| name | website-intelligence |
| description | Use this skill when the user asks to audit, analyze, score, or inspect a website for SEO, content, performance heuristics, tech stack, AI/GEO visibility, backlinks/search signals, conversion clarity, or competitor site intelligence. Triggers on phrases like audit this website, analyze site, website score, SEO audit, competitor website, site performance, tech stack, content audit, AI visibility, GEO visibility, and website intelligence. Use it to deploy the website analysis team for full audits or choose quick web_fetch/web_fetch_batch checks for narrow questions. |
| emoji | 🧩 |
| version | 1.1.0 |
| triggers | audit website, analyze site, website score, SEO audit, competitor website, site performance, tech stack, content audit, AI visibility, GEO visibility, website intelligence, site analysis, conversion audit, homepage audit |
Website Intelligence
Full playbook for deploying the one-shot 5-agent website analysis team and interpreting results.
Tool
deploy_analysis_team({ url })
- url: Full URL including https:// (e.g.
https://example.com)
- Spawns 5 specialist agents in parallel — runs async, result delivered to main chat via notification event
- Report saved to
workspace/reports/ after completion
- Team self-deletes after delivery
When to Use
Use deploy_analysis_team when the user asks to:
- Audit their website
- Check SEO or search rankings
- Analyze site performance or tech stack
- Understand their AI/GEO visibility
- Research a competitor's website
- Get a site score or full report
For a quick single-question web check (e.g. "does my site have HTTPS?"), use web_fetch directly instead. For several known pages or competitor/source URLs, use web_fetch_batch.
What the 5 Agents Do
| Agent | Checks | Output file |
|---|
| SEO Scanner | Title, meta description, H1/H2, image alt text, canonical, robots meta, SERP presence, top keywords | findings-seo.md |
| Performance & Stack Detective | Framework detection (Next.js, WP, Shopify), JS bundle size, render-blocking, mobile viewport, lazy loading | findings-performance.md |
| GEO (AI Visibility) Agent | Brand presence in AI-generated search results, featured snippets, knowledge panels, AI citation score | findings-geo.md |
| Backlinks & SERP Intelligence | Domain authority signals, top referring domains, competitive ranking gaps | findings-backlinks.md |
| Content Audit Agent | Page copy quality, readability, value proposition clarity, CTA strength, content gaps | findings-content.md |
Workflow
Standard site audit
1. deploy_analysis_team({ url: "https://example.com" })
2. Agents run in parallel (async — takes 2-5 minutes)
3. Result delivered as team_event notification to main chat
4. Report compiled at workspace/reports/[domain]-analysis.md
5. Read report and summarize top 3 priority fixes for the user
Competitive research
1. deploy_analysis_team({ url: "https://competitor.com" })
2. When report arrives, compare against user's own site
3. Identify gaps: keywords they rank for, tech advantages, content angles
4. Output a gap analysis with 5 actionable items
Reading the Report
After the report arrives, structure your response as:
1. Score summary — give each area a rating (SEO: X/10, Performance: X/10, GEO: High/Medium/Low/Invisible, Content: X/10)
2. Top 3 critical issues — the highest-impact problems to fix first
3. Quick wins — things fixable in <1 day that have outsized impact (missing meta description, no alt tags, etc.)
4. Strategic recommendations — 2-3 longer-term moves (tech stack upgrade, content strategy, link building)
Keep it direct. Numbers and specifics over adjectives. If data is missing from a section, note it as "scraping limitation" rather than guessing.
API Limitations
The team uses web_fetch, web_fetch_batch, and web_search — no Lighthouse API or PageSpeed Insights API. This means:
- Performance scores are heuristic, not Lighthouse numbers
- Backlink data is search-signal based, not Ahrefs/Moz level
- GEO visibility is based on web_search sampling, not exhaustive
When doing manual follow-up research after the team report:
- Use
web_search({ query, fetch_top_k: 2-5 }) for compact source discovery plus reading.
- Use
web_fetch_batch for selected competitor pages, review pages, docs, or SERP evidence URLs.
- Use browser tools only for live interaction, JS-rendered content, screenshots, or visual QA.
For production-grade audits, recommend the user also run Google PageSpeed Insights and Ahrefs manually and share results for a combined analysis.
Changelog
| Date | Change |
|---|
| 2026-03-19 | Initial skill created. Tool wired in Block A2. |
