

Takes raw ad campaign data (Google, Meta, LinkedIn — CSV, pasted table, or screenshot), runs performance diagnostics against benchmarks, detects budget waste, identifies winners, checks A/B test statistical significance, maps funnel drop-offs, and produces a structured diagnostic report with a prioritized cut/scale/test action plan.
Campaign: Brand Search — Google
Spend: $4,200 | Conversions: 38 | CPA: $110 | Target: $80
CTR: 6.2% (Good) | Conv Rate: 4.1% (Good) | CPA: $110 (Poor)
Budget Waste: $1,840 (18% of spend) — 3 zero-conversion ad groups
Verdict: Optimize — pause waste, reallocate to winnersPure reasoning skill
User provides the data.
Reads live Ahrefs Site Audit data and generates a full consultant-grade Technical SEO Audit .docx and .md file covering 9 issue categories — from indexability to Core Web Vitals. Every finding is backed by real URL data.
Project ID: 123456
Health Score: 64/100
Errors: 847 Warnings: 1,203
Sections: Indexability · Redirects · Metadata
Content · Images · Core Web VitalsAhrefs MCP (site-audit-*)
python-docx
Web fetch
Produces a 2-page, client-ready .docx report showing exactly where a brand appears (or doesn't) in AI-generated recommendations — and benchmarks it against up to 5 competitors. Ideal for prospect pitches and GEO strategy.
Target: acmecorp.com
AI Mentions: 0 Roundups Audited: 12
Share of Voice: ~0% Leader: Competitor A (45%)
Output: acmecorp_ai_visibility_report.docxAhrefs Brand Radar (optional)
Web search
python-docx
Pulls live Ahrefs data to identify referring domain gaps vs competitors, prioritizes target pages by link urgency, maps specific domains to specific pages, and builds a 90-day link building action plan. Outputs a full .docx report.
Target: acmecorp.com DR: 40
Gap Domains Found: 40+
Top Opportunity: npmjs.com (DR 92)
Effort: Low Timeline: Week 1Ahrefs MCP
docx.js (Node)
End-to-end BOFU keyword research for any SaaS or B2B company. Audits the target website to derive themes, then pulls live Ahrefs data for 18+ keyword variants per theme plus competitor alternatives and vs keywords.
Theme Type Keyword Vol
AI Governance Generic BOFU ai governance tool 1,100
AI Governance Generic BOFU ai governance sw 880
Credo AI Competitor Alt credo ai alternatives 210Ahrefs MCP
Web fetch
openpyxl (Python)
Fetches live hot/rising threads from Reddit (r/SEO, r/artificial, r/ChatGPT etc.), HN Algolia API, and LinkedIn via web search. Scores each item by engagement + recency, picks the top 5, and writes a Buzz Report with LinkedIn post angles.
Topic: SEO/GEO Recency: this week
1. Google AI Overviews eating click share
2. Perplexity citation patterns shifting
3. Reddit threads dominating SGE resultsReddit .json API (free)
HN Algolia API (free)
Web search
Uses Ahrefs live data to fetch up to 5,000 non-branded organic keywords for any domain, deduplicates by taking max page count per keyword, and returns a single cannibalization percentage. Fast, no-noise output.
Domain: example.com
Keywords analyzed: 3,847
Multi-page keywords: 538
Cannibalization Score: 14% of keywords affectedAhrefs MCP (site-explorer-organic-keywords)
Pulls live Ahrefs data to rank the target and its top 5 organic competitors across DR, traffic, keywords, and referring domains. Calculates share of voice, identifies quick-win targets, and outputs a polished .docx prospect report.
Target: acmecorp.com Rank: #6 of 6
Traffic Gap: 2,506 visits/mo
SOV: 0% Competitor Avg DR: 66
Quick Wins: Competitor C, Competitor DAhrefs MCP (batch-analysis, site-explorer-*)
docx.js (Node)
Multi-step workflow: enriches keywords via Ahrefs, analyses the SERP, asks what content type to create, scrapes top-ranking pages, then generates a full outline with H2/H3 structure, long-tail keyword targets, content guidance, SEO strategy, and fully-written FAQ answers.
Primary: ai governance software
Secondary: ai governance tools, ai compliance...
Content Type: Guide / How-To
Target Length: 2,800–3,400 words
FAQs: 8 writtenAhrefs MCP
Web search
Web fetch (page scraping)
python-docx
Takes a single section heading (H2/H3) from a content outline, runs 5–8 targeted web searches, extracts citation-backed data points (stats, findings, quotes with source + year + URL), and returns a structured list of "write about this" bullets with suggested angles and data gaps.
🔬 DEEP DIVE: Why AI Governance Matters in 2026
Data Points Found: 7
▸ 82% of enterprises now have an AI governance policy
→ Source: Gartner (2025) — [URL]
→ Use for: intro hook
▸ Companies with formal AI governance see 23% fewer regulatory incidents
→ Source: McKinsey (2025) — [URL]
→ Use for: body supporting claimWeb search + web_fetch (built-in Claude tools)
No external APIs.
Gathers ad copy, fonts, colors, logo, and sizes — then builds properly constrained HTML creatives, pushes them to Figma via capture API, gets your approval on the first size, then generates all remaining variants including dark mode.
Sizes: 720×900, 1200×628, 1200×1200
Variants: Light + Dark
Files: 6 HTML creatives
→ Pushed to Figma: 6 nodes createdFigma MCP (generate_figma_design)
Local HTTP server (port 8765)
Bash
Takes a list of URLs, batch-scrapes them via Firecrawl, and returns only the errors and suggested fixes — never the full corrected content. Preserves multilingual content, MDX syntax, code blocks, and all formatting.
URL 1: https://example.com/blog/post
Error: "it's" used where "its" needed → Fix: "its"
Error: "there are 3 issue" → Fix: "there are 3 issues"
URL 2: https://example.com/about
No fixes neededFirecrawl MCP (batch scrape)
Pulls page-level data from Google Search Console for two date ranges, computes deltas for clicks, impressions, CTR, and position, filters to pages with drops in both metrics, and writes everything to a Google Sheet with two tabs.
Property: sc-domain:example.com
P1: Sep–Nov P2: Dec–Feb
Pages analyzed: 847
Pages with drops: 203
→ Google Sheet created & sharedRube (Google Search Console + Sheets)
Python (workbench)
Reads a spreadsheet of page URLs and image URLs, decodes image filenames to extract visual context, maps page types from URL slugs, and writes accurate alt text under 125 chars for every image — no live page fetches needed.
Input rows: 312 images
Logos: 48 Icons: 97 Screenshots: 34
"Acme Corp logo – B2B SaaS SEO client page"
"Step 2 of onboarding process – audit" (96 chars ✓)openpyxl (Python)
pandas
Pulls live Ahrefs data for the target and up to 3 competitors, computes the full keyword gap, groups into content clusters, identifies quick wins (KD < 30), and produces a polished 3-section .docx prospect report.
Target: acmecorp.com Competitors: 3
Gap keywords: 86
Quick wins (KD<30): 10
Top opportunity: keyword X — TP 1,300/moAhrefs MCP (keywords-explorer-*, batch-analysis)
docx.js (Node)
Crawls each URL via Firecrawl, generates unique title tags (50–60 chars), meta descriptions (145–155 chars), and H1s matched to commercial intent — with anti-template enforcement to ensure no two pages follow the same pattern.
URL: example.com/solutions/data-governance
Title: Data Governance Solutions for AI Teams (39 ✓)
Meta: Enforce data policies across your AI... (152 ✓)
H1: Data Governance Built for AI-Driven TeamsFirecrawl MCP
openpyxl (Python)
Given a seed topic, pulls up to 150 matching keywords from Ahrefs, groups them into 6–10 thematic clusters, scores each by opportunity (KD + volume + CPC), and produces a multi-sheet .xlsx matching the Epic Slope cluster analysis template.
Seed: AI governance Country: US
Clusters: 8 Keywords: 142
Top Cluster: Tools & Software ⭐⭐⭐ High
Volume: 18,400 Avg KD: 44 Max TP: 4,900Ahrefs MCP (keywords-explorer-*)
openpyxl (Python)
Crawls a URL with Firecrawl, auto-detects the page type across 18 schema types (Article, Product, FAQ, HowTo, LocalBusiness, Event, Recipe, VideoObject and more), then generates accurate JSON-LD blocks with CMS-specific paste instructions and pre-filled Google Rich Results Test links.
URL: example.com/blog/ai-governance-guide
Detected: BlogPosting Rich results: ✅ Eligible
<script type="application/ld+json">
{
"@type": "BlogPosting",
"headline": "AI Governance Guide",
"datePublished": "2026-02-10",
"author": { "name": "Jane Smith" }
}
</script>Firecrawl MCP (firecrawl_scrape)
Runs 3 GSC queries (pages, queries, date trend) and pulls Ahrefs top pages in parallel, then synthesizes wins (pages near top 3, emerging clusters) and urgent findings (high impressions + low CTR, buried money pages) into a prioritized action table.
Domain: example.com
🏆 Win: /blog/post ranks #4 for "keyword X" (Vol 2,400)
🚨 Urgent: /solutions has 14K impressions, 0.08% CTR
🔴 1: Rewrite title tag on /solutionsRube (Google Search Console)
Ahrefs MCP (site-explorer-top-pages)
Fetches live Google SERPs for 2+ keywords, calculates Jaccard similarity at domain level, classifies dominant intent for each, and delivers a clear verdict: ✅ Same Page or ❌ Separate Pages — with reasoning and a summary table.
KW A: ai governance software
KW B: ai governance platform
SERP Overlap: 70% Intent: Both Commercial
✅ Same Page — high overlap, matching intentSERP API (SerpAPI)
Comprehensive audit from Ahrefs Site Audit data. Generates a .docx with Executive Summary and issue sections, plus a .xlsx workbook with one tab per issue and all affected URLs. Supports Screaming Frog pagespeed export for Core Web Vitals data.
Project: example.com
P0 Issues: 3 P1: 7 P2: 12
→ Technical_SEO_Audit___ExampleCom.docx
→ Technical_SEO_Audit___ExampleCom.xlsxAhrefs MCP (site-audit-*)
openpyxl + python-docx
Uses the squirrelscan CLI to crawl and audit any live website across 21 categories and 230+ rules. Returns LLM-optimized reports with health scores, broken links, meta tag analysis, and actionable fix recommendations.
URL: https://example.com
Coverage: surface Pages: 100
Health Score: 78/100
Errors: 12 Warnings: 34 Notices: 67squirrelscan CLI
Bash

