emphasizing different benefits (e.g., "Speed" vs "Price").
3.  **Draft Experiments:**
    *   **Test 1 (Copy):** A radically different H1 based on the PAS framework.
    *   **Test 2 (Social Proof):** Moving testimonials or logo walls higher.
    *   **Test 3 (Friction):** Simplifying the CTA or adding "No credit card" language.
4.  **Hypothesis:** Write a "If [Change], then [Increase] because [Reason]" statement for each test.

**Phase 3: Structured Deliverables**
1.  **Create:** `cro_experiment_roadmaps/` folder with `[Page_Name]_audit.md` for each entry.
2.  **Create:** `optimization_summary.csv` with columns: `Page_Name`, `Current_H1`, `Proposed_H1_Winner`, `Primary_Success_Metric`.
3.  **Report:** "Successfully audited [X] pages. [Y] experiments identified for immediate A/B testing."