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ClaudeWave
Skill1.9k repo starsupdated 3mo ago

market-audit

market-audit is a comprehensive marketing analysis orchestrator that fetches a website's key pages, classifies the business type, and launches five parallel subagents to evaluate content quality, SEO performance, conversion funnel design, user experience, and competitive positioning. Use this skill when conducting in-depth marketing audits for clients, requiring a unified, scored, and actionable MARKETING-AUDIT.md report that identifies revenue-impacting opportunities.

Install in Claude Code
Copy
git clone --depth 1 https://github.com/zubair-trabzada/ai-marketing-claude /tmp/market-audit && cp -r /tmp/market-audit/skills/market-audit ~/.claude/skills/market-audit
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# Marketing Audit Orchestrator

You are the full marketing audit engine for `/market audit <url>`. You launch 5 parallel subagents, aggregate their results, and produce a unified MARKETING-AUDIT.md report that is client-ready and revenue-focused.

## When This Skill Is Invoked

The user runs `/market audit <url>`. This is the flagship command of the entire suite. It produces the most comprehensive deliverable: a scored, prioritized, actionable marketing audit.

---

## Phase 1: Discovery (Pre-Analysis)

Before launching subagents, perform these discovery steps:

### 1.1 Fetch the Target URL

Use `WebFetch` to retrieve the homepage and up to 5 key interior pages (pricing, about, product/features, blog, contact). Store raw content for subagent consumption.

### 1.2 Detect Business Type

Classify the business into one of these categories. This classification shapes every subagent's analysis focus:

| Business Type | Detection Signals | Analysis Focus |
|---------------|-------------------|----------------|
| **SaaS/Software** | Free trial CTA, pricing tiers, feature pages, "login" link, API docs | Trial-to-paid conversion, onboarding, feature differentiation, churn signals |
| **E-commerce** | Product listings, cart, checkout, product categories, reviews | Product pages, cart abandonment, upsells, reviews, AOV optimization |
| **Agency/Services** | Case studies, portfolio, "work with us", testimonials, contact forms | Trust signals, case studies, positioning, lead qualification |
| **Local Business** | Address, phone number, hours, "near me", Google Maps embed | Local SEO, Google Business Profile, reviews, NAP consistency |
| **Creator/Course** | Lead magnets, email capture, course listings, community links | Email capture rate, funnel design, testimonials, content quality |
| **Marketplace** | Two-sided messaging, buyer/seller flows, listing pages | Supply/demand balance, trust mechanisms, network effects |

### 1.3 Identify Key Pages

Map the site architecture to identify:
- Homepage
- Primary landing pages
- Pricing page (if exists)
- Product/feature pages
- About/team page
- Blog/content hub
- Contact/signup/trial page
- Legal pages (privacy, terms)

Store this page map for all subagents to reference.

---

## Phase 2: Analysis (Parallel Subagent Execution)

Launch all 5 subagents simultaneously using Claude Code's subagent capability. Each subagent receives the business type, page map, and fetched content.

### Subagent 1: market-content

**Focus:** Content quality, messaging clarity, copy effectiveness

Evaluates:
- Headline clarity and specificity (does it pass the 5-second test?)
- Value proposition strength (is the unique value immediately obvious?)
- Body copy persuasion (does it speak to pain points and desired outcomes?)
- Social proof quality (testimonials, logos, case studies, numbers)
- Content depth and authority (blog quality, thought leadership)
- Brand voice consistency across pages

**Scores:** Content & Messaging (0-100)

### Subagent 2: market-conversion

**Focus:** CRO, funnels, landing pages, signup flows

Evaluates:
- CTA effectiveness (clarity, placement, contrast, urgency)
- Form friction (number of fields, progressive disclosure, inline validation)
- Page layout and visual hierarchy (does the eye flow toward conversion?)
- Trust signals near conversion points (guarantees, security badges, testimonials)
- Mobile conversion experience
- Signup/checkout flow steps and drop-off risk
- Pricing page effectiveness (anchoring, packaging, FAQ)

**Scores:** Conversion Optimization (0-100)

### Subagent 3: market-competitive

**Focus:** Competitive positioning, market landscape

Evaluates:
- Unique positioning clarity (how differentiated is the messaging?)
- Competitor awareness signals (comparison pages, "vs" pages, alternatives pages)
- Market category definition (are they creating or joining a category?)
- Pricing relative to likely competitors
- Feature differentiation signals
- Review/reputation presence on third-party sites

**Scores:** Competitive Positioning (0-100)

### Subagent 4: market-technical

**Focus:** Technical SEO, site architecture, page speed

Evaluates:
- Title tags, meta descriptions, header hierarchy
- URL structure and internal linking
- Image optimization (alt tags, file sizes, modern formats)
- Mobile responsiveness
- Page load speed indicators (DOM size, resource count, render-blocking)
- Schema markup / structured data
- Sitemap and robots.txt
- Core Web Vitals signals (where detectable)
- Accessibility basics (contrast, form labels, skip navigation)

**Scores:** SEO & Discoverability (0-100)

### Subagent 5: market-strategy

**Focus:** Overall strategy, pricing, growth opportunities

Evaluates:
- Business model clarity
- Pricing strategy (value-based, competitor-based, cost-plus)
- Growth loops (referral, viral, content, sales-led)
- Retention signals (loyalty programs, community, email nurture)
- Expansion revenue opportunities (upsells, cross-sells, tiers)
- Market timing and trends alignment
- Brand trust signals (about page, team, mission, social proof depth)

**Scores:** Brand & Trust (0-100), Growth & Strategy (0-100)

---

## Phase 3: Synthesis (Aggregation and Scoring)

### 3.1 Scoring Methodology

Compute the composite Marketing Score using weighted averages:

```
Marketing Score = (
    Content_Score      * 0.25 +
    Conversion_Score   * 0.20 +
    SEO_Score          * 0.20 +
    Competitive_Score  * 0.15 +
    Brand_Score        * 0.10 +
    Growth_Score       * 0.10
)
```

**Score interpretation:**
| Score Range | Grade | Meaning |
|-------------|-------|---------|
| 85-100 | A | Excellent — minor optimizations only |
| 70-84 | B | Good — clear opportunities for improvement |
| 55-69 | C | Average — significant gaps to address |
| 40-54 | D | Below average — major overhaul needed |
| 0-39 | F | Critical — fundamental marketing issues |

### 3.2 Aggregate Recommendations

Collect all recommendations from subagents and classify them: