Market AuditSAFE

AI Marketing Suite for Claude Code. 15 marketing skills with parallel subagents — audit any website, generate copy, email sequences, ad campaigns, content calendars, competitive intelligence, and client-ready PDF reports.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

# 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:

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (1)

Gates applied: no_behavioural_pass.