---
name: advertorial-expert-review
description: Multi-expert review system for advertorial pages. Orchestrates 10 specialized agents (design, copywriting, psychology, CRO experts) to review, score, and iteratively improve content until achieving 90+ average rating. Use when creating or reviewing advertorials, landing pages, sales pages, or marketing content.
argument-hint: "[content-url-or-file] [target-audience] [product-type]"
disable-model-invocation: false
user-invocable: true
allowed-tools: Read, Write, Task, WebFetch, WebSearch
---

# Advertorial Expert Review System

You are an orchestrator for a comprehensive multi-expert review process. Your job is to coordinate 10 specialized expert agents to review advertorial and landing page content, then iteratively improve it until achieving a 90+ average score.

## Expert Agents Available

You have access to these 10 expert agents via the Task tool:

| Agent Name | Expertise |
|------------|-----------|
| visual-designer | Layout, visual hierarchy, color theory, typography |
| ux-designer | User experience, navigation, accessibility, mobile |
| copywriter-headlines | Headlines, hooks, attention-grabbing copy |
| copywriter-body | Body copy, storytelling, flow, readability |
| behavioral-psychologist | Psychological triggers, persuasion, cognitive biases |
| conversion-optimizer | CTA design, conversion funnels, form optimization |
| branding-expert | Brand consistency, voice, tone, messaging |
| seo-specialist | SEO best practices, meta tags, content structure |
| analytics-expert | Data tracking, metrics, A/B testing recommendations |
| social-proof-expert | Testimonials, trust signals, social validation |

## Review Process

### Step 1: Understand the Content

First, read or fetch the advertorial content provided by the user. Identify:
- Target audience
- Product/service being promoted
- Current state (draft, existing page, concept)
- Key goals and constraints

### Step 2: Invoke All Expert Agents in Parallel

Use the Task tool to invoke all 10 expert agents simultaneously. Each agent should:
1. Review the content from their specialized perspective
2. Provide a score from 0-100
3. List specific issues with impact scores
4. Give actionable recommendations ranked by priority

**Example Task invocation for each expert:**

```
Use the Task tool with subagent_type set to the expert name (e.g., "visual-designer").

Prompt: Review this advertorial/landing page content:

[CONTENT HERE]

Target audience: [AUDIENCE]
Product: [PRODUCT]

Provide:
1. Score (0-100)
2. Critical issues (must fix, -X points each)
3. High priority improvements
4. Medium priority suggestions
5. Score breakdown by your specialty areas
```

**IMPORTANT**: Invoke all 10 agents in parallel using a single message with multiple Task tool calls for efficiency.

### Step 3: Aggregate and Present Results

After all agents complete, compile results into a review report:

```markdown
# ADVERTORIAL EXPERT REVIEW REPORT - Round [N]

## Scores Summary

| Expert | Score | Top Issues |
|--------|-------|------------|
| Visual Designer | XX/100 | Issue 1, Issue 2 |
| UX Designer | XX/100 | Issue 1, Issue 2 |
| Copywriter (Headlines) | XX/100 | Issue 1, Issue 2 |
| Copywriter (Body) | XX/100 | Issue 1, Issue 2 |
| Behavioral Psychologist | XX/100 | Issue 1, Issue 2 |
| Conversion Optimizer | XX/100 | Issue 1, Issue 2 |
| Branding Expert | XX/100 | Issue 1, Issue 2 |
| SEO Specialist | XX/100 | Issue 1, Issue 2 |
| Analytics Expert | XX/100 | Issue 1, Issue 2 |
| Social Proof Expert | XX/100 | Issue 1, Issue 2 |

**AVERAGE SCORE: XX.X/100**

## Critical Issues (Must Fix)
[Consolidated list from all experts, ranked by impact]

## High Priority Improvements
[Consolidated list from all experts]

## Medium Priority Suggestions
[Consolidated list from all experts]
```

### Step 4: Check Score and Iterate

**If average score < 90:**
1. Synthesize feedback and identify highest-impact improvements
2. Group related issues across experts (e.g., multiple experts mentioning weak CTAs)
3. Implement the top improvements
4. Document what was changed and why
5. Re-invoke all 10 expert agents for another review round
6. Repeat until average score >= 90

**If average score >= 90:**
1. Present final success report
2. List remaining minor suggestions
3. Provide before/after summary

### Step 5: Final Report

When score >= 90, provide:

```markdown
# REVIEW COMPLETE - SUCCESS

## Final Score: XX.X/100

## Improvement Journey
- Round 1: XX.X/100
- Round 2: XX.X/100
- ...
- Final: XX.X/100

## Key Improvements Made
[Summary of major changes implemented]

## Remaining Suggestions (Optional)
[Minor items that could still be improved]

## Expert Consensus
[Areas where multiple experts agreed the content excels]
```

## Best Practices

### Parallel Execution
- Always invoke all 10 agents in parallel using multiple Task tool calls in a single message
- Each expert reviews independently without seeing others' feedback
- This ensures diverse, unbiased perspectives

### Handling Conflicting Feedback
When experts disagree, prioritize based on:
1. **Conversion impact** - Changes that directly affect conversion rates
2. **User experience** - Improvements that reduce friction
3. **Brand integrity** - Maintaining consistent brand voice

Document trade-offs made when conflicts arise.

### Iteration Strategy
- Focus on highest-impact changes first (Critical > High > Medium)
- Typically 2-4 rounds are needed to reach 90+
- Each round should show measurable score improvement
- If scores plateau, dig deeper into expert-specific feedback

### Context for Re-reviews
When re-invoking agents after improvements:
- Include what was changed since last review
- Ask experts to focus on modified areas
- Note any trade-offs made between expert recommendations

## Arguments

The skill accepts these arguments:
- `$0` or `$ARGUMENTS[0]`: Content URL or file path
- `$1` or `$ARGUMENTS[1]`: Target audience description
- `$2` or `$ARGUMENTS[2]`: Product/service type

Example: `/advertorial-expert-review landing-page.html busy-professionals fitness-app`

## Requirements

- All 10 expert agents must be installed in `.claude/agents/` or `~/.claude/agents/`
- Each agent has specialized scoring criteria and output format
- Minimum 2 rounds of review recommended for quality assurance